Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 200514112EDITORIALEDITORIALCREATIVITY AND INNOVATION MANAGEMENT
EDITORIAL
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Editorial
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his first issue of 2005 is a special one, with seven contributions on TRIZ, the result of a call for papers on updating the theory of inventive problem solving. Guest-editor Martin Moehrle from the University of Bremen, with his team from the Institute for Project Management and Innovation, has managed this part from the issue from start to finish. Traditionally, TRIZ, as a theory with its concepts, tools and exemplary applications, fits very well in the focal area of creativity and innovation management. After a more detailed introduction to the content of this TRIZ special, we will briefly introduce the two additional contributions in this issue and look forward to publications and events in 2005. The theory of inventive problem solving (Russian abbreviation: TRIZ) was developed to provide access for engineers and natural scientists to the knowledge of former inventors. It consists of a bundle of tools, which can be used either separately or in combination with others. The usage of TRIZ and its tools leads to an improvement of efficiency within the innovation process as well as to more and smarter problem solutions. The known tools of TRIZ were mainly introduced before the mid-1980s. The question arises if and where there have been major further improvements, either in concepts or in adaptation. Taking this into consideration an update of TRIZ is necessary, which will be presented in this Special Issue of Creativity and Innovation Management. After an introduction to TRIZ, written by the guest editor and titled ‘What is TRIZ’, which shall give the readers a common understanding of specific TRIZ terms and insight into the tool-oriented structure of TRIZ, three elements of update aspects will be addressed: (i) conception – conceptual aspects of TRIZ tools, (ii) application – using of TRIZ and its tools in new fields, and (iii) inspiration – TRIZ in connection with bionics.
Conception The first part of updating TRIZ is dedicated to classical TRIZ tools. Both advances within the set of tools and the connection to other classical ideation tools will be discussed. Darrel © Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
Mann reports in a practitioner paper on advances in design and application of the contradiction matrix. This matrix was one of the most important elements of classical TRIZ, but became out of date, since new technologies have emerged and as a result, relationships between inventive principles and standardized contradictions have changed over the last decades. There is a need to change the parameters of the contradiction matrix and also to adjust the sequence of parameters. Furthermore, an actual assignment of inventive principles to now newly defined matrix parameters has to be provided. Jack Hipple shows how to combine TRIZ tools with classical ideation tools like Creative Problem Solving™ by Isaksen or Six Hats™ by deBono. He demonstrates that there is considerable benefit in the common usage of TRIZ and traditional ideation tools.
Application Based on the experiences and successes of TRIZ implementations within the technical world, some authors broaden the scope of applications to new fields. Therefore, a second part of this special issue deals with these aspects. Jun Zhang, Kah-Hin Chai and KayChuan Tan introduce a new method based on TRIZ to bridge gaps in traditional service conceptual design. They point out the differences between the design of physical products and services like customer participation, perish ability, intangibility and heterogeneity. Consistently, they develop a design method considering both the attributes of services as well as the possibilities of TRIZ. In a case study dealing with a university’s canteen operations, the authors demonstrate the use of their new design method. Sandra Mueller has chosen the field of human resources management (HR management) as a field for application of TRIZ. In her paper, she takes up a TRIZ tool called ‘resource analysis’. To discuss resources under HR management aspects, she suggests a three-level approach. The first level is based upon the traditional classification of resources within technical TRIZ, and the other levels are adding resources from the field of strategic
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management. Her argumentation is very convincing, due to the careful application of resource analysis supporting HR managers to come up with unconventional thinking directions and several starting points for creative solutions.
Inspiration The third part of this special issue investigates the links between two fascinating theories: TRIZ on the one hand, and bionics on the other. Both have inspiring elements, and their combination leads to enhanced tools for problem solving. Bernd Hill has developed a nature-oriented innovation strategy, which contains improved elements of TRIZ. In the paper he deals with a strategy for goal setting and problem solving in a bionics-oriented construction process. The application of the proposed procedure may lead to innovative problem solutions by overcoming successfully mental barriers. In detail, he shows that the usage of evolutionary laws and contradictions can open up new opportunities and possibilities for strategic product development. Biological information is an additional and profitable source for engineers and natural scientists using TRIZ. Julian Vincent, Olga Bogatyreva, Anja-Karina Pahl, Nikolay Bogatyrev and Adrian Bowyer turned this source into a ‘biological patents’ database. For gathering and presenting the information they adapt the structure of biological information to suit the established structure of TRIZ. Based on the contradiction matrix, they suggest 5D ‘conflict’ matrices for biological structures and environments. An example illustrates the use of the newly designed database. As one of two additional contributions from our ‘pipeline’, first, Mats Sundgren and his coauthors inform us about the results of their analysis of the data from a large survey in a major pharmaceutical company. The article
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examines how different forms of performance evaluation relate to aspects of the creative climate. In the conclusion, the challenges faced when trying to put ‘dialogue-based’ evaluation are discussed and related to HRM. This is also an interesting preview of the forthcoming June issue, which will contain contributions mostly focused on HRM and innovation. In the article that concludes the issue, Michael Lewis and James Moultry discuss the growing attention on an interesting phenomenon: innovation laboratories. The authors develop a framework which then is used as a basis for analysing the structure, infrastructure, benefits and dis-benefits of three UK-based cases: innovation laboratories in a mass service company, a government department and an academic institution. Two book reviews conclude this issue: Jan Verloop’s Insight in Innovation is a practical and inspirational book with a focus on hands-on experience, and Stefan Thomke’s Experimentation Matters is a must-read for anyone who wants to manage their projects as experiments to unlock potential of new technologies for innovation within and beyond organizational boundaries. Looking beyond this issue, for this year’s volume we already have many interesting and high quality articles and issues in store – among them the already mentioned theme HRM and innovation in Issue 2, and in the third issue, a set of contributions focusing on organizing for innovation and the innovation paradox. Also, we can look forward to some inspiring events relevant to the creativity and innovation management community, starting with the Oxford meeting in March (23–24), and the EACI conference in Lodz in September. See the adverts at the very end of this issue, which include calls for two CI net events, and the up-to-date information on our web site. Enjoy your reading! Martin Moehrle and Olaf Fisscher
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESWHAT IS TRIZ?CREATIVITY AND INNOVATION MANAGEMENT
WHAT IS TRIZ?
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What is TRIZ? From Conceptual Basics to a Framework for Research Martin G. Moehrle This paper introduces six aspects of the theory of inventive problem solving (TRIZ), from conceptual basics to a framework for interdisciplinary research, and explains some of the specific terminology, such as inventive principles, standard solutions, substance-field-systems or contradictions. The conceptual approach of TRIZ comprises the way from a concrete problem over an abstract problem to an abstract solution and from there to a concrete solution. This is supported by a toolkit, which helps the problem-solver analysing and solving problems in different perspectives. The ‘power supply for notebook computers’ example demonstrates a problem-solving process with TRIZ using contradiction thinking, the contradiction matrix and the inventive principles as tools. The TRIZ tools may be combined within a comprehensive process model such as ARIS or WOIS, which are briefly discussed. A framework for further research suggests five fields: the tools and their combination as the core, inspiration by new knowledge domains, adaptation to new fields of usage, psycho- and sociological contingency and integration with other creativity tools. The paper concludes with an overview of cornerstones in the history of TRIZ and suggests some introductory books and informative websites.
TRIZ – Using Knowledge from Former Inventors
T
he theory of inventive problem solving (usually abbreviated TRIZ for the Russian term ‘Teoriya Resheniya Izobreatatelskikh Zadatch’ and pronounced ‘treez’) was developed to support engineers and natural scientists solving inventive problems by using the knowledge of former inventors. For this purpose, TRIZ offers a comprehensive set of tools to analyse and solve problems in different perspectives. It also has established some specific terms like inventive principles, standard solutions, substance-field-systems, or contradictions. TRIZ was introduced in the period 1950s to the 1980s, primarily in the former USSR. This period of time, together with the parts of the theory developed in this period, are sometimes referred to as ‘classical TRIZ’. Since the 1990s, several members of European and American companies have recognized the benefits of TRIZ. One reason for this is the development of a number of TRIZ-based soft© Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
ware applications, such as TechOptimizer (Invention Machine, Inc.) and Innovation WorkBench (Ideation International, Inc.), to provide the problem-solver with more or less easy to handle access to TRIZ tools. A second reason is the publication of reports of successful applications, in journals such as the TRIZ Journal (see the Appendix for website recommendations). This paper addresses the fundamentals of TRIZ, and provides a general survey of the methodology. The key questions to be answered are: • What benefits can TRIZ provide for creativity management? • What are the fundamental ideas of TRIZ? • Which tools are offered for problem solving? • Which comprehensive process models are offered to combine several TRIZ tools? Two examples using combinations of tools demonstrate the use of TRIZ. A research agenda and some historical aspects of TRIZ will close the paper.
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level 3
abstract problem
abstract solution
level 2
abstract problem
abstract solution
level 1
abstract problem
abstract solution
specific problem
specific solution direct way
Figure 1. Problem solving with TRIZ tools at different levels of abstraction Source: Adapted from Pannenbäcker 2001; Terninko, Zusman and Zlotin 1998.
Conceptual Basics of TRIZ The goal of TRIZ, as it is known today, is to support inventors when they have to solve primarily technical or technical-economical problems. The fundamental idea of TRIZ is to provide them with easy access to a wide range of experiences and knowledge of former inventors, and thus use previous solutions for solving new inventive problems (Figure 1). Problem solving within TRIZ can be described using a four-element model:
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• The specific problem can be analysed and transformed into an abstract solution, for example, through the tool function analysis. • The tool contradiction can help to formulate the problem on an abstract level. • Afterwards, the inventor can find abstract solutions for the formulated abstract problem, for instance using the tools inventive principles, separation principles or substance-field-modulation. • Lastly, the problem-solver can use the tool ideal machine for assessing the found solution concepts and transfer the selected solution concept into a specific solution.
1. The problem-solver should analyse his specific problem in detail. This is similar to many other creative problem-solving approaches. 2. He should match his specific problem to an abstract problem. 3. On an abstract level, the problem-solver should search for an abstract solution. 4. If the problem-solver has found an abstract solution, he should transform this solution into a specific solution for his specific problem.
Survey of TRIZ Tools
During this process, TRIZ can support the problem-solver by accumulating innovative experiences and providing access to effective solutions independent of application area. For each step the problem-solver can resort to specific TRIZ tools:
As mentioned above, TRIZ offers a comprehensive toolkit. A framework of five fields can help to structure the tools within this toolkit. The five fields represent all the relevant aspects that have to be addressed while problem analysing and solving:
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As there are different TRIZ tools corresponding with different levels of abstraction (see Figure 1), this process may vary in the heights of abstraction, and also in the number of loops, which the problem-solver is passing through.
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• current state – what does the current state situation look like? • resources – which resources are available? • goals – which goals shall be fulfilled, to what degree? • intended state – how is the future situation supposed to look like? • transformation – in what way can the current state be transformed into the intended state? A survey of the tools is given to help with a better understanding of the published contributions in this Special Issue (Table 1). For all TRIZ tools, the table contains a brief description, supplemented with its main application, the kind of tool and the connections to other tools. For a more detailed view see the recommended literature at the end of this paper. All presented TRIZ tools have been well proven. Some are distinguished by characterized terms like ‘contradiction’. By using the various tools, the problem solver can view a problem from different perspectives, because they work in different ways. Some tools are based on the application of concentrated knowledge. For example, lists and samples may stimulate the inventor to new ideas, and other tools represent special techniques for directing creativity by structuring the process of inventing. Different tools can lead to identical as well as to diverse solutions. Furthermore, some tools correspond to other tools, particularly if they (can) work on the results of another tool, e.g. the tool substance-fieldmodulation on the tool substance-fieldanalysis. Nevertheless, all tools are can be applied in isolation by a problem-solver individual or a team.
Figure 2. Notebook computer with two systems of power supply
leaving with the notebook, and to pack and carry all the components while on journey. Given this situation, TRIZ tools will be applied according to the process defined before. The contradictions tool will be used first to help to analyse the problem. Second – in the form of a contradiction matrix – it will help to transform the concrete problem into an abstract problem. Third, inventive principles will be identified as solutions for the abstract problem, and fourth, they will be specified for the concrete problem.
Contradiction Thinking
Example for Problem Solving with TRIZ Tools Problem solving with TRIZ and its tools will now be demonstrated with an example. In almost all cases, notebook computers have a power supply, which consists of two interconnected systems (Figure 2). One system is the battery pack; the other is powered by the home power-supply system with a mains adaptor and two cables, one reaching from the socket to the mains adaptor, the other reaching from the mains adaptor to the notebook computer. If connected to the home power-supply system, the mains adaptor not only supplies the notebook with energy, but also recharges the batteries. For the user of the notebook it is not very convenient to assemble the mains adaptor when near a socket, dismantle it when
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Contradictions represent the core of a problem, where an invention has to deliver a convincing solution. Contradictions are very characteristic for TRIZ. Uncovering contradictions requires paradox thinking and leads the problem to extremes, whereby the problem becomes evident. But does the term contradiction in TRIZ mean the same as when used in colloquial language? In TRIZ terminology, the word ‘contradiction’ is used more precisely – a contradiction is present when three conditions are fulfilled: (i) there is a desired function in a system (ii) there is a conventional mean to realize this function and (iii) the realization is opposed by harmful factors (Figure 3). To be more precise, the defined type of contradiction will be referred to as a technical
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Table 1. Survey of tools in the field of TRIZ tools in the field of TRIZ procedure main field of application current state
tool
remarkable issues
SP
CK
referring to
function analysis (system analysis)
modeling of positive and negative functions of a system
¥
• substance-field-analysis • resource analysis
object analysis (system analysis)
modeling of objects (represent components or products) of a system
¥
• substance-field-analysis • resource analysis
contradiction
confronting desired functions with harmful factors
¥
• system analysis • ideality
substance-fieldanalysis
modeling of substances and fields of a problem
¥
• system analysis • resource analysis
evolution analysis
analyzing of the previous evolution of a system
¥
• evolution prediction
resource analysis
resource analysis
making aware of all available resources in and around a system
¥
• system analysis • substance-field-analysis
transformation
inventive principles (IP) in independent form
direct applying of abstract inventive principles
¥
• contradiction
IP with contradiction matrix
transferring the desired function and the harmful factor of a problem to the contradiction matrix and applying of recommended abstract inventive principles
¥
• contradiction
separation principles
separating of conflicting system requirements
substance-fieldmodulation
applying of standard operations
¥
• substance-field-analysis
evolution prediction
anticipating of the further development of a system
¥
• evolution analysis
resource variation
applying of the available resources
scientific effects and phenomena
making use of scientific effects and phenomena of different disciplines
idealty
radical asking for the best possible solution
¥
• contradiction • evolution prediction
fitting
considering of restricting basic conditions
¥
• contradiction
strong solution
balancing between ideality and fitting
¥
• ideality • fitting
goals
intended state
• contradiction
¥
• resource analysis
¥ ¥
• system analysis
Source: Pannenbäcker 2001. Notes: In the column ‘procedure’ SP stands for ‘special technique’ and CK for ‘concentrated knowledge’.
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A function is desired, ...
J ... but the conventional realization is opposed by harmful factors.
Figure 3. Definition of a technical contradiction
(or two-parametrical) contradiction, as opposed to a physical (or one-parametrical) contradiction. It is useful to apply contradiction thinking in order to analyse the example. (i)
What function is desired? Perhaps the user wants to get rid of the inconvenience. He wants to take the notebook away on a journey, but not the mains adaptor and the different cables. (ii) Is there a conventional mean to achieve this function? The answer is yes, the user can just leave the mains adaptor and the different cables at home. (iii) The user will be able to work with the notebook computer as long as there is energy in the battery – then the operating system will run the notebook computer down. Therefore, if the user wants to work for a longer period of time, a harmful condition occurs, which opposes the conventional mean for solving the problem.
Contradiction Matrix Using contradiction thinking, the concrete problem can be analysed. Afterwards, it is useful to transform the concrete problem into an abstract problem. For this purpose, the contradiction matrix can be applied. The contradiction matrix consists of 39 rows and 39 columns. Rows and columns represent parameters of technical systems, which can be changed in different directions, such as volume, mass, energy supply or user friendliness of a system. The rows of the matrix contain desired functions of a system, and the columns contain harmful factors of a system. To use the contradiction matrix, the problemsolver has to proceed in three steps. First, the desired function is transformed into one (or more) of the parameters of the contradiction matrix’ rows. Second, the harmful factor is transformed into one (or more) parameters of
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the contradiction matrix’ columns. After the second step, the conrete problem has been assigned to one of 39 ¥ 39 - 39 = 1.482 abstract problems. Third, the problem-solver will find in the cross-field, defined by the selected row and the selected column of the contradiction matrix, up to four of forty inventive principles (see below), which are recommended for overcoming the technical contradiction. The three steps will now be applied to the power-supply example (Figure 4). The desired function may be related to parameter ‘waste of energy’ (which should be reduced), row 22 of the contradiction matrix. The harmful factor may be transformed to ‘weight of moving object’ (which rises, if the desired function is achieved by a conventional mean), column 1 of the contradiction matrix. In the cross-field, four inventive principles are recommended (see below for an explanation).
Inventive Principles One important result of Altshuller’s comprehensive patent analysis is the so-called inventive principles. He found that a large number of inventions were based on a small number of principles, which the inventors used intuitively. In addition, he developed the contradiction matrix mentioned above, which allows the connection of abstract problems with abstract solutions – in this case the inventive principles (Table 2). The inventive principles offer a different degree of abstraction, and various of them are divided into two to five sub-principles. The problem-solver can apply them by looking over them and using his intuition for the best fitting principle, or he can use the contradiction matrix to lead him to inventive principles, which had been applied in similar abstract problems. In the example, inventive principles 15, 6, 19 and 28 are proposed by the contradiction matrix (Table 3).
Solution Concepts Having identified the inventive principles as abstract solutions of the abstract problem, it is now necessary to find concrete solution concepts. The problem-solver therefore applies the inventive principles. At this point it should be noted that TRIZ does not replace an inventor’s natural creativity, but leads it in some predefined direction. This will be demonstrated with the help of two inventive principles. For instance, inventive principle 6, ‘universality’, could be applied: ‘Have the object perform multiple functions, thereby eliminating the need for some other object(s)’. To apply the
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6, 2, 34, 19
.. . 22. Waste of energy
15, 6, 19, 28
39. Productivity
38. Level of automation
22. Waste of energy
1. Weight of moving object 1. Weight of moving object
. ..
. ..
USEFUL FUNCTION / PARAMETER I
HARMFUL FACT OR / PARAMETER II
26, 35, 18, 19
35, 3, 24, 37
2
28, 10, 29, 35
.. . 38. Level of automation 39. Productivity
28, 26, 18, 35 35, 26, 24, 37
5, 12, 35, 26
23, 28 28, 10, 29, 35
5, 12, 35, 26
Figure 4. Selecting inventive principles by applying the contradiction matrix Table 2. Forty inventive principles 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
Segmentation Extraction Local conditions Asymmetry Consolidation Universality Nesting Anti-weight Prior counteraction Prior action Cushion in advance Equipotentiality Inversion Spheroidality Dynamicity Partial or excessive action Shift to a new dimension Mechanical vibration Periodic action Continuity of useful action
21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40.
Rushing through Convert harm into benefit Feedback Mediator Self-service Copying Disposable object Replacement of a mechanical system Pneumatic or hydraulic construction Flexible ‘shells’ or thin films Porous material Change the color Homogeneity Rejecting or regenerating parts Transforming the physical/chemical state Phase transition Thermal expansion Strengthen oxidation Inert environment Composite materials
Source: Ideation International, 1999
principle, the problem-solver could use the inventive principle like an equation with some variables. What could the ‘object’ mentioned in the inventive principle be? The problem-solver considers the situation of a human using a notebook computer with several elements: • the notebook computer itself and its parts – display, keyboard, motherboard, expansion units, network connectors, etc;
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• around the system: the table, the chair, illumination, panel lighting, pens, electric sockets etc.; • last, but not least, the person using the notebook computer, who, in the sense of the inventive principle, is also considered as an object. All these objects have functions. Now the problem-solver should think about new func-
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Table 3. Four selected inventive principles with descriptions/sub principles Principle
Description/Sub principles
Dynamicity (inventive principle no. 15)
a. Make an object or its environment automatically adjust for optimal performance at each stage of operation b. Divide an object into elements which can change position relative to each other c. If an object is immovable, make it movable or interchangeable Have the object perform multiple functions, thereby eliminating the need for some other object(s) a. Replace a continuous action with a periodic (pulsed) one b. If an action is already periodic, change its frequency c. Use pulsed between impulses to provide additional action a. Replace a mechanical system by an optical, acoustical or olfactory (odor) system b. Use an electrical, magnetic or electromagnetic field for interaction with the object c. Replace fields 1. Stationary fields with moving fields 2. Fixed fields with those which change in time 3. Random fields with structured fields d. Use a field in conjunction with ferromagnetic particles
Universality (inventive principle no. 6)
Periodic action (inventive principle no. 19)
Replacement of a mechanical system (inventive principle no. 28) Source: Ideation International, 1999.
tions for those objects, especially the function of supplying energy. In a number of seminars with managers from a large power-supplying company some ideas arose: • new function for the table: it may provide low voltage access; • new function for the display and the illumination: there could be photovoltaic elements on the backside of the display and focused lighting in the rooms; • new function for the keyboard: with piezo elements the keystrokes could be used for supplying power (to a very limited extent); • new function for network connectors: with additional cores they may be used for lowvoltage power supply; • new function for the room: under supposition of standardized battery packs there may be battery-pack exchanger units (put a battery pack in, add some money, get a charged battery pack);
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• new function for the human and the expansion slot: with a small generator, which fits into the expansion slot, and a crank handle the user could supply the battery pack with energy. After having applied inventive principle of universality, the problem-solver may like to apply another inventive principle and lead his creativity to a new direction. For example, inventive principle 28, ‘replacement of a mechanical system’, could be applied next (see Table 3 above for sub-principles). Again, the problem-solver could use the inventive principle like an equation with variables. The problem-solver should now consider all mechanical or electrical systems. Some ideas: • replacement of cable connection with induction: there could be induction loops in the table or in specific mats, which send
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electrical energy to an induction loop in the notebook computer (sub-principle b); • replacement of the battery pack with a fuel cell (sub-principle using a wide interpretation); • replacement of the electrical system with an optical system: there could be photovoltaic elements on the backside of the display and focused lighting or even laser beams in the rooms (sub-principle a). Comparing the ideas that have been found by applying inventive principle universality with those that have been found by using inventive principle mechanical systems, two findings come out: • In some cases, the same ideas come up when applying different inventive principles. This shows that there are different roads leading to Rome. • The inventive principles led the problemsolver into specific directions, but the concrete solution concepts had to be found by combining inventive principle with knowledge and human creativity. This is not only that case in this example: TRIZ helps humans to be creative, but it does not replace human creativity (like one could think when reading advertisements of some companies like ‘invention machine’ – nomen est omen).
Comprehensive Process Models: ARIS, WOIS, I-TRIZ and PI A number of comprehensive process models have been introduced to provide a more specific way through the TRIZ tools; for example, ARIS, WOIS, I-TRIZ and PI.
ARIS Altshuller propagated an ‘Algorithm for Inventive Problem Solving’ (1973, 1984; Ideation International, 1999) according to a determined sequence of mathematical operations. ARIS was improved a number of times between 1961 and 1985 (ARIS-61, ARIS-77, ARIS-85). The recent version comprises nine sections with 40 steps, which the problemsolver has to process sequentially. This makes an entire application of ARIS very ambitious. According to Terninko, Zusman and Zlotin (1998), only 5 per cent of all problem-solvers apply ARIS.
WOIS The ‘Inventive Product Development Strategy Focusing On Contradictions’ (abbreviation
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derived from the German title: Widerspruchsorientierte Innovationsstrategie) was generated by Linde and Hill (1993). It is geared to the metaphor of the evolution spiral of a technical system. The problem-solving process using WOIS is divided into three phases: (i) orientation; (ii) evolution barrier; and (iii) solution finding, in which various TRIZ-based tools can be used. In contrast to ARIS, iterations and parallel operations are explicitly permitted. Furthermore, WOIS uses elements of design and bionics and can be combined with other methods, such as QFD.
I-TRIZ The software Innovation WorkBench, by Ideation International (1995) is based on a methodology that is in many, but not all aspects similar to ARIS. Characteristic sections are the Innovation Situation Questionnaire to define a problem and the Problem Formulator to develop a function model of the problem. Although the sections are arranged sequentially in the software, the problem-solver can decide whether or not all steps should be processed.
PI Moehrle and Pannenbaecker (1997) have proposed a ‘Concept of Problem-Driven Inventing’ (abbreviation: PI). In the framework of the Five-Field-Analysis, all TRIZ tools are integrated. This concept suggests a flexible approach to the process of inventive problemsolving. It leaves the decisions – which tools should be processed in which sequence – to the problem-solvers’ experience and intuition, with only a weak suggestion to start from the outside fields. PI is designed to be open in the same way as WOIS; therefore other tools may supplement it. Although the comprehensive process models have been known since a long time, other combinations of TRIZ tools are not unusually in companies. In an empirical study, Moehrle (2003) reports three cluster types of tool configuration: (i) basic TRIZ, (ii) resource and ideality-based TRIZ; and (iii) substance-field based TRIZ.
Framework for Interdisciplinary Research Although TRIZ has been discussed in several books and articles, there is still a lot of research to be carried out. To structure these research aspects, a framework for interdisciplinary research is suggested in Figure 5.
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Core Inspiration
Adaptation
Psycho- and sociological contingency
Integration
Figure 5. Framework for interdisciplinary TRIZ research
The framework has been modelled with the help of the entity-relationship-approach as a mean of structuring knowledge networks (see Chen, 1976). It will be presented in five aspects: • Core – the various TRIZ tools are located in the centre of the framework. How effective and efficient are those tools? What combinations of them are particularly helpful? What theoretical background can be found to enhance the toolkit? What comprehensive process models (see above) should be used under what conditions? • Inspiration – some aspects may inspire the development of TRIZ. New scientific knowledge domains, and new patented inventions may be assigned to this field of research. What findings, for example, from the field of bionics can be integrated into TRIZ and its tools? Which new inventive principles can be found in patents covering newer technologies like photovoltaics or nanotechnology? • Adaptation – TRIZ may be applied in different fields. In a narrow sense, one can think of different technical fields, reaching from classical engineering to software and gentechnology. In a broader sense, other fields of application like management, sociological or even psychological problems can be
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considered (bearing in mind that people are not technical systems). • Psychological and sociological contingency – the application of TRIZ and its tools is embedded in psychological and sociological factors. Which personnel types prefer which TRIZ tools? How is this related to the qualification of the people? How should group members be assigned to reach acceptable results with TRIZ tools? • Integration – several creativity techniques, such as lateral thinking, morphology, CPS and synectics have been developed. Furthermore, a lot of quality tools such ase FMEA or QFD are being discussed. How could these techniques and tools be combined with TRIZ tools? This framework shows a number of basic research questions. It may be stretched by combinations of research areas and questions. Some examples for this are marked in Figure 5 (above), with dotted lines. Furthermore, interdisciplinary research is needed, as there are technical, biological, chemical, physical, managerial, psychological and sociological aspects, which have to be combined. Having the framework for research in mind, it is easy to assign the six TRIZ papers of this special issue. Mann can be assigned to the core field, Hipple to the field of psychological
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and sociological contingency, Hill and Vincent et al. belong to the field inspiration, Zhang et al. and Mueller to the field of adaptation.
History of TRIZ TRIZ has an interesting history (see Terninko, Zusman & Zlotin, 1998). After World War II Genrich Saulowitsch Altshuller (1926–1998), at that time a young and talented inventor in the former Soviet Union and a patent examiner for the Russian navy, started to develop TRIZ as a methodology to create systematic innovations. His research began with comparing patents to analyse how inventors invent. Because of his criticism of the Soviet system, Stalin banished Altshuller to Siberia, where he spent a number years in a labour camp. During this time, he worked intensively on the patent analysis and discovered cohesions between inventions. For instance, Altshuller extracted the 40 inventive principles, which were mentioned above and on which many technical solutions are based on. Furthermore, in the labour camp he met like-minded researchers who were interested in his research, and with them he discussed his ideas. In the following years, Altshuller developed successively TRIZ tools and characteristic terms. Later, as a consequence of glasnost, several of Altshuller’s students emmigrated to the USA, Scandinavia, Israel and Germany, so that TRIZ has been introduced in the Western hemisphere. Some of the TRIZ experts have worked on the development of software applications. Since this time TRIZ has spread out to several countries all over the world.
Conclusions Based on the thorough analysis of former inventions, TRIZ enriches the theory of creativity in multiple ways. It serves with many interesting tools and comprehensive process models, both having several connections to traditional creative methods. Furthermore, the basic idea of TRIZ – using knowledge from former inventions – gives starting points for similar research in other fields: what principles and abstract contradictions may be found in biology, in management, in psychology? In addition, there is still a lot of research work to do, as it is specified in the framework for research mentioned above. TRIZ will not only influence science and research, but also applications in companies. R&D Managers and service designers have the opportunity to organize creativity workshops with TRIZ tools, stimulating the creativity of
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individual problem-solvers as well as problem-solving groups. For successful implementation, the recommendation of the author is to search for an interesting inventive task, work on it with an interdisciplinary team and deploy experienced consultants directly in the problem-solving process.
Appendix – Recommendations for Resources Recommended Reading There are nine recommendations for introductory books about TRIZ; six in English, and three in German. Altshuller’s Creativity as an Exact Science (1984) is an original book by the father of TRIZ. It gives insight into the goals that Altshuller expected to reach with TRIZ, and many tools. As the book was published in 1984 and there are newer, more tool-oriented introductions, this book is not recommended for the first reading about TRIZ, but for the experienced TRIZ user. As mentioned above, Terninko, Zusman and Zlotin provide a tool-oriented introduction to TRIZ together with improvements to the tools in Systematic Innovation: An Introduction to TRIZ (1998). A similar introduction, but with some newer tools like the nine fields analysis is offered by Mann, Hands-on Systematic Innovation (2002). In Inventive Thinking Through TRIZ: A Practical Introduction, Orloff (2003) links classical TRIZ with newer developments made in Russia. Many creative aspects may be found in his book. Savransky, in Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving (2000), gives an introduction with some case studies. A simplified introduction with only few tools has been written by Rantanen and Domb, Simplified TRIZ: New Problem Solving Applications for Engineers & Manufacturing Professionals (2002). In addition to the above books in English, three books in the German language are recommended. A very solid book has been written by Zobel, Systematisches Erfinden (2004), which combines theory with humor in a very pleasant way. In Methodisches Erfinden in Unternehmen: Bedarf, Konzept, Perspektiven für TRIZ-basierte Erfolge, Pannenbaecker (2001) shows how TRIZ tools are assigned within the concept of problem-driven invention and adds empirical findings about problem-solving processes in industry. Linde and Hill, in Erfolgreich erfinden. Widerspruchsorientierte Innovationsstrategie für Entwickler und Konstrukteure (1993), suggest TRIZ tools arranged
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WHAT IS TRIZ?
within the ‘Inventive Product Development Strategy Focusing On Contradictions’.
Recommended Websites There are two types of interesting website for TRIZ; one giving access to discussions of TRIZ topics, the other providing information about software tools for TRIZ. For the first type, five websites are recommended: • http://www.aitriz.org (Altshuller Institute for TRIZ Studies) • http://www.altshuller.ru/world/eng (source for all articles published by Altshuller) • http://www.triz-journal.com (international, moderated, but unreferreed web journal) • http://www.triz-online.de (international, moderated, but unreferreed web journal in German) • http://www.trizexperts.net For the second type, three websites are recommended, which are all provided by producers of TRIZ-related software: • http://www.creax.com • http://www.ideationtriz.com • http://www.invention-machine.com
References Altshuller, G.S. (1973) Erfinden – (k)ein Problem? Anleitung für Neuerer und Erfinder. Tribüne, Berlin. Altshuller, G.S. (1984) Creativity as an Exact Science. Gordon & Breach Science Publishers, New York. Altshuller, G.S. (1996) And Suddenly the Inventor Appeared. Technical Innovation Center, Worcester, MA. Chen, P.P. (1976) The Entity-Relationship Model – Towards a Unified View of Data, in: ACM Transactions on Database Systems, 1(1), 9–36. Ideation International (1999) Innovation WorkBench, Version 2.6. Southfield, MI. Linde, H. and Hill, B. (1993) Erfolgreich erfinden. Widerspruchsorientierte Innovationsstrategie für Entwickler und Konstrukteure. Hoppenstedt Technik, Darmstadt. Mann, D. (2002) Hands-on Systematic Innovation. CREAX, Ieper/Belgium.
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Mann, D. (2001) TRIZ: The Theory of Inventive Problem Solving. Creativity and Innovation Management, 10(2), 123–25. Moehrle, M.G. (2003) Implementation of TRIZ Tools in Companies: Results of a Cluster Analysis. In: Butler, J. (ed.), Implementing the Theories of R&D Management – Advancing the State of the Art. Manchester Business, Manchester, 1–8. Moehrle, M.G. and Pannenbaecker, T. (1997) Problem-Driven Inventing: a Concept for Strong Solutions to Inventive Tasks. Creativity and Innovation Management, 6(4). Orloff, M. (2003) Inventive Thinking Through TRIZ: A Practical Introduction. Springer, Berlin. Pannenbaecker, T. (2001) Methodisches Erfinden in Unternehmen: Bedarf, Konzept, Perspektiven für TRIZ-basierte Erfolge. Gabler, Wiesbaden. Phan, D. (1995) TRIZ: Inventive Creativity Based on the Laws of Systems Development. Creativity and Innovation Management, 4(4), 19–30. Rantanen, K. and Domb, E. (2002) Simplified TRIZ: New Problem Solving Applications for Engineers & Manufacturing Professionals. CRC, Boca Raton. Savransky, S.D. (2000) Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving. CRC, Boca Raton. Terninko, Z., Zusman, A. and Zlotin, B. (1998) Systematic Innovation: An Introduction to TRIZ. St. Lucie Press, Boca Raton. Zobel, D. (2004) Systematisches Erfinden. Expert, Berlin.
Prof. Dr. Martin G. Moehrle, born in 1962, is Director of the Institute for Project Management and Innovation, University of Bremen, Germany. He has worked on the TRIZ topic for many years and is leading several research projects on the bases of TRIZ, its application in early phases of the problemsolving process and the transfer to management problems. He has also taught TRIZ in a number of companies. From 2004 onwards, he is speaker of the German TIM committee, which consists of more than 100 academics working in the field of technology and innovation management (TIM). Among his recent publications is a book about technology roadmapping (published by Springer) and an encyclopaedia about technology management (published by Gabler). E-mail:
[email protected]
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESCONTRADICTION ELIMINATION TOOLSCREATIVITY AND INNOVATION MANAGEMENT
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New and Emerging Contradiction Elimination Tools Darrell Mann The paper discusses a systematic programme of patent and science-based research that has culminated in the production of a range of new matrices for the TRIZ toolkit, in particular, the creation of new generic matrices aimed at technical, business and software applications. The new technical matrix tool updates both the form and content of the original matrix, expanding the list of parameters it contains, increasing the inventive principle recommendations for each contradiction, and also making it easier for users to connect their specific problem to the generic framework. The paper also discusses the creation of a number of company and industry-specific matrix tools based on the mass of research data collected, and discusses the likely future evolution of the contradiction elimination toolkit.
Introduction
T
he classical TRIZ contradiction matrix (Altshuller, 1999) presents users with a conceptually simple means of tapping in to the successful contradiction-eliminating strategies of the world’s most successful problemsolvers, presenting a simple 39 ¥ 39 array of parameters relevant to technical problem situations. To use the matrix, problem-solvers are required only to identify and match pairs of things that they wish to improve and things that get worse or prevent them from making the desired improvement onto one or more of the 39 parameters. Then, at the intersection of the improving and worsening parameters in the matrix, the user is able to identify the numbers of the inventive principles most commonly used to successfully challenge that particular conflict pair. Despite being conceptually simple, however, the classical matrix is in need of some attention in order to make it effective for modern-day problem-solvers. The paper describes some of the output of a programme of systematic research to update and refine the matrix. An earlier paper (Mann and Dewulf, 2003a) the form and content of that research programme, while a complete book (Mann et al., 2003b) offers details of the full output of the research and in particular presents the full new technical matrix in all of its detail.
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The aim of this paper is to describe some of the underlying philosophy behind the structuring of the new technical matrix, and the equivalent matrices designed for software and business applications. It is also to describe how the tool fits into a longer-term strategy headed in the direction of the ‘ideal contradiction matrix’. The paper is in four main sections. The first section examines the new technical matrix from the perspective of the changes in its design relative to the original. The second and third parts then discuss the business and software matrices respectively. The final section of the paper then goes on to describe the expected evolution of the matrix as the concept evolves towards its ideal final result.
New Technical Matrix The history of the method of construction and population of the classical TRIZ contradiction matrix is largely shrouded in mythology. Essentially, the original TRIZ researchers abandoned the tool during the early 1970s, focusing their attention instead on other parts of the toolkit (Ideation International Inc., 1999). The basic concept of the tool remains attractive to newcomers to TRIZ, however. This interest unfortunately often turns into frustration when the matrix fails to deliver adequate rec© Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
CONTRADICTION ELIMINATION TOOLS
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Figure 1. Matrix Explorer Software Tool Screen Shot
ommendations. As a consequence, an extensive programme of research to update the tool was instigated. Starting in the year 2000 and completing in mid-2003, over 150,000 additional successful contradiction-breaking solutions were analysed and codified. The results of the research were recorded in a specifically constructed matrix explorer tool. The matrix explorer was primarily developed as an internal facilitation tool, but it now seems that there is a value in making uninterpreted, raw TRIZ data available to users. Specifically, there has been a desire to relate a given pair of conflict parameters not just to a series of inventive principle solution suggestions, but also to the specific patents that feature that particular conflict pair. Figure 1 illustrates the basic configuration of the Matrix Explorer. It has been written in the Java language, and as such is intended to be usable in an online form. The basic structure contains a number of hyperlinks that first enable a user to click onto a particular box within the matrix to determine what principles have been used to solve that conflict. This is the view shown in Figure 1 – where we see the ‘extent of automation’ versus ‘device complexity’ conflict pair being opened to illustrate the fact that in addition to finding examples of patents using the
© Blackwell Publishing Ltd, 2005
inventive principles suggested by the classical matrix (10, 15 and 24), there are a variety of other patents that have successfully challenged this conflict pair using other principles. Figure 2 illustrates the consequence of hyperlinking from this ‘other’ folder. It may be observed that a new screen opens up. This screen contains further hyperlinks to the specific patents that involve the conflict pair under consideration. Not shown on the screen due to lack of space, the screen containing the hyperlinks to the patent database, also describes the inventive principles used by each of the patents listed. The screen-shots shown in these two figures illustrate the form of the matrix when using the 39 parameters as described in classical TRIZ. The next section describes the alternative structuring of these and other new parameters in the new matrix structure.
New Matrix Parameters The principle guiding features we used when determining the form of any new matrix structure were (a) to include parameters that were not adequately addressed in the original matrix (specifically those parameters were not considered to be important when that
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Figure 2. Matrix Explorer Software Tool – Hyperlinks to Patent Database
matrix was devised), and (b) to re-order the parameters into a more logical and informative sequence. With regard to the first issue, it is evident from the original matrix – and specifically the ‘amount of information’ parameter (for which the matrix contains many blank entries) – that the growth of matrix is a story of a gradually unfolding world of innovation in which new parameters become important in the design process. Issues like safety, noise and environmental factors, for example, are considered to be much more important today than they were during the 1970s. With regard to the second issue, we have tried to re-sequence the matrix parameters in line with a general progression and shift of focus as systems evolve from their conception and infancy through to maturity and retirement (see Figure 3). The full list of parameters in the new matrix and some of the detailed definition underlying how we placed different solutions into the different classifications is illustrated in Figure 4 below. The new categories are: • physical parameters • performance-related parameters
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• efficiency-related parameters • ‘ility (reliability, durability, etc) related parameters • manufacture/cost-reduction parameters • measurement parameters (special category) The parameters shown in italics are the ones that were introduced into the new structure relative to the original matrix. The new parameters give an indication of how the focus of engineers and problem-solvers has broadened since the original work of the Soviet TRIZ researchers. New parameters were only included provided that a statistically significant quantity of solutions could be found to permit the recommendation of a valid array of inventive principle suggestions. The new ‘Matrix 2003’ was first published in June 2003. More recent work (Mann, 2004a, 2004b) has sought to compare the new and original matrices against solutions from patents granted after the publication of the book. The overall findings of this work suggest that the likelihood that the new matrix will feature the inventive principle recommendations of the inventors of the sample successful patents is over 95 per cent compared to a figure of less than 30 per cent obtained using the original matrix.
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Physical Concept/ Birth
Performance Efficiency Growth
‘ility/Cost Maturity/ Retirement
Figure 3. Re-Sequencing of Matrix Parameters 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24.
25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48.
Weight of moving object Weight of stationary object Length of moving object Length of stationary object Area of moving object Area of stationary object Volume of moving object Volume of stationary object Shape Amount of Substance Amount of Information Duration of action - moving object Duration of action - stationary object Speed Force/Torque Use of energy by moving object Use of energy by stationary object Power Stress/Pressure Strength Stability Temperature Illumination Intensity Function Efficiency
Loss of Substance Loss of Time Loss of Energy Loss of Information Noise Harmful Emissions Object Generated Side Effects Adaptability/Versatility Compatibility/Connectability Ease of Operation Reliability Repairability Security Safety/Vulnerabilty Aesthetics Object affected harmful effects Manufacturability Accuracy of manufacturing Automation Productivity System Complexity Control Complexity Ability to Detect/Measure Measurement Precision
Figure 4. Revised List of Technical Matrix Parameters
Contradiction Matrix for Business Problems Interest in the concept of resolving conflicts in no-compromise ways has proved to be equally high to those working in non-technical fields as those working to resolve technical problems
© Blackwell Publishing Ltd, 2005
(Stalk, Pecault & Burnett, 2000). Work to create a business version of the technical matrix consequently began in the late 1990s. The underlying philosophy and method of constructing a business specific tool was first discussed in a paper at the 2002 Altshuller Institute TRIZ Conference (Mann, 2002). Essentially, the
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1. 2. 3. 4. 5.
R&D Spec/Capability/Means R&D Cost R&D Time R&D Risk R&D Interfaces
6. Production Spec/Capability/Means 7. Production Cost 8. Production Time 9. Production Risk 10. Production Interfaces 11. Supply Spec/CapabilityMeans 12. Supply Cost 13. Supply Time 14. Supply Risk 15. Supply Interface
16. Product Reliability 17. Support Cost 18. Support Time 19. Support Risk 20. Support Interfaces 21. Customer Revenue/Demand/Feedback 22. Amount of Information 23. Communication Flow 24. System affected harmful effects 25. System generated side effects 26. Convenience 27. Adaptability/Versatility 28. System Complexity 29. Control Complexity 30. Tension/Stress 31. Stability
Figure 5. List of Parameters Found In Business Matrix
strategy adopted was one that involved locating successful applications of win-win in the business context and distilling from such cases what the conflicting parameters were, and what inventive strategies were utilized to resolve those conflicts. The number of available case studies has proved to be considerably lower than that found for technical problems, but nevertheless, by the end of 2002 the findings had stabilized sufficiently to release a first version of a business-conflict resolution Matrix (CREAX, 2002). An updated version was subsequently published in June 2004 (Mann, 2004c) following the acquisition of several hundred more case studies and, more importantly, findings from real-life case studies in which an expert panel systematically sought solutions from all 40 of the currently known inventive principles and then identified those that gave the strongest solutions. One stable aspect of the business version of the matrix has been the form and content of the 31 parameters that make up its axes. These 31 parameters are reproduced in Figure 5. They are intended to describe all of the issues relevant to managers and business leaders when they are facing conflict resolution or trade-off elimination situations. Conceptually, the new business matrix is identical to that found in the technical version; to use it the user has to identify pairs of parameters in conflict with one another, to map these specific parameters onto the closest possible match(es) in the matrix and then extract the inventive principles suggested as most appropriate, based on what other problem-solvers with similar conflicts and tradeoffs have used. Like the technical version too, the new matrix has been designed to act as a framework into which new case studies can be
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fitted. In this way, it offers the ability to permit distillation of knowledge from across all sectors of business, and thus to accelerate the transfer of good ideas from one sector to another. One of the key ideas within TRIZ is that ‘someone, somewhere has already solved your problem’; the matrix presents a means through which the ‘someone’ can be found.
Contradiction Matrix for Software Problems A lack of relevance of many of the parameters used to make up the axes of either the classical or new matrix to software problems (weight, length, area, shape, volume, temperature for example) has prompted many users to ask us for a bespoke matrix specifically for software problems. In researching the possibilities for such a matrix, researchers have had to conduct an analysis of both patents and (outside the USA – where patents on software are not permitted) examples from journals and trade literature to establish whether the concept of conflict elimination was being practiced at all. Very soon into this research in fact, although in the case of patents the level of invention (as per Altshuller’s (1979) codification system) is generally very low, it became clear that all 40 inventive principles are being used to challenge conflicts, and that there were certain emerging patterns of usage that meant construction of a new matrix was going to be possible. The current public form of the new – currently 22 ¥ 22 matrix – is currently being Beta tested by a number of industry-based lead users. It is expected that the final version will be published in book form during the fourth quarter of 2004 (Mann, forthcoming).
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The Ideal Contradiction Matrix . . . The 2003 publication of the updated version of the technical contradiction matrix, and the recent publication of the new business matrix, and the ongoing creation of the above outlined matrix specifically aimed at software applications, have prompted a number of questions about the longer-term matrix strategy. What is in fact being experienced with this apparent proliferation of matrices is one of the fundamental trends described within TRIZ – that of increasing complexity followed by decreasing complexity (Salamatov, 1999). Or rather increasing number of components followed by decreasing number of components – see (Mann, 2003c) for more details on why the difference between these two is important. The increasing number of matrices, then, is simply a system in the first half of the trend – see Figure 6. Why should this characteristic be expected to be relevant to the evolution of the contradiction matrix? There are several answers to this question. The first relates to the needs and desires of users of the matrix: for a long time, the classical contradiction matrix has been viewed as a ‘good enough’ or ‘sufficient’ tool (albeit, as hinted at in the Introduction, some TRIZ researchers have since walked away from the concept completely). But then when the business community began to become interested in TRIZ, it very quickly became apparent that the conceptual elegance of the matrix was not matched by its relevance to typical business situations. This phenomenon was the main spur for researchers to create the business matrix. Subsequently, similar relevance problems have been seen in the software-development sector; here too, people have been attracted to the conceptual elegance of the concept, and then disappointed when
Number of Matrices
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they find it difficult to relate their particular problems to the generic parameters contained in the matrix. As a consequence, researchers have also been forced to construct the above outlined matrix tailored specifically to the needs of the software sector. While not being ‘new’ in the same terms, we are also expecting the Matrix concept to expand further when other sectors (and in some instances, individual companies) seek to produce a bespoke matrix for a particular field. In the majority of cases, these bespoke matrix tools will be subtle variants on the technical, business and software matrix tools with parameters reframed in the terminology and jargon of a particular organization or industry. In other cases, we will simply be deleting lines from a matrix in order to remove parameters that are considered irrelevant to a given type of situation (the legal sector, for example, has generally speaking very little interest in R&D, at least not in those words). It is important to recognize, of course, with any of these specialized matrices, that we are not trying to filter out the ability of TRIZ to transfer ideas from one sector to another, but merely to make it easier for users to translate their specific problem into the generic problem – Figure 7. Beyond that, it is the job of the Matrix to identify the best generic solutions from across all fields that may be used to help solve the specific problem at hand. So then, what about the second half of the component-count trend curve? What about the ideal contradiction matrix? According to the TRIZ definition (Domb, 1997), the ideal Matrix is the one that delivers the desired benefit without any of the downside. In other words, it presents the users with the best generic solutions, without the matrix actually having to exist at all. At least, it should not exist as far as the user is concerned. In effect, the ideal matrix would offer the shortcut illustrated in Figure 8. We are beginning to see the emergence of this ideal matrix in the CREAX ‘Contradiction Finder’ tool. You will find a free version of this A SITUATION LIKE MINE
WORLD’S BEST IDEAS IN THIS SITUATION
MY SPECIFIC SITUATION
MY SPECIFIC SOLUTION
Multiple Matrix Formats Simplify This Transition
Figure 6. Number of Components Trend in Relation To TRIZ Contradiction Tools
© Blackwell Publishing Ltd, 2005
Figure 7. Multiple Matrices Help Make the Transition from Specific to Generic Problem
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WORLD’S BEST IDEAS IN THIS SITUATION
MY SPECIFIC SITUATION
MY SPECIFIC SOLUTION
Figure 8. The Ideal Matrix . . . is no Matrix
Figure 9. Contradiction Finder Software
tool on the CREAX website, as well as inside the latest versions of the CREAX Innovation Suite software – Figure 9. The basic idea behind the contradiction finder is that eventually users of any background will simply be able to enter the description of a problem in their own language and jargon. An appropriate algorithm will then analyse this input and provide the most appropriate generic solutions contained in the TRIZ toolkit – whether they be inventive principles, inventive standards, trends of evolution or knowledge/effects. So why not just go straight to this ideal final result you might be asking? The answer lies in the fundamental phenomena underlying the increasing-decreasing complexity trend. It is simply that without working having worked out the ‘right’ routes from specific problem to right generic solution, it is not possible to eliminate the matrix. Put another way, it is only by acquiring the data to populate the various different matrices that we will acquire sufficient
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data to ensure we are making effective recommendations when a user types in their problem. The proliferation, to put it yet another way, is an essential requirement along the road to a more ideal system. At this point in time, it is not clear how long it will take to get to that ‘ideal final result’ end point. All we can say with any degree of certainty is that the journey from here to there will require as much, if not more research effort, as has been devoted to the updating of the original 1973 version of the tool.
References Altshuller, G. (1979) Creativity As An Exact Science. Gordon & Breach, New York. Altshuller, G. (1999) The Innovation Algorithm. (In Russian). English translation by Technical Innovation Centre, Worcester, MA. CREAX (2002) Innovation Suite, V3.1, available at: http:www.creax.com.
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CONTRADICTION ELIMINATION TOOLS
Domb, E. (1997) The Ideal Final Result: Tutorial. TRIZ Journal, February. Ideation International Inc. (1999) TRIZ In Progress, Transactions of the Ideation Research Group. Available at: http://www.ideationtriz.com, February 1999. Mann, D.L. (2002) Systematic Win-Win Problem Solving in a Business Environment. Paper presented at TRIZCON2002, St Louis, April. Mann, D.L. and Dewulf, S. (2003a) Updating TRIZ – 1985–2002 Patent Research Findings. Paper presented at TRIZCON2003, Philadelphia, March. Mann, D.L., Dewulf, S., Zlotin, B. and Zusman, A. (2003b) Matrix 2003: Updating the TRIZ Contradiction Matrix. CREAX Press, Ieper, Belgium. Mann, D.L. (2003c) Complexity Increases And Then . . . (Thoughts From Natural System Evolution). TRIZ Journal, January. Mann, D.L. (2004a) Comparing The Classical And New Contradiction Matrix Part 1 – Zooming Out. TRIZ Journal, April. Mann, D.L. (2004b) Comparing The Classical And New Contradiction Matrix Part 2 Zooming In. TRIZ Journal, May. Mann, D.L. (2004c) Hands-On Systematic Innovation For Business and Management. IFR Press, Bristol.
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Mann, D.L. (forthcoming) TRIZ Tools for Software Developers. IFR Press, Bristol. Salamatov, Y. (1999) TRIZ: The Right Solution At The Right Time. Insytec, the Netherlands. Stalk, G., Pecaut, D.K. and Burnett, B. (2000) Breaking Compromises, Breakaway Growth. In Markets of One. Harvard Business School Press, Berkely.
Darrell Mann is director of Systematic Innovation Ltd, a small consultancy company involved in the advancement, education and deployment of TRIZ and systematic innovation methods. He has been an active member of the global TRIZ community, publishing over 100 papers, patents and patent applications, since departing the employment of Rolls-Royce in 1995. He actively consults with many of the world’s leading organizations. E-mail:
[email protected]
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESINTEGRATION OF TRIZ WITH OTHER TOOLSCREATIVITY AND INNOVATION MANAGEMENT
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The Integration of TRIZ with Other Ideation Tools and Processes as well as with Psychological Assessment Tools Jack Hipple When TRIZ is introduced into an organization setting, it invariably encounters a host of processes and tools already in place. These can include enterprise tools such as Six Sigma, Design for Six Sigma (DFSS), QFD and Lean Manufacturing. It is fairly easy to combine TRIZ problem-solving and technological forecasting with these processes and tools, because most of these enterprise tools are problem-identifying processes that couple easily with the strong problem-solving capabilities of TRIZ. What is more difficult is to integrate TRIZ thinking with other psychologically based creativity and assessment tools. Users and trainers for these various tools tend to be very protective about each process and do not spend sufficient time thinking about ways to integrate the best of all tools. Organizations also frequently use psychological assessment tools to assist employees in career development, but they are seldom used in a proactive way to improve group problem-solving. These assessments can be used proactively within the use and implementation of TRIZ. This paper will review suggested ways to effectively integrate TRIZ innovation and problem-solving principles with these other tools.
Introduction
S
ince the mass production invention lab of Thomas Edison, people have been trying to improve the quality and productivity of the inventive process. New ideas and more efficient innovation processes are always sought within organizations. Prior to TRIZ, all of the improved processes were based on psychological stimulation – changing the thinking patterns and attitudes already existing within the problem-solving group in an attempt to generate ideas that were not seen earlier by these same individuals. These techniques bring no additional knowledge into the innovation session, but attempt to stimulate the knowledge already present within the problem-solving group. TRIZ (Russian algorithm for ‘Theory of Solving Inventive Problems’) is a problemsolving, analysis and forecasting toolkit derived from the study of the global patent literature. Its basis, the study of patterns of invention in the global patent literature, was initiated by a brilliant Russian patent
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examiner, Genrich Saulowitsch Altshuller (1926–1998) in the 1950s. He reasoned that the way to improve the quality and pace of innovation was to study the patent literature where inventions are documented. His premise was that innovation did not need to be a psychologically based process, but a science that could be analysed and made available in a useful and practical way in the same way as any other science, such as physics. In his mind, it was illogical to assume that innovation was unique among the sciences in the sense that it could not be studied in a fundamental way. After a study of the patent literature (which continues today by various TRIZ consulting companies and academic institutions), Altshuller organized the inventive principles he found repeatedly used in a useful form such as a contradiction table, a list of inventive principles, a list of standard solutions and how to graphically describe problems in a standard fashion (substance-field modelling) that couple with standard solutions. A number of companies have also taken these tools and integrated them into software products. This © Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
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paper is not intended to be a treatise on TRIZ, but will review the basic parts of its toolkits to facilitate the discussion of its use with other assessment and ideation tools. We will also review some of the major psychological assessment tools and psychologically based problem-solving processes and then present some preferred ways of integrating TRIZ with these assessments and processes. Though TRIZ is relatively new (circa. 1990) to the West, it has been practised and been part of the education and training system in the former Soviet Union for over 50 years. Basic overview, history, and technological foundation of the TRIZ process are readily available (Altshuller, 1996; Altshuller Institute; Ideation International, 1999; Mann, 2002; Rantanen & Domb, 2002; Salamatov, 1999; Savransky, 2000; TRIZ Journal).
Basics of TRIZ In reviewing the inventive principles used by thousands of inventors around the world, we find that some overriding inventive principles exist. Together with the inventive principles, there are five more tools to mention (for a survey of tools see Moehrle, 2003).
Ideal final result (IFR) Systems and products evolve toward a more ideal state over time. This ideal state can be described as a system performing its function without existing, with no negative side effects, etc. Systems evolve toward this state over time through the resolution of contradictions and the recognition of and use of system resources.
Contradictions Products and systems typically are not ideal because they need to serve dual functionality, creating conflicts in design and operation. The typical approach to these types of problems is to compromise on each function, creating a system that meets several requirements in a less than optimum way. The genius of Altshuller was to recognize that contradiction resolution was the key to inventive problemsolving. The most commonly used inventive principles used in resolving contradictions were captured within the framework of a contradiction table, using 39 physical parameters of a system as X and Y axes. The intersection of a conflicting set of parameters provides the number of appropriate inventive principles on which to focus problem-solving. This table, available from many resources, (Altshuller Institute; Mann, 2002; Savransky, 2000;
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Salamatov, 1999; TRIZ Journal) has continued to be updated via continuous study of the patent literature. New versions of the table, with expanded lists of parameters and updated examples, have also been published (Mann, 2002). This tool has become known as the ‘40 Principles’, based on the original number that Altshuller identified. A separate aspect of contradiction analysis is focusing on conflicts within a parameter itself, as opposed to conflicts between parameters. When this is the case, TRIZ offers four specific ‘separation principles’, shown to resolve these kinds of contradictions. These are: • separation in time (Can the conflicting parameter be separated in time?), for example, a delay in the time setting for an appliance; • separation in space (Can the conflicting parameter be separated in space?), for example, different properties at various places in a toothbrush to achieve different hardness for teeth versus gums; • separation between parts and the whole (Can a conflicting property requirement be achieved by separating the design requirements in space?), for example, a bicycle chain is very flexible at the macro level and very rigid at the micro level; • separation upon condition (Can a property of a system be affected or controlled by differing conditions under which it operates or is presented with?), for example, a material whose viscosity responds to shear. This separation principle can frequently be a different way of viewing the other separation principles, i.e. separation upon condition is frequently also a separation in space or time. It is possible to link these four separation principles indirectly with the original 40 principles (Mann, 2002).
Resources Systems and products seldom use all the resources available to them. Methodical processes to identify and use system resources were developed as part of the TRIZ methodology. These resource classifications include: • • • •
time (including before, during, and after); space (at all levels of a system); material; information (including that being generated but not necessarily recognized or collected); • energy; • fields (including those indirectly generated).
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Define the ideal state for the system or product
Identify resources available to achieve
Identify contradictions preventing achievement of an ideal state
Use contradiction table, 40 principles, and separation principles
New ideas for implementation and evaluation
Generalize problem and compare with standard solutions
Use TRIZ in ‘reverse’ for failure analysis of new ideas
Figure 1. The Basic TRIZ Process In TRIZ problem solving, it is not unusual to find the problem solution contained within the identification of resources initially not seen by problem owners.
Standard solutions In analysing inventive problem solutions, Altshuller was also able to make general models of problems and couple these with ‘standard’ solutions that could be used independent of the specific nature of the technology or industry. These models and standard solutions are captured in a number of references (Ideation International, 1999; Salamatov, 1999; Savransky, 2000) and are also are the original basis for much of the commercially available TRIZ software.
Lines and patterns of evolution The study of the patent literature also reveals that there are reproducible patterns of technological evolution for technical systems. The original eight lines of evolution have been extended and dissected in a number of ways by both consultants and software providers. These lines have also been extended and modified for non-technical systems.
‘Reverse TRIZ’ This is a process wherein the basic TRIZ algorithm is inverted and used for failure analysis and prediction. For example, we have a system or product failing for unknown reasons. We invert this problem statement and state what we wish to have this problem. Next we exaggerate the inverted problem (we want this failure, etc.) to occur all the time. We then ask: how could this be accomplished? Do we have the resources, etc? This technique is used as an
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alternative to such tools as FMEA and HAZOP when these standard industry analytical tools have failed to identify failure or potential failure mechanisms and routes. An outline of the basic TRIZ process is shown in Figure 1. Details on all these different aspects of TRIZ are beyond the scope of this paper, but suffice it to say that TRIZ is a structured (‘leftbrained’) approach to inventiveness and problem solving, whose basis is that most problems we encounter have already been solved in a generic sense. Thus the focus of TRIZ problem solving is to model a problem in a basic, fundamental way and then to use the previously described principles (which apply to all inventive solutions) to solve it. ARIZ (‘Algorithm for Solving Inventive Problems’) is a sophisticated model for combining all of the previous tools, and again, it is the basis for much of the commercial TRIZ software. It will not be discussed here. There are many ways that the elements of this toolkit are used or combined in a typical TRIZ problem-solving effort. These include remote consulting by a consultant knowledgeable about the various tools and the problem of concern and providing solutions directly to a client, to on-site group problem-solving session (varying in length) using the aspects of the toolkit and/or software as desired. Combinations of these two extremes are also used commercially. The structure of any of these efforts or work sessions usually follows the following structure: 1. Define system ideality or ideal final result. 2. Evaluate resources that are available to achieve the ideal state. 3. Define contradictions that prevent the achievement of the ideal final result. 4. Use the resources and suggested problemsolving principles (from standard solutions,
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Morphological matrix
Brainstorming
Evaluation matrix musts/wants
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Random image stimulation
Idea generation
Sort, evaluate and prioritize idea output
SCAMPER and other techniques
ALUo matrix paired comparison analysis
Figure 2. The Basic CPS Process contradiction table) to generate breakthrough ideas to resolve contradictions. 5. Evaluate the problem and solution against the TRIZ lines and patterns of evolution to provide additional idea and longer-term ideas. 6. Uses ‘reverse’ TRIZ to analyse for potential failure routes and mechanisms. Time for an initial on-site TRIZ problem solving session can vary from 2–5 days depending upon quantity of solutions desired, and the nature of the complexity of the problem. Longer-term consulting with TRIZ on strategic new business or product development can be done over a period of months and years, no differently than other type of intensive problem-solving consulting. There is no one standard way of using the TRIZ problem-solving toolkit described above, and the tools and the exact process will vary with the type and nature of problem, the problem owner, the organizational situation and environment, and the expertise and preferences of the external consultant (if used). The process can be used to generate a large quantity of ideas for further evaluation or to generate a few focused ideas for immediate use. TRIZ software can be used successfully, but only after the group understands the basic thinking fundamentals of the process.
Creative problem solving (CPS) Creative problem solving (CPS), first developed in the 1950s by Alex Osborne, was an attempt to improve the simple process of brainstorming. An overview of this process is
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shown in Figure 2. Summaries of the process and the various tools in its toolkit are available (Isaksen, Dorval & Treffinger, 1998; MBTI 1998). The major improvement made in this process, compared to previous ideation techniques, was to separate the idea generation phase of the process from the idea evaluation process in order to ensure the maximum volume of idea generation. The number of ideas generated in a CPS session is a primary success measurement used by most CPS facilitators. The assumption here is that the more ideas generated, the more likely it is to generate sufficient ideas of further interest that could solve the problem of concern. The separation between idea generation (the divergent phase) and idea combination, critique and evaluation (the convergent phase) allows the generation of ideas without fear of criticism or preliminary evaluation from other participants. This process, over time, has also developed a number of idea-generation tools to improve the quality of the ideas produced during the divergent phase. This process and its tools are widely used within group problem-solving sessions, and are the focus of several conferences. A consulting industry has also built up around the process, its concepts and improving its use and application. One of the fundamental differences between CPS and TRIZ is that TRIZ does not make the linkage between the need to generate a large quantity of ideas to generate the optimum solution. TRIZ has the capability to produce an optimum solution without the need to analyse many alternatives. One of the commonly used CPS models would look as follows, dividing into (i)
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the divergent phase and (ii) the convergence phase:
Divergent phase In the idea generation using brainstorming (assisted with Post-It™ notes if desired), brain writing, image stimulation, the SCAMPER idea generation (Substitute, Combine, Adapt, Modify, Eliminate, Put to other Uses, Reverse) tool. A morphological matrix, where a few basic properties of a system or product are varied deliberately, can also be used to stimulate ideas. Each major attribute of the system under consideration and the group lists possible parameters or characteristics of each parameter. Randomly combining each of the entries of the matrix can generate hundreds or thousands of possible ideas.
Convergence phase Once a list of ideas has been generated by the divergent phase of the CPS process, these ideas are narrowed and focused by a number of different tools, regarding focusing, screening, and selection and use of an evaluation matrix, comparison against criteria, highlighting and grouping, prioritization against absolute needs, and comparison of ideas with each other.
ALUo This focusing tools asks the participants to analyse each idea in terms of its Advantages, Limitations, Unique qualities, and overcome limitations to assist in idea optimization, prioritization and selection. Evaluation matrix This tool lists the ideas selected for final evaluation against evaluation criteria established by the problem-solving group or its organization. In a simple table, the ideas are listed vertically and the criteria listed horizontally. At each intersection, a rating or relative ranking is made, assisting in the final decision-making of the group. Other techniques for focusing and choosing include distinguishing between musts and wants, and deliberate paired compared analysis of ideas against each other.
Using CPS as an umbrella for the use of TRIZ CPS can be used as an ‘umbrella’ under which to incorporate many of the simple TRIZ problem analysis and problem solving tools (Figure 3). The details of this overall diagram and structure will now be discussed.
Idea generation Brainstorming
Identify contradictions in ideas generated and use contradiction table to resolve
TRIZ ‘feature transfer’ function
Improve ideas
SCAMPER and other techniques
Use 40 inventive principles in random fashion as idea stimulants
Revisit with TRIZ concepts of ideal final result and resources
Figure 3. A CPS umbrella for TRIZ
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Segregated brainstorming via Six Hats™ process
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Generate new ideas
Introduce provocations with the ‘po’ concept
Figure 4. Overview of Lateral Thinking™ and Six Hats™ Use
There is complete agreement between TRIZ and CPS with regard to the concept of separating the idea generation from the idea evaluation phase of an innovation process. In fact, TRIZ consultants frequently make a point of noting individual statements made during a problem-solving session, such as ‘that won’t work because . . .’, to illustrate and take note of a contradiction for later consideration. TRIZ adds more emphasis on the problem-definition aspect. In the idea generation phase of CPS, the emphasis is on quantity of ideas on the assumption that the ratio of ideas generated to potentially valuable ideas is a constant. TRIZ argues that a well-defined problem eliminates the need for a high ratio, but if this is the desired goal, this is how TRIZ principles can be used to improve the CPS process, assuming that it is the overriding general process to be used.
TRIZ tools for the CPS idea generation phase The simplest way to introduce a small part of the TRIZ toolkit into CPS is simply to use the original 40 principles as part of the general idea generation phase. Suggest one of the principles at any time. Continue to do this with all 40 principles. An interesting exercise at the end of such a session would be to compare the ‘roots’ of the various ideas selected and compare the efficiency of the 40 principles with other techniques used. A second way is to write each of the 40 principles on a Post-It™ note and distribute them among the group and ask for ideas via that stimulus as part of a brain-writing exercise. A third way, connecting with the visual CPS image stimulation tool, is to use a simplified diagram of a particularly inventive patent, illustrating an inventive principle, which may not necessarily have any direct connection to the problem at hand. The SCAMPER idea generation tool mentioned previously has some strong overlaps with parts of the TRIZ toolkit. For example, asking the ‘E’ question ‘eliminate?’, that is, ‘can we leave it out? Have fewer parts? Make it lighter, shorter’ and so on, are all examples of questions that would be asked by a TRIZ facilitator while focusing on the question of ‘resources’. Since the TRIZ definition of resources (see previous list) is far more
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comprehensive, additional ideas would be expected. Similarly, the SCAMPER ‘A’ question relating to ‘adapting’ (What could I copy? Does the past offer a parallel?) would allow a knowledgeable TRIZ participant to suggest a more formal review of parallel industries that might have similar problems or the use of TRIZ software products that contain or have access to this kind of information immediately. The ‘R’ question is a restatement of one of the basic TRIZ principles, ‘do it in reverse’.
TRIZ tools for the CPS convergent phase The ALUo analysis described above will generate a list of limitations to which the TRIZ contradiction table, 40 principles and separation principles can all be applied, as opposed to simply brainstorming solutions to the problems identified. The CPS evaluation matrix is an excellent tool for graphically displaying contradictions. The TRIZ tools used for contradiction resolution, 40 principles and separation principles can be used to solve these contradictions. Paired comparison analysis is similar to a TRIZ technique (not mentioned previously) called ‘feature transfer’ where we ask what good features of a known good idea (or one good aspect of an overall poor idea) can be transferred to another idea to make an overall better idea. The CPS evaluation matrix, identifying why a particular idea does not meet the criteria, can be supplemented with TRIZ problem-solving tools to improve the suggested ideas. TRIZ can be used to expand the utility of paired comparison analysis by using this tool, not as one solely to compare and prioritize ideas, but as a list of ideas whose features can be transferred to other ideas.
Six HatsTM and Lateral ThinkingTM These tools and processes, developed by Edward DeBono and the subject of numerous books and publications, take the separation in idea generation and evaluation used in the CPS process to a higher level. Excellent summaries are available (DeBono, 1999, 1985, 1973). In the following, Six Hats™ and Lateral Thinking™ are introduced.
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Six Hats™ Six Hats™ is a problem-solving process, during this participants are asked to wear different ‘hats’, allowing only a certain type of thinking and sharing. This is a more focused way of eliminating the immediate criticism of ideas that typically occurs during idea generation. These hats have colour codes as follows: • blue – discussion of the meeting and idea generation process itself; • green – idea generation; • white – discussion of information needed to define the problem or to evaluate proposed ideas and solutions; • yellow – discussion of positive aspects of an idea or ways to improve it; • black – discussion of negative aspects of ideas or their implementation; • red – discussion of emotional or ‘gut feel’ about an idea, regardless of facts or information. This ‘gut’ or emotional feeling could be positive or negative. These hats can also be used in varying sequences depending upon the nature of the problem. Six Hats™ facilitation can also involve the wearing of ‘hats’ of these various colours to accentuate the differentiated thinking as a function of time. The major point is that all participants in the session wear the same ‘hat’ and think along the same lines at the same time.
Lateral Thinking™ Lateral Thinking™ is a formal technique used to generate provocations, and if used in conjunction with Six Hats, would typically be used under the green hat. It provides a specific technique known as ‘po’ (‘provocative operation’) to purposely reverse the problem or another orthogonal way of looking at the problem. Lateral Thinking™ contains specific suggested ‘po’s’ to use in various situations.
Using the Six HatsTM and Lateral ThinkingTM processes as an umbrella for use of TRIZ tools These processes, as was the case with CPS, are inherently limited by the knowledge of the individuals in the problem-solving group. TRIZ adds the knowledge of patterns of invention from the global patent literature and well as knowledge from outside the room to both tools. As with CPS, there is a tendency within an organization, evaluating a competitive
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process, to focus on the differences instead of the possibilities of combining TRIZ within an existing problem-solving structure. TRIZ tools can be used under the umbrella of the ‘hats’ and integrated into this process (transparently if necessary!) as follows.
Blue This ‘hat’ is used to discuss the structure of the problem solving and meeting process itself. It is typically used at the onset of the group session, but can be revisited at times during the problem solving session as deemed necessary by the group or its facilitator. Under this hat, the high-level TRIZ algorithm is used to check the logic of the meeting process itself. For example, has the problem been adequately defined? Has an ideal state been clearly defined? Are the problem owners in the room? Have the consequences of both success and failure been thought through adequately?
White This ‘hat’, used in clarifying and asking about informational aspects of a problem, can be supplemented in its effectiveness by various pieces of the TRIZ toolkit. What information is a system generating but is not being collected? Is there enough information to clearly identify the conflict in a problem? Are there parallel industries or technologies that can be searched and studied?
Green When generating ideas under this ‘creative’ hat, the 40 principles can be used for general stimulus, and if, under the blue or white hats, a contradiction has been identified, the TRIZ contradiction table can be used to resolve these identified contradictions, resulting in new ideas.
Red Even though this hat is typically used for only a short period of time, the feelings and expressions may be indications of past knowledge and experience, which if captured properly, can add value. For example, an expression of ‘that won’t work’ may in fact be rooted in an experience with an inherent contradiction that TRIZ may be able to solve. Encouraging a participant to express this concern as a contradiction (rather than some general statement of dislike) sharpens and clarifies the issue and
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Use TRIZ 40 principles
Lateral thinking and “po” provocation
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Problem definition with the addition of TRIZ concepts of ideal final result and resource identification
Use of TRIZ techniques in association with each Hat
Idea generation
Six Hats™ segregation in idea generation
Analysis of ideas for contradictions and resolution via contradiction table
Figure 5. A Six Hats™ and Lateral Thinking™ Umbrella Structure for the Use of TRIZ allows the use of contradiction-resolution tools.
Yellow While asking and considering what is good about a particular idea or concept, the TRIZ concept of IFR can be used aggressively for each idea already expressed. The question of how to make an idea even better will also elicit expressions of limitations, which in turn can be translated into contradictions, again allowing several TRIZ contradiction tools to be used.
Black In the group’s discussion of negative attributes of an idea or concept, it is best to express these concerns in the form of a contradiction, allowing the use of TRIZ contradiction-resolution tools. Using ‘reverse’ TRIZ to ask how we might intentionally cause this idea to fail commercially or technically can also develop solutions to problems whose root cause has been identified. A summary of a proposed combination of the Six Hats™ process with TRIZ tools is shown in Figure 5.
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Using individual psychological assessment tools in conjunction with TRIZ problem solving As with problem-solving tools and processes, most organizations use various psychological assessment tools to assist employees in self-development, provide input that allows employees to improve their interactions with other employees, or for career planning. These tools include the Myers Briggs Type Indicator™ (MBTI, 1998) and alternatives such as the Herman Brain Dominance (HBDI™) model and the KEYS™ (Keirsey, 1998) assessment tools. Many large consulting firms also have developed their own proprietary audit and assessment tools. The degree of psychological validation of the various assessments should be considered prior to using in a broad-based, organizational way. Assessments focusing more on an individual’s problem-solving style and their preferred behaviour in problemsolving team situations (Kirton KAI™, BCPI™/FourSights™) are also used. The objective of this paper not to compare or explain all these different assessment instruments, nor to cover all the possible connections and comparisons between them. The reader is referred to the appropriate references
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for detailed information on any one particular assessment instrument. In this paper, we will review two examples of TRIZ use with two of these assessment tools that are widely used. These assessment tools are (i) the Myers Briggs Type Indicator™ and (ii) the Kirton KAI™.
The Myers Briggs Indicator The Myers Briggs Indicator, commonly referred to as MBTI™, is one of the most widely used psychological assessment tools. In addition to the MBTI™, there are competitive assessments such as 16Types™ and Insights™, generating the same type of analysis. However, all of them analyse the same aspects of social behaviour, all have been validated, and the comments following apply to all similar instruments. Basically, these assessments measure an individual’s style of social interaction in the following categories: • Extroverted/Introverted (E/I): is someone basically inward or outward focused in their interactions with others? In a problemsolving session, this characteristic may indicate a person’s willingness to express his/ her opinion or the desire to enter into a discussion or argument about the quality or validity of an idea. • Sensing/Intuitive (S/iN): Is someone’s perception process based on the senses of sight, touch, smell, hearing, taste? Is hard data of primary concern? Or is it more intuitive and based on meanings, relationships, and insights? In a problem-solving session, this difference may manifest itself in how someone reacts to an idea and how interested he/she may be in evaluating or implementing it. • Thinking/Feeling (T/F): what is the nature of an individual’s decision making process? Is it based upon impersonal logic, objectivity, and fact or upon feelings and personal values and standards? In a problem-solving session, this difference will show itself in the manner in which someone reacts to an idea, for example, gut feeling or asking for facts and data. • Judging/Perceiving (J/P): what is a person’s drive toward closure, organization, plans and schedules? A person having an open-minded perceiving attitude versus a judgemental, closure one. An individual profile is a four-letter summary, covering each aspect of the evaluation. An example of a profile determined by such an instrument would be INTP (Introverted, iNtuitive, Thinking, Perceiving). There are 16 possible combinations, and thus a wide diversity in any group’s participants. This type of
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assessment instrument is widely used within organizations, but very seldom within the context of TRIZ problem solving. Participants may be aware of their individual profiles or of the behavioural patterns that are normal for them, but have seldom proactively used it personally, let alone in a group problem-solving situation. There are 16 possible combinations of profiles and not equally distributed by gender, race, occupation, level in an organization. As two examples, 75 per cent of the general population are ‘E’s’ or extroverts and 75 per cent of the population are ‘S’s’ (MBTI, 1998). Twothirds of men are ‘T’s’ and two-thirds of women are ‘F’s’ (MBTI, 1998). There is no reason to expect any particular distribution in a problem-solving group. How can this knowledge be used to improve a TRIZ session? First of all, the knowledge of ‘E’ versus ‘I’ profiles can assist a session leader in soliciting input from individuals less likely to actively participate. Second, the level of problem definition and attack can be segregated and matched with the ‘T’ versus ‘F’ component of the MBTI™. A less-defined, longer range, more ideal vision of a problem solution can be done by one group, while another sub-group can look at more practical, shorter-term aspects. In all problem-solving sessions, closure is desired on post-session action items. A combination of ‘S’ and ‘T’ people might be used to structure and plan immediate activities; while a group of ‘P’ individuals might be asked to look at longerterm options requiring additional research or exploratory efforts. Individuals with a strong ‘F’ preference would be outstanding at evaluating the people impact of new ideas generated from a TRIZ session. These suggestions are not meant to be all-inclusive, but starting points in thinking about how to use, from a TRIZ perspective, the resources already present within the group. The reader can think of other examples of proactive use of this information in combination with TRIZ problem solving. The extent to which this might be useful will be a function of the degree of permanence of the problem solving team, time the team is going to be together as a team, how much of the discussion is done remotely and so on.
Kirton KAI™ A further very useful assessment tool is the Kirton KAI™ assessment tool, which measures an individual’s problem-solving style (not capability). One can think of this instrument, in a very general sense, as assessing one’s relationship to problem solving in the
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same sense as the MBTI™ measures one’s relationship to other people. The KAI™ instrument contains 32 questions, which can be completed in 15–20 minutes. Examples of the types of questions would include: how easy or difficult is it for you to present yourself, long term and consistently, as someone who conforms, enjoys detailed work, is stimulating, is predictable? Another similar instrument, whose output is in the form of bar graphs and is available online, is the Buffalo Creative Process Inventory (BCPI™) – now referred to as FoursSights™ (Puccio, 2002). For the purposes of this paper, we will use the KAI™ as the instrument for the discussion focal point. The output of the KAI™ is an output number ranging from 32–160 with the ‘norm’ around 90 and a two-sigma deviation from 70–120. This varies a slight amount by gender and national origin, but not significantly. Subscores, which can vary significantly, highlight particular areas such as originality, rule/ group conformity and efficiency. These subscores sum to give the total KAI™ assessment number mentioned earlier. These sub-score areas are as follows: • originality (total from 13–65). This can be looked upon as a measure of an individual’s raw capacity to generate ideas. A person with high originality sub-score will tend to generate a large quantity of unfiltered ideas. A person with a lower subscore will tend to filter ideas prior to expressing them. This filtering could be as a result of a sense of impracticality or fear of being ridiculed. Note that this is a different aspect than extroversion and introversion as mentioned previously during the MBTI™ discussion; • efficiency (total from 7–35). This is a measure of how structured and visible an individual’s problem-solving process is to others. For example, if someone is talking and discussing an idea concept and the methodology and thinking process are readily visible to everyone, this individual probably has adoptive efficiency sub-score. However, if an individual always appears to others as ‘coming out of the blue’ and their thinking and analysis process is not externally visible, they likely have innovative efficiency sub-score. • rule and group conformity (total 12–60). This is a measure of an individual’s tendency toward needing group processes, norms, and structures during problem solving. A strongly adaptive rule-and-groupconformity person will be very concerned with group consensus around an idea and respect rules and procedures that may be in
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place for idea evaluation, implantation and so on. A strongly innovative person on this sub-scale will be more challenging to norms and procedures, permissions required and group consensus prior to proceeding. How can this information be used to improve the quality of output of a TRIZ session? A TRIZ problem-solving session, as indicated earlier, has a strong problem definition aspect to it, even more so than other creativity and problem-solving processes. The structure of typical TRIZ problem solving effort helps both sides of the KAI™ problem-solving spectrum – a very unusual and positive aspect of TRIZ. It provides stimulus, via its basic concepts (IFR, use and recognition of resources, identification and resolution of contradictions) that assist in problem definition. The stimulus of previous patent examples and strict causeand-effect modelling for provided by TRIZ software products positively impacts both ends of the KAI™ spectrum. Prior to a TRIZ session, the KAI™ instrument can be completed by participants and then feedback provided as part of the TRIZ session. It is sometimes productive and educational to segregate the group, via scores known only to the instructor, to illustrate how different people approach problem-definition and solution ideas. In one actual industrial case study (Hipple, 2003), KAITM assessments were made prior to a TRIZ problem-solving session with a Fortune 100 US chemical company. The KAITM scores were segregated very strongly into adaptive (scores 80–85) and innovative (110– 130) problem-solving styles, with no representation of mid-range problem solving. Each group was asked to separately diagram the problem they were addressing using a commercial TRIZ software product. Figure 6 shows the cause and effect diagram created by the more adaptive segment of the group. One can see the structure and organization apparent in the diagram and problem definition. Figure 7 shows the same basic problem diagrammed by the higher KAI™ (more innovative) segment of the group, demonstrating the lack of need for structure and organization for this group. There were also differences in the number of contradictions identified, but this cannot be tied directly to the KAI™ scores as this is seen frequently in TRIZ sessions. Links between exact problem definition and the KAI™ needs further research. The discussion which occurred after the diagrams and their associated idea output were presented consisted of the following types of questions: • Why do you see the problem that way?
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• How can you possibly make any sense of your diagram? • Why did you choose those particular ideas to pursue? This difference in KAI™ perspective was also used in a proactive way to separate short- and long- term action items resulting from the session.
Summary and conclusions
Legend: Useful Function
Harmfull Function
Figure 6. TRIZ Cause and Effect Diagrams Drawn by KAI™ Adopters Source: modelled by Ideation WorkBench
TRIZ uses a fundamentally different process and framework to the problem-solving environment, which is difficult for many individuals and organizations to understand and accept. It is frequently introduced into organizations with existing problem-solving processes (two of the major ones having been discussed in this paper). This paper has presented overviews of other tools and how they can be integrated in a preliminary manner with the TRIZ toolkit in order for an objective analysis to be done without having to make a discrete choice between one or the other being ‘better’. Though this author believes TRIZ to be a superior problem-solving tool, it is far better to have part of the methodology tried and used within an existing problem-solving structure such as Six Hats™/Lateral Thinking™ or
Legend: Useful Function
Harmfull Function
Figure 7. Cause and Effect Diagram Drawn by KAI™ Innovators Source: modelled by Ideation WorkBench
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INTEGRATION OF TRIZ WITH OTHER TOOLS
CPS than not to be used at all. Once the various tools have been validated, curiosity will occur about the nature and origin of these tools, leading to additional learning and use of the complete TRIZ process. The fundamental limitation of these processes and tools is their reliance on psychological stimulation or organization of the session and session participants. The processes and tools, on their own, bring no additional problem-solving or technical knowledge beyond that which already exists within the group of problem-solvers. One of the major strengths of TRIZ problem solving is not only its ability to provide structure (beyond that provided by CPS or Six Hats™) to a problem-solving situation, but its capability, through both its problem-definition and problem-solving principles, to inject new knowledge that did not exist in the group originally. In summary, it is not necessary to ‘replace’ processes such as CPS or Six Hats™ in totality to have TRIZ problem-solving concepts demonstrated and evaluated. Many TRIZ tools can be used under the umbrella of these and other processes to demonstrate their value, and produce interest about the entire TRIZ process and toolkit. It is also possible to use known, or easily obtainable, psychological-profile information, to make a TRIZ implementation and problemsolving session more productive and rewarding, not only for the problem owners, but also the problem-solving participants themselves. Assessment tools such as MBTI™ and KAI™, usually used solely to assist individuals in career planning or in work environment situations can be used proactively within TRIZ problem-solving sessions to maximize their productivity.
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Keirsey, D. (1998) Please Understand Me II: Temperament, Character, Intelligence. Prometheus Nemesis Book Co., Del Mar. Kirton, M.J. (1984) Adaptors and Innovators – Why New Initiatives Get Blocked. Long Range Planning, 17, 137–143. Kirton, M.J. (1980) Adaptors and Innovators. Planned Innovation, 3, 51–54. Mann, D. (2002) Hands On Systematic Innovation. CREAX, Ieper, Belguim. MBTI (1998) Training Materials from CAPT. Consulting Psychologists Press, Gainesville, FL. Moehrle, M.G. (2003) Implementation of TRIZ tools in companies: Results of a cluster analysis. The R&D Management Conference, Manchester. Puccio, G. (2002) FourSight: The Breakthrough Thinking Guide. THinc Communications, Evanston IL. TRIZ Journal. Available at: http:// www.triz-journal.com Rantanen, K. and Domb, E. (2002) Simplified TRIZ. CRC St. Lucie Press, Boca Raton. Salamatov, Y. and Schaaf, van der G.B.(1999) TRIZ: The Right Solution at the Right Time. B.V, Enschede. Savransky, S. (2000) Engineering of Creativity. CRC Press, Boca Raton.
™Six Hats and Lateral Thinking are registered trademarks of Edward DeBono ™KAI is a registered trademark of Michael J. Kirton ™KEYS is a registered trademark of the Center for Creative Leadership ™BCPI and FourSights are registered trademarks of Gerard Puccio ™Myers Briggs type indicator and MBTI are registered trademarks of Consulting Psychologists Press ™Post-It Notes is a registered trademark of 3 m Corporation ™HBDI is a registered trademark of Hermman International
References Altshuller, G. (1996) And Suddenly the Inventor Appeared. Technical Innovation Center. Altshuller Institute for TRIZ Studies, http:// www.aitriz.org DeBono, E. (1999) Six Thinking Hats Application Methods. APTT, Des Moines, Iowa. DeBono, E. (1985) Six Thinking Hats. Little, Brown & Co., Boston MA. DeBono, E. (1973) Lateral Thinking: Creativity Step by Step. Harper & Row, New York. Hipple, J.W. (2003) The Integration of TRIZ Problem Solving Techniques with Other Problem Solving and Assessment Tools. Proceeding of the Altshuller Institute 2003 Annual Conference. Ideation International (1999) Tools of Classical TRIZ. Ideation International, Southfield. Isaksen, S.G., Dorval, K.B. and Treffinger, D. (1998) Toolbox for Creative Problem Solving: Basic Tools and Resources. The Creative Problem Solving Group, Buffalo.
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Jack Hipple is Principal in the consulting firm, Innovation-TRIZ. In addition to his formal training in chemical engineering (BS ChE, Carnegie Mellon University, 1967) and a 30-year career in the chemical industry, he is certified and trained in most major creativity, innovation, and organizational assessment techniques. For the past six years, he has focused on TRIZ training and problem solving and teaches TRIZ for the American Institute of Chemical Engineers and the Society of Mechanical Engineers. He also runs TRIZ training workshops for the Institute of International Research, the Innovation Network and the World Future Society.
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESAPPLYING TRIZ TO SERVICE CONCEPTUAL DESIGNCREATIVITY AND INNOVATION MANAGEMENT
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Applying TRIZ to Service Conceptual Design: An Exploratory Study Jun Zhang, Kah-Hin Chai and Kay-Chuan Tan This paper introduces a new avenue for applying the Theory of Inventive Problem Solving (TRIZ). TRIZ tools may be used in designing new service concepts in the field of new service development (NSD). Up to the present time, the practice of generating new ideas in NSD has been dependent largely on inspiration, luck and flair. One shortcoming in the generation of creative ideas is the psychological inertia or mental block commonly encountered. This research proposes to use TRIZ to help spawn new and perhaps unorthodox ideas and concepts in NSD. A case study on canteen operation demonstrates the feasibility of applying TRIZ in service design.
Introduction
W
ith the shift from manufacturing to services, the issue of new service development (NSD) has grown in importance. Companies such as General Electric, Xerox and Hewlett Packard, which until a few years ago generated a majority of their profit from selling physical products, are rapidly transforming themselves into service providers. To remain competitive in the service market, companies actively seek creative ways to generate new service concepts that can meet customer needs. Service design is distinct from the development of physical products due to characteristics such as customer participation, perish ability, intangibility and heterogeneity. These characteristics collectively impact service development and make it more challenging than physical product development. It is, thus, of no surprise that the art of service development has tended to be ad hoc and haphazard in nature (Metters, King-Metters and Pullman, 2003). In response, NSD researchers have developed processes such as the stage-gate product development process (Cooper, 2001). A number of NSD process models have been proposed in the last decade. However, as observed by Johnson et al. (2000) and Bowers (1989), most of the existing NSD process models ignore the unique characteristics of ser-
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vices, and are based mainly on a small number of new product development processes. Johnson et al. (2000) further argued that little empirical work exists to validate the processes across different service industries. Recent studies showed that one of the weakest activities in a typical NSD process is idea generation. Although idea generation is a pivotal pre-development activity, it has not been addressed adequately by researchers (Bowers, 1989; Edgett, 1996; Kelly & Storey, 2000). Service developers have tended not to engage in formal ways of idea generation (Easingwood, 1986). Instead they rely largely on the experiences of front-line staff or customers. One consequence has been that the quality and innovation levels of new ideas are severely affected because of the psychological inertia inherent in human thinking. The tendency is to focus on what is known (i.e. along the assumed search direction), thereby keeping the solver from the right solution (Savransky, 2000). To overcome this limitation, one solution is to identify and establish a systematic and operational mechanism in idea generation. Menor, Tatikonda and Sampson (2002) discussed that more studies have to be done on the operational tools employed for successful NSD. Johnston (1999) argued for the development of good design tools and techniques in NSD. This paper proposes an approach to new service development through the use of TRIZ. © Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
APPLYING TRIZ TO SERVICE CONCEPTUAL DESIGN
Literature Review In this section, a literature review is presented in the following three areas: (i) new service development; (ii) service design tools; and (iii) TRIZ.
New Service Development Traditionally, new service development processes were rather informal and employed ad hoc procedures (Metters et al., 2003). As a result of the intangibility of services, providers find it difficult to control and measure the specification or quality of services before launch. For this reason, service companies tend to revamp service development processes in their own ways. As a result, many service developers would rather believe that new services came about as a result of intuition, personal fancy or inspiration (Gummesson, 1989; Langeard, Reffiat & Eiglier, 1986). However, several researchers holding the opposite view argue that new services are more likely the outcome of formal development processes (Bowers, 1989; Martin & Horne, 1993; Sheuing & Johnson, 1989). A number of NSD process models have been proposed in the last decade. Johnson et al. (2000) reviewed and classified NSD process models into the following three categories: partial models, translational models and comprehensive models. The results of the exploratory research in formalizing NSD is beginning to convince many people that the process of NSD can be as systematic as that of new product development (NPD). In between the above two opposing views, some researchers argue that services tend to use less formal NSD processes than those found in NPD (Griffin, 1997). The innovation and adoption of new services must be both a planned process and a happening (Edvardsson, Haglund & Mattsson, 1995). Although this discussion is still inconclusive up to now, recent findings have demonstrated that it is possible to increase the degree of formality in NSD processes. A good example for this is the NSD process cycle proposed by Johnson et al. (2000). Menor et al. (2002) considered their research to be crucial in increasing the speed and effectiveness of developing NSD competencies.
Service Design Tools It is generally agreed among service researchers that a poor strategy is to depend totally on luck in developing new products (Zeithaml & Bitner, 2000). Indeed successful service firms have been found to sometimes adopt elaborate
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ways of developing services (Shueing & Johnson, 1989). In the previous decade, much effort had been put into formalizing NSD. Besides the proposition of formal NSD process models, service design tools, such as service blueprinting (Shostack, 1984), functional analysis (Berkeley, 1996), and structured analysis and design (Congram & Epelman, 1995), have also been developed to assist service designers in developing service concepts. The limitations in existing service design tools to overcome psychological inertia in problem solving have severely affected both the amount and the quality of design solutions. The challenge is compounded by the illstructured nature of design problems where often one or more steps (or states) are either unknown or incoherent. Furthermore, sufficient information concerning the initial state and the properties of the goal state are rarely fully specifiable in advance (Goldschmidt, 1997). Compared to methods such as brainstorming, lateral thinking, morphological analysis and mind mapping, Savransky (2000) argued that only TRIZ would be useful for solving difficult problems with unknown causes and unknown search directions. The knowledge-based toolkit provided by TRIZ is very effective in helping problem-solvers to overcome their own psychological inertia, which is considered the hardest part in solving difficult problems (Altshuller, 1984). Mann and Dewulf (2002) argued that in terms of its toolkit, method, strategy and philosophy, TRIZ is the most comprehensive of any available model. TRIZ has great potential to integrate with many innovation tools, such as six sigma, quality-function deployment and neuro-linguistic programming.
TRIZ TRIZ is the Russian acronym for the Theory of Inventive Problem Solving. It was developed by Genrich Altshuller and his colleagues in 1946, in the former USSR. The hypothesis of TRIZ research is that if there are existing universal creativity principles that can be identified, codified and taught to people, the innovation process can be made more predictable. The grounding TRIZ research was done by way of analysing over two million patents worldwide, from the 1940s to the 1980s. Through this detailed work, a number of innovation patterns and laws of ideality were identified and extracted. The distinct features of TRIZ can be summarized as follows: • it helps to generate many quality ideas in a systematic and efficient manner;
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• it helps to overcome psychological inertia by formulating an exhaustive set of possible solutions; • it encourages breakthrough thinking without trade-off or compromise. With several decades of development and application, TRIZ has proven its effectiveness and efficiency in resolving technical problems for physical product design (Altshuller, 1997; Rantanen & Domb, 2002; Terninko, Zusman & Zlotin, 1998). In recent years, especially since the establishment of the TRIZ Journal in 1996, there has been greater interest in applying TRIZ to various other fields, such as NPD and technology management (Clausing, 2001; Ungvari, 1999), education and training (Marsh, Waters & Mann, 2002; Rivin, 1998; Schweizer, 2002), biology (Vincent & Mann, 2000), and business management (Mann &Domb, 2001; Ruchti & Livotov, 2001). The integration of TRIZ with other leading methodologies, such as quality function deployment (Domb, 1998; Schlueter, 2001; Terninko, 1998) and six sigma (Tennant, 2003; Verduyn, 2002) has also demonstrated strong potential. Thus far the literature on applying TRIZ in services development has been limited (see Low et al., 2001 and Rantanen & Domb, 2002, for a review of possible applications of TRIZ in services). No systematic examination has been done to explore the use of TRIZ in resolving problems in service design and development. The present research made modifications to several TRIZ tools. These were applied to resolve problems in a service context. By doing so, we introduced a new means of succeeding in service design – one that is able to achieve systematic innovation using formal tools and steps.
Theoretical Approach As mentioned, TRIZ is useful in product design because of its unique method of problem resolution. In this section, the synergy between TRIZ and service problem solving is discussed, together with an explanation for how TRIZ would be useful in new service design and development. TRIZ analyses problems through the unique perspective of contradiction. In technical areas, contradictions are relatively more tangible and easier to appreciate. Although service products are different from physical products, contradictions are also often found in services. Service contradictions may seem more intangible and abstract than those found in technical areas. Table 1 lists a few common service
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contradictions. Each of the contradictions is expressed briefly with key contradictory terms. For instance, the contradiction of ‘standardization versus customization’ used to be a common problem in service industries. With the introduction of computer technology that enabled individual personalization, the contradiction of mass customization is no longer unsolvable. Since TRIZ provides a powerful toolkit to separate contradictions without the need to compromise, it might be possible to use TRIZ to resolve service problems that have embedded contradictions. The second synergy between TRIZ and services concerns the issue of innovation in service design. TRIZ has a very large knowledge base consisting of information abstracted from patent analysis. This information has been well instilled into the creation of several TRIZ tools, such as the 40 inventive principles, 4 separation principles, patterns of technological evolution and the 76 standard solutions. A parallel with innovation tools in service design can be drawn. For instance, Berry and Lampo (2000) identified the following five categories of service redesign: self-service, direct service, pre-service, bundled service and physical service. The comparison between these five service redesign patterns and TRIZ’s 40 inventive principles reveal similarities in their concepts. The implication is that service innovation may be brought about by the use of an identified and codified innovation methodology. The NSD process can be made more predictable. To substantiate the above point, Zhang, Chai and Tan (2003) collected numerous examples in service operations management. They found that the examples could be categorized into the original 40 inventive principles. Despite differences between goods and services, it was found that most of the inventive principles and their innovation patterns could be applied to the service sector. Zhang, Chai and Tan (2003) also found that an enhanced set of service innovation patterns could be developed to better portrait generic innovation patterning in the service sectors. This research shows that with appropriate modification, TRIZ tools can be applied to resolve problems found in service development and operations. In the following section, an empirical case study is provided to verify the viability of using TRIZ to solve service problems. A study was conducted on restructuring the service operations of a university canteen. Several TRIZ tools, such as the problem formulator, root contradiction analysis and the 40 inventive principles, were applied with certain modifications. These were used to generate conceptual solutions to address the
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Table 1. Common Contradictions in Service Industries Service Contradiction
Description
Diversity versus Focus
Services targeted for mass market cater to the needs of a wide range of customers, but result in undifferentiated services. However, services targeted for a niche market cater for a certain segment of customer profile. They are not good for expanding market share by widening the range of customer needs. Customizing service offerings according to the preferences of customer needs can attract and retain customers from a wide range. However, this will reversely lower the speed of service delivery. Delivery efficiency is one of the most important dimensions in measuring service quality. Multi-functional e-services are powerful in problem solving for e-customers, but it increases the load of customers to figure out the usage of e-services. General (guideline) information gives users general ideas and save them time in searching. But they do not tell the full story. Specific, concrete information is informative but less focused, simplified and difficult to browse. Secure service ensures the safety of transaction and privacy of customer information, whereas customers are not well informed of the operation process of transaction. This lowers the trust of customers, and reduces further usage of the secure service. However, making the operation process transparent will reversely enhance the risk of losing confidentiality. Customers receive convenient and swift online services (e.g. e-banking transaction, funds transfer). However, this self-service involves much less human interaction and naturally reduces customer loyalty, which is usually established by ‘tangible’ service (e.g. brick-and-mortar bank branches).
Customization versus Standardization
Functionality versus Ease of use
General information versus Detailed information
Security/Privacy versus Transparency
Industrialization versus Personalization
identified problems in the operation of the canteen system.
Case Study The Techno Edge canteen is one of three major university canteens surveyed in this study. Typically, the food outlets in the canteen operate from 8.30 am to 6.30 pm on weekdays, and from 8.30 am to 2:00 pm on Saturdays. The canteen is closed on Sundays and public holidays. Since it is not convenient to purchase food elsewhere outside of the operation hours of the canteen, some students have requested an extension of the hours of operation. This solution, however, may not be welcomed by
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the food operators for reasons of cost ineffectiveness. To capture the situation information, several questions from the innovative situation questionnaire were selected and modified (see Terninko, Zusman & Zlotin, 1998, for the original questions). These questions were used in our interviews with the food-outlet operators, operations staff and consumers. Some example questions are as follows: 1. What might be the possible solution(s) to the canteen operation hours problem? 2. What are the advantages and disadvantages of these solutions? 3. What might be the ideal solution to address the operation hours problem?
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4. What are the local constraints to implement the solutions and resolve the operation hours problem? Through an investigation of the canteen operations and interaction with the operations staff, information was obtained, structured and processed using TRIZ’s tools and methods. The objective of the case study was to find effective measures to improve the operation of the canteen so that the dining needs of all customers can be met. The centre of the operations system is the targeted canteen, which includes all of the physical facilities, operators and other resources. The super-system over the canteen operations system is the entire university. To address this problem, the ideal solution should be able to eliminate all of the existing problems on both sides (both the customers and canteen operators) at as little cost as possible. After gathering the interview information, the problem formulator was used to analyse the problem (see Terninko, Zusman & Zlotin, 1998). Based on the rules of using the problem formulator, a set of events was extracted. The events were linked to each other as shown in Figure 1. A total of 11 problem statements were formulated (see Table 2). With analysis of the abstract problem statements, a number of possible solutions can be interpreted within the context of canteen operations. In addition to the use of the problem formulator, the problems found in this case study could also be analysed with the use of contradiction analysis. It is not difficult to identify the two conflicting aspects in the original sys-
tem, which are the operating hours of the food outlets and customer demand. Thus the contradiction can be structured as ‘the operation time should be long enough in order to meet the dining needs of students and staff’. However, operation time should not be too long, because it is not cost-effective for the food-outlet operators. The essence of eliminating this contradiction is to take effective measures to either stretch the operation time or to condense/concentrate the demand of the customers into a shorter time period. Based on extreme situation analysis and combined with the 4 separation principles and 40 inventive principles, a number of solutions are proposed to eliminate the contradiction. They include: 1. Separation in space. Separate food preparation from food supply by contracting food preparation to off-campus operators and using phone ordering, direct delivery or other means to supply food in batches to campus. 2. Separation within a whole and its parts, and segmentation. Segment the dinning needs of customers into different types and patterns. Categorize those who have particular needs such as late dining, and provide a special service for them. 3. Separation in time. Divide the operation hours of the food outlets into two parts (i.e. daytime operation and night-time operation) so that different operators can use the existing outlets for night-time operation. 4. Self-service. Using a deliver-on-order service, customers can collect the ordered food by themselves at designated collection points. Office pantries can be provided such
UF: Useful Function HF: Harmful Function
UF Food outlets run by contract operators
UF is required to
Provide fresh cooked food
UF is required to
Meet the dining needs of students and staff
causes HF
Limitation on operation time causes
HF
influences
Late comers fail to buy food
Figure 1. Functional Diagram of the Problem of Food Outlet Operation
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Table 2. Interpretation of the Formulated Problem Statements Problem Statement
Practical Indication
1) Find an alternative way to obtain the useful function of [Meet the dining needs of students and staff] that does not require [Fresh cooked food] and is not influenced by [Later comers fail to buy food]. 2) Find a way to enhance the effectiveness of [Meet the dining needs of students and staff]. 3) Find an alternative way to obtain the useful function of [Fresh cooked food] that can provide [Meet the dining needs of students and staff] and does not require [Food outlets run by contracted operators]. 4) Find a way to enhance the effectiveness of [Fresh cooked food]. 5) Find an alternative way to obtain the useful function of [Food outlets run by contracted operators] that provides [Fresh cooked food] and does not cause [Limitation on operation time]. 6) Find a way to enhance the effectiveness of [Food outlets run by contracted operators]. 7) Find a way to resolve the contradiction that [Food outlets run by contracted operators] should be established in order to provide [Fresh cooked food], but it should not be established in order to avoid causing [Limitation on operation time]. 8) Find a way to eliminate, reduce, or prevent the harmful function of [Limitation on operation time] in order to avoid causing [Later comers fail to buy food] under the condition of [Food outlets run by contracted operators]. 9) Find a way to benefit from [Limitation on operation time]. 10) Find a way to eliminate, reduce, or prevent the harmful function of [Later comers fail to buy food] in order to avoid influencing [Meet the dining needs of students and staffs] under the condition of [Limitation on operation time]. 11) Find a way to benefit from [Later comers fail to buy food].
Find alternative ways of delivering food or replacements of fresh cooked food so that people can come and buy at anytime. Examples: Direct sale of ordered food, automated vending machine, canned food, provide office pantry so that students can cook or heat own food, etc.
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Find alternative ways to obtain fresh cooked food without relying on contracted operator. Example: Contract with off-campus operators who can operate without time constraint.
Find alternative ways to operate existing food outlets without time constraint. Example: Encourage a few existing operators to extend operation time.
Find alternative ways to serve customers without time constraint. Example: New packaging way to sustain the freshness of food so that operators can first cook food on order, and then deliver to designated places where customer can collect the food.
Find some means to change the customer demand cycle. Example: Early bird discount.
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that late diners can self-resolve the problem by cooking or heating their own food. 5. Preliminary action. Set up complementary measures, such as providing food-vending machines and/or pantries to relieve peakhour demand and also meet the needs of late comers. As demonstrated in the process of problem solving, a number of possible solutions were generated to address the canteen operation hours problem through using TRIZ tools. With the evaluation from a customer workshop, solutions such as outsourcing food supply or setting up new outlets for night operation were considered feasible for implementation. The implementation of these ideas required the support of the university. Another solution is to launch a new food-ordering service on campus. Students and staff can order their food via the telephone or Internet. The food can be prepared using contracted operators, who can be either from the existing canteens or from off-campus restaurants. The ordered food can be delivered to designated collection points, or delivered directly to offices. A service fee would be charged.
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the problem situation, revealed the inherent contradiction and generated multiple quality solutions in a systematic manner. Moreover, during the problem-solving process, ideas were generated throughout the entire conceptual design process. Such a process allows service designers to stop as soon as satisfactory solutions are found, thus saving time and cost. The implementation of this new method in NSD may be beneficial to service companies in several ways. First, as a formalized approach, this method at least fills in a void of previously unsystematic practices of some companies in developing new services. Second, it has the potential to shorten development cycle time which may in turn lead to cost savings and an overall shorter time-tomarket. Third, having a powerful knowledge base that consists of a collection of innovative patterns, TRIZ can help practitioners to develop new services in the first place by avoiding the need to ‘reinvent the wheel’. In fact, service organizations can further enhance the effectiveness of the knowledge base by collecting the best service innovation examples across different industries.
Discussion and Implications
Conclusion
With a powerful knowledge base as its foundation, TRIZ contrasts with other problemsolving methodologies through its unique way of delivering quality and innovative solutions without compromise. The effectiveness and efficiency of using TRIZ in technical problem solving have been proven through over four decades of practice. Non-technical problem solving, as a new area in which to apply TRIZ methodologies, is receiving increasing attention. Service product design is a promising avenue with much potential to benefit from their integration with TRIZ. Taking this as an objective, this research project applied modification of selected TRIZ tools, such as the problem formulator and the 40 inventive principles, to resolve problems in service operations. Its successful application in this study confirms the premise that the TRIZ knowledge base is applicable to a wide scope of problemsolving situations. The success of using TRIZ in service operations also contributes to the literature on NSD. Unlike existing service design tools, a TRIZintegrated service conceptual approach can help service developers to overcome their psychological inertia and generate many quality and potentially breakthrough ideas throughout the process of service conceptualization. In the present case study of restructuring canteen operations, the use of TRIZ tools redefined
This paper proposed a new method of service conceptual design based on the TRIZ methodology. We argue that with appropriate modification, TRIZ tools can be applied to service problem solving. An empirical case example validated the use of TRIZ tools such as the problem formulator, contradiction analysis and the 40 inventive principles, in resolving problems in service operations. Although at this stage the classical knowledge base of TRIZ may not be able to reflect all the distinct innovation patterns found in services, we believe that the effectiveness of using TRIZ in the service domain can be further enhanced through the incorporation of best practices knowledge. The implementation of this approach should be able to address the existing gaps in service development, and have a significant impact on the industrial practice of new service development.
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References Altshuller, G.S. (1984) Creativity as an Exact Science: The Theory of the Solution Inventive Problems. Gordon & Breach Science Publishing, New York. Altshuller, G.S. (1997), 40 Principles: TRIZ keys to technical innovation, trans. and ed. Shulyak, L., Rodman, S. Technical Innovation Center, Worcester, MA.
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Berkeley, B.J. (1996) Designing services with function analysis. Hospitality Research Journal, 20(1), 73–100. Berry, L.L. and Lampo, S.K. (2000) Teaching an old service new tricks. Journal of Service Research, 2(3), 265–75. Bowers, M.R. (1989) Developing new services: Improving the process makes it better. The Journal of Service Marketing, 3(1), 15–20. Clausing, D.P. (2001) The Role of TRIZ in Technology Development. TRIZ Journal, August. Congram, C. and Epelman, M. (1995) How to describe your service: An invitation to the structured analysis and design technique. International Journal of Service Industry Management, 6(2), 6–23. Cooper, R.G. (2001) Winning at new products: accelerating the process from idea to launch. Perseus Publishing, Cambridge, MA. Domb, E. (1998) QFD and TIPS/TRIZ. TRIZ Journal, June. Easingwood, C.J. (1986) New product development for service companies. Journal of Product Innovation Management, 3(4), 264–75. Edgett, S.J. (1996) The new product development process for commercial financial services. Industrial Marketing Management, 25(6), 505–15. Edvardsson, B., Haglund, L. and Mattsson, J. (1995) Analysis, planning, improvisation and control in the development of new services. International Journal of Service Industry Management, 6(2), 24–35. Goldschmidt, G. (1997) Capturing indeterminism: representation in the design problem space. Design Studies, 18, 441–45. Griffin, A. (1997) PDMA research on new product development practices: Updating trends and benchmarking best practices. Journal of Product Innovation Management, 14, 429–58. Gummesson, E. (1989) Nine lessons on service quality. The Total Quality Management Magazine, 1(2), 82–90. Johnson, S.P., Menor, L.J., Roth, A.V. and Chase, R.B. (2000) A critical evaluation of the new service development process: Integrating service innovation and service design. In Fitzsimmons, J.A. and Fitzsimmons, M.J. (eds), New Service Development: Creating memorable experiences. Sage Publications, Thousand Oaks, CA. Johnston, R. (1999) Service operations management: return to roots. International Journal of Operations & Production Management, 19(2), 104–24. Kelly, D. and Storey, C. (2000). New service development: initiation strategies. International Journal of Service Industry Management, 11(1), 45–62. Langeard, E., Reffiat, P. and Eiglier, P. (1986) Developing new services. In VenKantesan, M., Schmalensee, D.M. and Marshall, C. (eds), Creativity in services marketing: What’s new, what works, what’s developing. American Marketing Association, Chicago, IL. Low, M.K., Trond L., Kathryn W. and Odd, M. (2001) Manufacturing a green service: Engaging the TRIZ model of innovation. IEEE Transactions on Electronic Packaging Manufacturing, 24(1). Mann, D., and Dewulf, S. (2002) Evolving the World’s Systematic Creativity Methods. TRIZ Journal, April.
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Mann, D. and Domb, E. (2001) Using TRIZ to Overcome Business Contradictions: Profitable E-Commerce. TRIZ Journal, April. Marsh, D., Waters, F. and Mann, D. (2002) Using TRIZ to Resolve Educational Delivery Conflicts Inherent to Expelled Students in Pennsylvania. TRIZ Journal, November. Martin, C.R. and Horne, D.A. (1993) Services innovation: Successful versus unsuccessful firms. International Journal of Service Industry Management, 4(1), 49–65. Menor, L., Tatikonda, M.V. and Sampson, S.E. (2002) New service development: area for exploitation and exploration. Journal of Operations Management, 20, 135–57. Metters, R., King-Metters, K. and Pullman, M. (2003) Successful service operations management. Thompson South-Western, Mason, OH. Rantanen, K. and Domb, E. (2002) Simplified TRIZ: New problem-solving applications for engineers and manufacturing professionals. St. Lucie Press, Boca Raton, FL. Rivin, E.I. (1998) Use of the Theory of Inventive Problem Solving (TRIZ) in Design Curriculum. TRIZ Journal, February. Ruchti, B. and Livotov, P. (2001) TRIZ-based Innovation Principles and a Process for Problem Solving in Business and Management. TRIZ Journal, December. Savransky, S.D. (2000) Engineering of creativity: introduction to TRIZ methodology of inventive problem solving. CRC Press, Boca Raton, FL. Schlueter, M. (2001) QFD by TRIZ. TRIZ Journal, June. Schweizer, T. (2002) Integrating TRIZ into the Curriculum: An Educational Imperative. TRIZ Journal, November. Sheuing, E. and Johnson, E. (1989) A proposed model for new service development. Journal of Services Marketing, 3(2), 25–34. Shostack, L. (1984) Designing services that deliver. Harvard Business Review, January–February, 133– 39. Tennant, G. (2003) TRIZ for Six Sigma. Mulbury Six Sigma, available at: http://www.sixsigmatriz.com/TRIZ_ebook.htm. Terninko, J. (1998) The QFD, TRIZ and Taguchi Connection: Customer-Driven Robust Innovation. TRIZ Journal, January. Terninko, J., Zusman, A. and Zlotin, B. (1998) Systematic innovation: An introduction to TRIZ. St. Lucie Press, Boca Raton, FL. Ungvari, S.F. (1999) Product Differentiation Strategies Incorporating TRIZ Methodology. TRIZ Journal, May. Verduyn, D. (2002) Integrating Innovation into Design for Six Sigma. TRIZ Journal, February. Vincent, J.F. and Mann, D. (2000) TRIZ in Biology teaching. TRIZ Journal, September. Zeithaml, V.A. and Bitner, M.J. (2000) Services marketing: integrating customer focus across the firm. Irwin/McGraw Hill, Boston, MA. Zhang, J., Chai, K.H. and Tan, K.C. (2003) 40 Inventive Principles with Applications in Service Operations Management. TRIZ Journal, December.
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Jun Zhang (
[email protected]) is an engineer working in the semiconductor industry in Singapore. He received his MEng degree in Industrial and Systems Engineering from the National University of Singapore (2004), and his BEng degree in Mechanical Engineering from the Northwestern Polytechnic University, China (2001). Kah-Hin Chai (
[email protected]) is an Assistant Professor in the Department of Industrial and Systems Engineering at the National University of Singapore. He received his PhD degree from Cambridge University (2000) in the area of manufacturing management. His current research interests are new product development, service innovation and knowledge management. Kay-Chuan Tan (
[email protected]) is an Associate Professor in the Department of Industrial and Systems Engineering at the National University of Singapore (NUS). He is also Deputy Director of the Office of Quality Management, NUS, where he is involved in the setting-up of benchmarks for quality excellence in education as well as the dayto-day quality management of NUS. His current research interests include: advancement of quality function deployment, assessment of national quality awards, service quality assessment and use of quality indicators in information technology systems.
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESTHE TRIZ RESOURCE ANALYSIS TOOLCREATIVITY AND INNOVATION MANAGEMENT
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The TRIZ Resource Analysis Tool for Solving Management Tasks: Previous Classifications and their Modification Sandra Mueller This paper illustrates different approaches of classifying resources inside the field of TRIZ and in the strategic management with focus on the Resource-based View. The Resource-based View is introduced in order to discuss resources under management aspects. The goal is to integrate the most promising approaches for increasing the effectiveness of the TRIZ-based resource analysis. The TRIZ-based comprehension of resources will be broadened such that the specifications of management problems are sufficiently considered. Based on the proposed classification, a three-step process to analyse resources in well-structured form based on TRIZ is recommended. In that way, the problem-solver can identify resources that might not normally be viewed as such. Furthermore, the proposed classification includes different categories of resources, with examples in the field of management that can be used as a database.
Introduction
ompanies have to face several kinds of management problems. In this context, management is defined as an activity of organizing and contains aspects such as planning, controlling, and organization, as well as personal aspects such as leadership (Staehle, 1999). Problems arise from all these areas, and are mainly characterized as management problems. In this context the ‘Theory of Inventive Problem Solving’ (Altshuller, 1984) becomes more popular, because many problems cannot be solved by known solving methods or techniques. Several experts feel confident about the application of TRIZ to management problems (Pannenbaecker, 2001). The transfer of TRIZ to the field of management is referred to as ‘Management-TRIZ’. TRIZ is based on consolidated findings from extensive research of patents, and offers users the compressed knowledge and experiences of former inventors. In the evolution of TRIZ, a collection of tools has been designed that can be either used separately or in combination with others. The toolkit offers some technical
C
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knowledge-based tools that provide recommendations for system transformation and as well as analytic tools for defining, formulating and modelling (Zlotin, Zusman & Kaplan, 1999). Resource analysis is an analytic tool to direct the user’s creativity towards possible resources in the problem situation. The first basic idea was to apply TRIZ tools through direct analogy to non-technical problems. Even if analytic tools such as resources can be applied easier to any kind of problems than, for example, scientific effects and phenomena, it seems to require some modifications. Orloff (2002) has pointed out that the human as a problem-solver plays a central role, so the success of a problem solution depends on two resources: (i) the resources of the problem; (ii) the resources of the problem-solver. For management problems, it is necessary to go even further. Within a management problem, the human being, with individual
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characteristics and its own personality, plays an important role. Socio-technical systems consist of human and technical elements, while social systems only contain human elements. Both systems are different from other systems. They are strongly influenced by moral values, which have an impact on human behaviour. As a consequence, a system culture emerges including expectations of behaviour and roles. Sometimes the company’s set of objectives is different from the employee’s aims, wherefore a formal and an informal organization can emerge, and conflicts may arise. Technical systems and the appearing problems can easily be divided into system elements. Social or sociotechnical systems mainly consist of lessstructured problems. Division of system elements is much more complex for this kind of problems (Hill, Fehlbaum & Ulrich, 1994). From a system perspective, a company can be seen as a dynamic, open socio-technical system that consists of social and technical elements, as well as relationships between them and the company environment (Staehle, 1999). In this system, the work factor is generated by humans – employees. The employees are a work factor for a company, a bearer of motivation and decisions, a cost-causer and a coalition partner, if they belong to different groupings. Furthermore, every company is confronted with different social groupings from the environment. All in all, the complexity of systems decreases considerably by combination of technical and social elements and the manifold relationships between them. This applies increasingly to social systems, from which various management problems could arise. The search for resources is the basis for every system development. Resources are necessary and sufficient to realize the postulated system attributes and they may solve problems (Orloff, 2002). It is never trivial to think about the use of resources, because their availability and configuration could be changed in the course of time. The resources in TRIZ are based on a wider understanding compared to other resource definitions. They are regarded as available funds of every description in and around a system, which are not used to their maximum for forming a system, e.g. substances or materials, as well as fields such gravitation (Mann, 2002; Pannenbaecker, 2001). The TRIZ-based resource analysis with a unique resource understanding is a powerful tool to discover resources for solving problems, but the focus so far has been on technical and techno-economical problems. In company practice, the term resources is often used, although in theory, alternative definitions and different classifications within and
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outside the field of TRIZ, as well as alternative uses, can be found. Resources and their strategic role have been discussed copiously in the field of management in the context of strategic competition in particular, as well as in mergers and acquisitions. The questions arise, what kind of resources can be applied to solve management problems, and how could the problem-solver systematically identify resources in the field of management? An analysis of resource-based research in strategic management can give an indication for classifying resources for solving management tasks, because company-internal resources are at the centre of attention in the so-called Resource-based View. In this paper, first the basic resource understanding and the different approaches in the field of TRIZ and in the Resource-based View are introduced. Next, classifications of resources within and outside the field of TRIZ are briefly pointed out, to analyse the availability and applicability for solving management problems. Both resource understandings are finally merged to develop a classification of resources that is suitable for management problems. This proposed classification is supplemented with a recommendation to analyse resources systematically in three steps. Furthermore, lists of example resources are given for further support.
Resources in TRIZ The analysis tool as a part of the TRIZ-based toolkit can be defined as a special technique for directing creativity. This tool is principally directly applicable to every kind of problem situation (Moehrle, 2003). By drawing attention to all resources currently used and those additionally available and by listing them completely, the analysis tool itself can offer possible solutions to the problem-solver (Pannenbaecker, 2001). In order to solve a problem the problem-solver always reflects on resources. Specific to the analysis tool is the fact that thinking is an aware process. Implicit knowledge is transformed into explicit knowledge. For externalization, analogies are often used to enlarge implicit knowledge to explicit issues. This is because implicit knowledge is often difficult to communicate and formalize (Nonaka & Takeuchi, 1995; Polanyi, 1985, 1982). The externalization also occurs if individuals exchange knowledge in a group, when a constructive discussion can be useful. The analysis tool supports the process of externalization. Hidden implicit knowledge is externalized into visible explicit knowledge. The resource ‘thought’ aims at the maximization of every system element. In TRIZ terms,
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a resource is anything in and around the system that is not being used to its maximum potential (Mann, 2002). One of the key findings of TRIZ research was that the strongest solutions transform the unwanted or harmful elements of a system into useful resources. Thus, TRIZ requires the consideration of all resources within and outside the system. This refers to traditionally positive resources as well as to negative resources. Both kinds of resource might lead to discovery of problemsolving opportunities. All available resources describe the scope of design for transforming the present state to the target state (Pannenbäcker, 2001). A problem-solver may identify the resources principally using brainstorming, or within TRIZ, systematically with supporting techniques as well as based on other TRIZ tools such as system analysis or substancefield-analysis. A frequently used and established technique to support the analysis resource is the classification of resources into several categories. Through the classification, the problem-solver should be able to collect resources more easily, because it helps him to think more effectively. Within the latest developments of TRIZ, several authors have introduced classifications of resources. The classification mainly aims at structuring technical or techno-economical problems. A standard classification has not been accepted yet. In the following, five TRIZbased approaches of classifying resources will be described briefly: • Terninko, Zusman and Zlotin (1998), and Pannenbaecker (2001); • Orloff (2002); • Pevzner, Kasymov and Savransky (1992– 1996); • Mann (2002); • Ideation International (2003).
Terninko, Zusman and Zlotin, and Pannenbaecker Terninko, Zusman and Zlotin (1998) and Pannenbaecker (2001) divide resources in the six categories; (i) substances, (ii) fields, (iii) functional, (iv) informational, (v) time and (vi) spatial. (i)
Substances are any materials of which the system and its surroundings are composed. Readily available resources include raw materials or semi-finished products, as well as waste or absence of a substance. (ii) Fields are any kind of energies inside or around a system, e.g. gravitation, light or electromagnetic radiation.
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(iii) Functional resources are any kind of effects. They include the capability of a system or its surroundings to perform additional functions. A super-effect is an additional (unexpected) benefit that arises as a result of innovation, e.g. producing heat from cow dung (methane). (iv) Informational resources are any perceptible information. Additional information about the system could be extracted from existing fields in a system. For example, the crankshaft transports not only strength, but also information, e.g. the speed of pressing together. (v) Time resources are any kind of time including time intervals before, during and after a process, e.g. use of online computer access. (vi) Spatial resources are free, unused space in a system or in its environment, e.g. use of the interior of spare wheels in a passenger car.
Orloff Orloff (2002) defines an alternative classification of resources. Instead of substances, Orloff uses ‘substantial’ in terms of material properties such as chemical composition and the term energy instead of fields. Furthermore, he supplements the categories ‘structural resources’ and ‘system resources’ in terms of general system properties such as efficiency. Orloff differentiates between two main categories of resources: system-technical and physical-technical. System-technical resources are abstract, while physical-technical resources are easier to recognize in a system. He emphasizes that system-technical resources are always realized on the base of physical-technical resources. He subordinates the several types of resources in two main categories: The category system-technical resources consists of the resources (i) system, (ii) information, (iii) functional and (iv) structure. The resources (i) time, (ii) spatial, (iii) substantial and (iv) energy belong to the category physical-technical resources.
Pevzner, Kasymov and Savransky A further approach that has to be mentioned offers a classification scheme consisting of the nine categories (i) energy, (ii) matter, (iii) space, (iv) time, (v) informational, (vi) functional, (vii) composite (combined), (viii) environmental and (ix) from sub- or super-system, with the three sub-classes; (a) internal, (b)
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external and (c) common (Pevzner et al., 1992– 1996). The nine categories are not explicitly described. It is therefore difficult to differentiate between (viii) environmental and (ix) suband super-system. The class (a), internal, consists of things, substances and fields that are in the conflict area during or before a conflict time. The class (b), external, consists of things, substances and fields in neighborhood of the conflict area. The class (c), common, contains air, water or gravitation and so on. Overlaps can be interpreted between the categories and the classes, e.g. between the category (viii), environmental, and the class (b), external, or between the category (i), energy, as well as (ii), matter, and the class (c), common.
Mann Another current approach is the classification of resources in the six categories (i) environment, (ii) low-cost, that is, plentiful, (iii) material, (iv) transforming, that is, modifying, (v) manufacturing and (vi) associated with humans, by Mann (2002). In this approach, the resources of the categories (i) environment, (ii) low cost (plentiful) and (vi) associated with humans are broken down into the three subcategories (a) space, (b) time and (c) interface. Mann (2002) emphasizes the conceptual thinking in the dimensions (a) space, (b) time and (c) interface as an important part of TRIZ. He provides a considerable database of resource triggers to support a problem-solver. This leads to a new awareness of resources that are usually not taken into consideration such as the constitution of air or the human pulse variation. Mann’s approach considers resources associated with humans. In the context of space, time and interface, this category is not suitable for management problems. This results from the missing reflection of the human itself and its psychology. Each approach, taken separately, can be helpful to discover resources inside a technical or techno-economical system. For management systems, difficulties arise from the resource understanding. For example, substances are defined as any materials and fields are viewed as energy in terms of physical properties. Based on this definition, it is not problem-free to define substances and fields in social or socio-technical systems. There are intangible things as well, and a field can for instance be personnel communication and less physical energy. In addition, a category ‘from sub- or super-system’ should be handled carefully. With respect to a list of resources, a general attribution of resources fails with the special kind of system with
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respect to the point of view. The super-system of a company is different to a departmental environment. Nevertheless, it is helpful to keep a different system view at the back of one’s mind to identify resources in the system surroundings as well.
Ideation International With the aim of solving management problems, Ideation International (2003) provides a classification of resources in the software ‘Knowledge Wizard’. This software was developed notably for ‘human systems’. Human systems can be organizations, departments or teams. Resources are differentiated basically between four categories (i) financial, (ii) human, (iii) technical and (iv) other business assets such as equipment, facilities, inventory, information and other. Technical resources (iii) are divided into the six sub-categories (a) substances, (b) energy substances, (c) functional, (d) informational, (f) time and (g) spatial according to Terninko, Zusman and Zlotin (1998). This approach is an enhancement of the resource understanding in the context of management TRIZ. It can be seen as a step in the right direction, especially regarding the attempt to take human or financial resources into consideration. Nevertheless, it seems that this classification needs to be examined closely. Terninko, Zusman, Zlotin (1998) and Pannenbaecker (2001) defined this classification, which is also the basis of the software ‘Ideation WorkBench’ by Ideation International (2003). This classification is supplemented by the categories financial, human and other business assets, whereby the categories substances, fields -now energy substances-, functional, informational, time and spatial are only integrated into the category technical resources. As mentioned before, substances or energy substances are not clearly definable for management situations. It is questionable, whether energy substances like electromagnetic and magnetic fields can be assigned to management problems. Besides, this term ‘technical resources’ is not adaptable to management usage. All in all, the described approaches refer to technical or techno-economical problems and it can be helpful to solve them. In Table 1 the different TRIZ-based classifications of resources and their cohesions based on the given examples are summarized. The approach by Ideation International (2003), from the software ‘Knowledge Wizard’, is not included. It is a new approach related to management problems and therefore not directly comparable to the already introduced classical TRIZ-based approaches. For clarity reasons,
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Table 1. Classifications of TRIZ-based resources
Author
Terninko, Zusman, Zlotin (1998); Pannenbaecker (2001); Ideation International WorkBench (2003)
Orloff (2002)
TRIZ Basic understanding of the term resource: Positive as well as harmful things inside and around the system, which are not being used to their maximum potential Classifications of resources Substances (e.g. raw materials, products, waste, absence of a substance, system elements, substance flow) Fields (e.g. energy inside a system and from its environment, energy flow, energy loss, energy reserves) Functional (e.g. gaps in a function, application of harmful factors, exploit casual provided functions) Informational (e.g. inherent properties of the system and their changes, temporary information, information flow resp. information changes) Time (e.g. preliminary work, scheduled workflow, parallel process, pauses, temporary actions, rework) Spatial (e.g. free and unused space, other dimensions, for- and backside, vertical arrangement, nesting) System-technical: – System in terms of general system properties (e.g. efficiency,reliability) – Information (e.g. accuracy, completeness, methods of coding) – Functional (e.g. propose, auxiliary functions, harmful factors) – Structure (e.g. kind of structure like linear, parallel or closed) Physical-technical: – Time (e.g. frequency of occurrence, durability of time intervals, sequence) – Spatial (e.g. shape, height, hollows)
a b c d e f
– Substantial in terms of material properties (e.g. chemical composition, physical property) –Energy (e.g. mechanical, electromagnetic, gravitation) Energy Matter Space Pevzner, Kasymov, Savransky (1992-1996)
Time Informational Functional Composite (combined) Environmental From sub- or super-system With three classes: internal, external and common Environment, atmosphere (e.g. constitution of air, density) and in the context of space (e.g. mass of earth, river), of time (e.g. cycle of sun, speed of light) and of interface (e.g. sound attenuation, nitrogen cycle) Low-cost/plentiful in the context of space (e.g. rock, biomass), of time (e.g. shadow, resonance) and of interface (e.g. rain, wind)
Mann (2002)
Material (material family e.g. metals/alloys, polymers) Transforming/modifying in the context of space (e.g. asymmetry, bubbles), of time (e.g. constant or variable fields) and of interface (e.g. strong taste or odour) Manufacturing in the context of mechanical (e.g. conventional machine tools, forging) and of chemical (e.g. granulation, nitration) Associated with humans in the context of space (e.g. mass, height), of time (e.g. blink rate, pulse) and of interface (e.g. variation in temperature, sweat)
Legend: a Substances, material b Fields, energy c Functional
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d Informational e Time f Spatial
Directly belonging Indirectly belonging
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the table only represents important cohesions. Because of this, the reader’s attention is directed to the resources (i) substances (material), (ii) fields (energy), (iii) functional, (iv) time, (v) informational and (vi) spatial that appear directly and indirectly in the aforementioned approaches. Apart from these repeatedly arising resources, there are specific resources such as composite resources, and resources that arise sometimes such as environmental resources.
The Concept of Resources Inside the Field of Research on the ResourceBased View The role of resources is frequently discussed in strategic management. The major difference between resource understanding inside the field of TRIZ and inside the field of strategic management is the application of resources. The resources are consulted in strategic management to explain sustainable competitive advantages of companies, while within TRIZ resources are used to solve a problem. Furthermore, the TRIZ-based view has mainly been focused on a technical or techno-economical problem. The analysis of the resource-oriented research inside the field of management could provide incitements to solve social and sociotechnical problems. In the following, the basics of the resource-oriented research will be outlined briefly without going further into detail regarding the resource discussion. Attention should be drawn to the basic resource understanding and the classification of resources. It will be amplified on selected approaches. In the recent past, in particular in the AngloSaxon literature, a resource approach of a company, well-named as the ‘Resource-based View of a Firm’ (Penrose, 1959) – shortened to ‘Resource-based View’ – has entered strategic management. The basic questions are the development, the protection and the realization of resources. The Resource-based View shows various characteristics. To give a general survey of all different currents in the field of research is impossible. However, all partial approaches are based on the fundamental assumption that internally available resources of companies are the basis for sustainable competitive advantages and for long-term above-average profits. The Resource-based View describes a direct relationship between the resource position of a company and the realization of competitive advantages. Competitive advantages lead not implicitly to above-average profits. Companies are defined as a bundle of tangible and intangible
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resources. The heter-organic and specific configuration of a company results from its historic development (Bamberger & Wrona, 1996a, 1996b). Only a specific resource configuration causes sustainable competitive advantages and the base for above-average profits. For sustainability a resource must be valuable, in the sense that it exploits opportunities and/or neutralizes threats in a company’s environment, be rare among a company’s current and potential competition, be imperfectly imitable, and there cannot be strategically equivalent substitutes for resources that are valuable, but neither rare nor imperfectly imitable (Barney, 1991). Grant (1991) links resources for sustainable competition to four attributes: durability, transparency, transferability and replicability. In this context, Wernerfelt (1989) introduces the term ‘critical resource’. A resource is critical, if it can differentiate the company from competition by holding the greatest potential, for which a resource has to be unique. In the following, eight approaches to classifying resources based on the resource-based view will be briefly described: • • • • • • • • •
Penrose (1959); Wernerfelt (1984, 1989); Barney (1991); Grant (1991, 2002); Hall (1992); Prahalad and Hamel (1990); Sanchez and Heene (1996); Freiling (2003); Collis and Montgomery (1995).
Penrose The strategic discussion about the resources has a long tradition. Penrose (1959) had in first place introduced the idea in her publication, The Theory of the Growth of the Firm. She defines a company as a bundle of resources and marks the importance of the inherent characteristics of individuals and the relationships between them. She proposes a classification of resources into (i) physical and (ii) human. In her view physical resources of a company consist of tangible things such as plants or equipment. Available human resources are unskilled and skilled labour, such as administrative or managerial staff. Knowledge is important in order to identify the utilization of physical resources and to co-ordinate the resources and their changes to generate specific performances.
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Wernerfelt Following up the fundamental idea of the bundle of resources, Wernerfelt (1984) differentiates between tangible and intangible assets. In this view, the terms resources and assets are not differentiated; ‘By a resource is meant anything which could be thought of as a strength or weakness of a given firm’ (Wernerfelt, 1984). Resources include items such as brands, in-house knowledge of technology or machinery. During his scientific work, Wernerfelt has enhanced and revised his classification. Currently he distinguishes between (i) fixed assets, which are resources with fixed long-run capacity, (ii) blueprints, which are resources with practically unlimited capacity and (iii) cultures, which are resources with limited short-run, but unlimited long-run capacity (Wernerfelt, 1989).
Barney While Penrose (1959) emphasizes human resources and Wernerfelt (1989) accents the cultures, Barney (1991) introduces organizational resources as a stand-alone category, and widens the view on human resources. He classifies three categories (i) physical capital, such as plant or equipment, (ii) human capital, such as training or experience and (iii) organizational capital, such as formal reporting structure, as well as informal relations among groups within a company and between a company and those in its environment.
Grant Grant (1991) also emphasizes organizational resources. He divides the term resource into six categories: (i) financial, (ii) physical, (iii) human, (iv) technical, (v) organizational and (vi) reputation. Like Wernerfelt (1989), he revised and widened his view on resources as well as his classification scheme. Currently, he groups the three main categories as (i) tangible resources, including financial and physical, (ii) intangible resources, including technology, reputation and culture, and (iii) human resources, which includes specialized skills and know-how, capacity for communication and interactive abilities, as well as motivation. Grant (2002) distinguishes between resources and capabilities of a firm, because ‘resources must work together in order to create organizational capability’.
Hall Hall (1992) points out the capability differentials for sustainable competitive advantages as well, but in his judgement they only result
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from intangible resources. According to Hall (1992), intangible resources are (i) assets and (ii) competencies (skills). (i) Assets are broken down into; assets with a legal context for regulatory differential, and assets without a legal context for positional differential. Assets are people independent, except for the reputation and networks assets. In contrast, competencies are people dependent and are divided into know-how for functional differential and organizational culture for cultural differential. Although Hall refers to intangible resources, he lists owned physical resources as assets within a legal context.
Prahalad and Hamel Prahalad and Hamel (1990) suggested that the organizational resources have an intellectual dimension. Their approach is based on an application-oriented point of view. They introduce the term ‘core competencies’ as ‘the collective learning in the organization, especially how to coordinate diverse production skills and integrate multiple streams of technologies’ (Prahalad & Hamel, 1990). Recent developments show the transformation of the Resource-based View towards a knowledgebased view (Fried, 2003). Companies were seen as a ‘body of knowledge’ (MuellerStewens & Lechner, 2001) or as a ‘distributed knowledge system’ (Tsoukas, 1996). Altogether, various authors define the role of knowledge: sometimes knowledge is apprehended as a resource, in other cases the role of knowledge is emphasized, but it is not regarded as a resource itself (Table 2). Although tangible resources could make a company distinguishable from others, the Resource-based View directs much more importance to the intangible resources. It supports the view that employees and their knowledge are necessary to form singularity, that is, heterogeneity of a company. This applies accordingly to other intangible resources such as the organizational culture, the patents or the reputation of the particular company. Within the Resource-based View, the human factor, the human interactions and the interactions between organizations as well as the available knowledge created by humans come increasingly to the fore (Freiling, 2001, 2003; Hall, 1992). That is possibly one reason why the Resource-based View is accepted and triggers high interests inside the field of business economics.
Sanchez and Heene Competitive advantages are explained by the existence of internal resources. Sanchez and
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Heene (1996) have brought up the role of ‘firm-addressable resources’. These are a firm’s available assets and capabilities to perceive market opportunities. ‘Firm-addressable resources’ are classified in operations such as applications of existing capabilities for research and development, tangible or physical assets such as machines and intangible assets such as knowledge. Resource-based understanding becomes more realistic through this view, because it refers to increasing organizational networking.
Freiling A further conceptual development in this context is the market orientation, i.e. a company has to interact consistently with its environment to accommodate and develop resources further. ‘Firm-addressable resources’ could absorb gaps in the resource configuration (Freiling, 2003). The development considers not just customers in the sales market, but also stakeholders, who play an important role. External resources can make content and structural changes. If a company has less ideas or perspectives for example, it may contact persons and/or organizations in its environment. It will be a sustainable effect, if the company ensures the right of disposal of that knowledge, e.g. by long-term contracts.
Collis and Montgomery It can be deduced from the various partial approaches in the Resource-based View that there are different terms and classifications of resources. A widely accepted definition is in the publication of Montgomery (1995, quoted after American Heritage Dictionary): a resource is ‘something that can be used for support or help; an available supply that can be drawn on when needed’. Available resources can take a variety of forms. They can be physical, intangible or take the form of an organizational capability (Collis & Montgomery, 1995). According to Bamberger and Wrona (1996a, 1996b), internal resources can be defined by nearly all internal tangible and intangible goods, systems and processes, whereby Sanchez and Heene (1996) include available external resources as well. An accepted classification scheme consists of the categories physical or tangible, intangible and particularly financial, enhanced by organizational and human resources. Table 2 gives an overview of classifications of resources based on the Resource-based View. Direct and indirect cohesions between them are mapped. The attention is directed toward the resources (i) tangible (physical), (ii) human (human
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capital), (iii) organizational and (iv) intangible. For the sake of completeness, the earlier approaches by Wernerfelt (1984) and Grant (1991) are also listed. Apart from Wernerfelt, most authors, e.g. Barney (1991), focus only on a company’s positive assets and attributes (Montgomery, 1995). Montgomery (1995) regards this kind of resource as the crown jewels, because they are the minority in all companies. She demands the acknowledgement of ‘pedestrian’ resources, because ‘their common nature does not mean that their assembled presence has no consequence’ (Montgomery, 1995). It is also important to consider the resources that have a negative impact on the company. Both types of resources, pedestrian and negative resources, form the majority of resources in a company. This view links to TRIZ-based resource understanding. For solving management problems, it is neither useful to consider only positive resources, nor advisable to define special types of resources too narrow. The fact that there are various resources and their spectrum is wide has to be taken into account. A useful approach can therefore be a combination of the TRIZ-based resource understanding and comprehension on the Resource-based View.
Merging the Resource Understandings in the Field of TRIZ and in the Resource-Based View for Solving Management Problems On the one hand, the Resource-based View considers management specifications by listing organizational and human resources in particular. On the other hand, resources are in principle furthest defined in the TRIZ-based view, because negative resources are seen as a potent opportunity. However, taking a closer look at the classifications in the field of TRIZ, the technical orientation becomes obvious, despite the wide definition. A combination of both views seems beneficial regarding a useful differentiation of resources that support the problem-solver to list the dominated aspects of a management problem situation and its solution. The merging of the TRIZ-based resource understanding and the Resource-based View is reflected in three levels: (i) elementary, (ii) concrete and (iii) specific. Every level offers different categories of resources in ManagementTRIZ, which are characterized by different abstraction (Figure 1). The elementary resources are of a very abstract and general nature. They require a
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Table 2. Classifications of resources based on the Resource-Based View of a Firm Resource-based View of a Firm Basic understanding of the term resource: Resources -especially internal resources- are the source of sustained competition advantages Author Classifications of resources Penrose (1959)
Wernerfelt (1989)
a
Physical/tangible (plant, equipment, land, natural resources, raw materials, semi finished goods, waste products, by-product, unsold stocks of finished goods) Human (unskilled labor, clerical, administrative, financial, legal, technical and management staff) Fixed Assets – resources with fixed long-run capacity (plant, equipment, mining rights, employees with specific training, firm specific investments by suppliers or distributors) Blueprints – resources with practically unlimited capacity (patents, brands or reputations) Cultures – resources with limited short-run, but unlimited long-run capacity (team effects)
b c d
Physical capital (a firm’s physical technology, plant, equipment, geographic location and access to raw materials) Barney (1991)
Hall (1992)
Collis and Montgomery (1995) Sanchez, Heene (1996) Freiling (2001)
Human capital (training, experience, judgment, intelligence, relationships and insight individual managers and workers in a firm) Organizational capital (formal reporting structure, formal and informal planning, controlling, coordinate systems, informal relations among groups within a firm and between a firm and those in its environment) Intangible resources: Assets: – Assets within a legal context for regulatory differential (contracts, licenses, intellectual property, trade secrets and owned physical resources) – Assets without a legal context for positional differential (reputation, networks and databases) Competencies: – Know-how for functional differential (e.g. employee-, supplier-, distributor know-how) – Organizational culture for culture differential (e.g. perception of quality or service, ability to manage change) Physical (e.g. wire) Intangible (e.g. brands, technological know-how) Organizational capability (e.g. routines, processes, culture) “Firm addressable resources”: Intangible assets (capabilities, knowledge, reputation, property rights, relationships) Tangible/physical assets (machines, buildings) Operations (application of existing capabilities for developing, producing, marketing and distributing products) Firm’s specific tangible and intangible assets (no production factor) resp. “input goods” (procurable on markets) that are refined to firm’s characteristic Tangible: – Financial – Physical
Grant (2002)
Wernerfelt (1984) Grant (1991)
Intangible: – Technology – Reputation – Culture Human: – Specialized skills – Know-how – Capacity for communication – Interactive abilities – Motivation Reworked definitions Tangible and intangible (examples of resources (assets) generally: brands, in-house knowledge of technology, employment of skilled personnel, trade contracts, machinery, effi cient procedures, capital etc.) Financial, physical, human,technical, organizational, reputation Legend
a Tangible, physical b Human, human capital
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c Organizational d Intangible
Directly belonging Indirectly belonging
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Resources in Management-TRIZ Business
Management
•
Tangible assets
•
Planning and Controlling
•
Finances
•
Organization
•
General characteristics
•
Leadership
Substances
Human
Low cost
•
Humans
•
Physical
•
Associated with humans
•
Visual
•
Sensitive
•
Behavior
Fields Functional Informational Time Spatial
Concrete level (b)
Specific level (c)
Elementary level (a)
Figure 1. Three Levels of Resources in Management-TRIZ
high analogy, wherefore a three-step deductive process is suggested to support the problem-solver: (i)
First, the user can work on the elementary level with elementary resources. With a broader understanding, those elementary resources cover the whole spectrum of resources in Management-TRIZ. (ii) If this is not sufficient, the user can next go on to a more concrete level. This level consists of the business, management, human and low-cost resources. In relation to management tasks, they are more descriptive and concentrated. With brainstorming the user is able to identify system elements with a useful and harmful impact on the system as resources to solve the considered problem. (iii) Third, the user may go on to the specific level, in case he wants a deeper level of support in the process of uncovering resources. For each category of the concrete level, the specific level offers up to four characteristic sub-categories. These are recommendations. Of course, the user can go directly to the concrete or specific level and, if necessary, descend later to the elementary level. The suggested deductive process can be described as a house with a fundament (elementary level), a crossbeam (concrete level) and four supporting pillars (specific level). Together they form the base course for the roof, which represents the resources in Management-TRIZ resulting from the different categories of each level. Focusing on TRIZ, six common classical categories of resources defined by Terninko, Zusman and Zlotin (1998) as well as Pannenbaecker (2001), are used as elementary
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resources for solving management problems. They are principally transferable to management problems provided that their understanding would be widened in some parts. To adapt, the TRIZ resource analysis tool to management tasks, the resources substances, fields and information must be expanded conceptually: In classical TRIZ-based understanding, substances are any kind of lifeless material. In the following, substances are considered to be systemic. They are defined as lifeless and alive, because socio-technical systems consist of both: technical elements such products or computer and social elements such individual people or social groupings. A further fragmentation such as tangible and intangible would not be meaningful, because, for example, functions, fields or information would likewise be substances. They should be considered separately. In classical TRIZ-based understanding fields are any kind of energy. In the following, fields are interpreted as a relationship between a causer, who acts on the field, and a recipient of the fields. Causer and recipient interact reciprocally. Thus, fields are defined as interactions. They can be, for example, communication or motivation, because humans can be system elements (substances). In classical TRIZ-based understanding, informational resources are any kind of perceivable information. In the following, informational resources include explicit and implicit knowledge. Informational resources are fundamental for solving management problems, and can be interpreted as a property of a system as well as of its
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Table 3. Elementary resources in Management-TRIZ Resources in Management-TRIZ Elementary level Substances
Fields (Interactions)
Functional
Informational
Time
Spatial
System elements Elements of a system’s environment (lifeless e.g. products and alive e.g. humans) Between system elements within a system (e.g. communication) From a system’s environment (e.g. competition) Between systems (e.g. networking) Reserves (utilization of the full potential e.g. motivation) Loss (e.g. anger) Gaps in a function Application of harmful factors Exploit casual provided functions Additional useful functions Emitted/transmitted information from the system and/or its elements Inherent properties of the system and/or its elements including explicit and implicit knowledge (e.g. behavior or inherent information such a patent or curriculum vitae as information resources as well as efficiency, availability, transparency, shape) Temporary information (e.g. date) Information flow resp. information changes (e.g. reporting structure) Time before the process starts (e.g. preliminary work) Time during the process – Parallel work/process (e.g. project organization) – Pauses – Idling Post-process time (e.g. feedback) Scheduled work flow Temporary actions (e.g. projects) Speed (e.g. reduction of training) Duration (e.g. fixed-term contract) Unoccupied space – Space between elements (e.g. reduction, expansion) – Space inside elements (e.g. interleave of information) – Unoccupied surfaces of elements (e.g. notice board on a wall) Space occupied by unnecessary elements (e.g. office as storage room) Space available in another dimension (e.g. advancement) Another arrangement (e.g. another seating arrangement or different location) Foreside/backside (e.g. using backside of a package) Shape, surface (e.g. display)
elements. Explicit knowledge is of systematic structure and easily transferable. Implicit knowledge applies to problem solving know-how with a less visible structure and is used intuitively (Polanyi, 1985). Both can be organizational (e.g. formal and informal planning) and individual (e.g. publications or social competence).
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Inside the field of Management-TRIZ, spatial resources bear less meaning. Regarding human conflicts, their use seldom leads to innovative solutions. Nevertheless, they can be useful. As a result, they are marginal and range at last place. Table 3 shows the six categories of resources; (i) substances, (ii) fields, (iii) functional, (iv) informational, (v) time and
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Table 4. Business resources with sub-categories and examples in Management-TRIZ Resources in Management-TRIZ Concrete level
Business
Specific level Tangible assets
Finances
General characteristics
Fixed assets
Equipment (e.g. machinery, hard- and software), facilities, plant, land
Current assets
Inventory (e.g. raw materials, semi finished goods),
Assets
Investment, cash reserve, trade receivables, factoring
Capital
Equity capital, liabilities (loans, leasing, salaries and wages), financial incentives (e.g. social contributions, private use of company’s car)
Location, areas, type of products and services, size (e.g. turnover, market size, goodwill, physical value, competition position)
(vi) spatial with examples, which are termed as elementary resources. Every category of the concrete level is described by various categories on the specific level. For each category on the specific level, sub-categorie with diverse examples are listed. The included list of examples, which is expandable, in particular should help the problem-solver to identify easier resources in management. Each of the following tables gives an overview of the levels and their categories. The elementary resources can be recovered in the categories on the other two levels. For example, tangible assets or humans are substances in a socio-technical system. It has to be pointed out that several categories like finances are divided again into further subcategories as generic terms such as assets and capital, for more clarity. Business resources and used business capital are issue related, because they give a description of the tangible and financial assets. According to the fiscal management in this concept, business resources are divided into the sub-categories, tangible assets and finances (Table 4). Tangible assets contain both fixed assets and current assets. Finances enclose (financial) assets and capital (equity and loan capital). Both sub-categories, tangible assets and finances, are supplemented with the general characteristics of a company, because they are issue-related resources as well, but neither tangible assets nor finances. General characteristics can be a useful
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resource for example, when a company exploits its image or location for applying special employees. Because of the used management understanding, the management resources consist of management functions. They are defined as planning and controlling, organization and leadership. In this case each function can be divided again (Table 5). According to Steinmann and Schreyoegg (2000), planning can be broken down into strategic and operational planning. Knowledge becomes more and more important. It is already represented inside the Resource-based View. Therefore, organizational knowledge is assigned to management resources in the sub-category organization. In addition to organizational knowledge, the organization sub-category contains organization and environment as well as informal organization, according to Steinmann and Schreyoegg (2000). The interaction between a company and its environment, especially inter-organizational relationships and networks, gains in importance, and an informal organization opens up new paths. With respect to the specifications of management problems it makes sense, to choose the human resources as separate category (Table 6). On the one hand, humans can be an important resource by themselves. On the other hand, individual properties can be supported as well. Individual properties are termed as associated with humans. In this context, the sub-category associated with humans
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Table 5. Management resources with sub-categories and examples in Management-TRIZ Resources in Management-TRIZ Concrete level
Management
Specific level Planning and controlling
Organization
Strategic planning
Strategic options (e.g. diversification, internationalization), strength and weakness analysis, environmental analysis, corporate policy (e.g. philosophy, vision)
Operational planning
Budgeting, break-even analysis, project planning
Controlling
Coordination (e.g. target/actual comparison, variance analysis), control
Company operational structure
Analysis and synthesis of work (personnel, temporal, local), procedures (formal planning), information and communication structure
Company organization structure
Hierarchy, coordination system (positions, divisions), communication (reporting structure)
Organizational knowledge
Intellectual property rights
Patents, trademarks, brands, trade names, copy rights
Internal documents
Contracts, licenses, concessions, forecast studies, estimates, customer list
Other explicit know-how
Inventions, programs, databases, processes, methodologies, formulas, core competencies, simulation, training methods, in-house knowledge of technology or of special departments (e.g. R&D or Human Resources Development)
Global
Technical (e.g. technologies and their life cycle), political/legal (e.g. laws, national and international policy), socio-cultural (e.g. education system, social developments), ecological (e.g. water quality), macroeconomic (e.g. competition, economic growth)
Stakeholders
Customers, competitors, suppliers, stockholders, banks, trade unions, consume/industry councils, government, media
Interorganizational relationships and networks
Alliances, joint ventures, co-operations, creation of institutions
External effects
Reputation, image, publicity, sponsoring, customer loyalty
Culture
Perception of service, working atmosphere, form, informational relations, unspoken rules, rituals, subcultures, cultural change
Learning
Experiences (e.g. successful projects, well past crises and conflicts), capability to development, organizational changes
Political processes
Divergent interests, conflicts, exertion of power, oppositions
Organization and environment
Informal organization
Leadership
Of humans
Informing (e.g. of goals), communication, motivation, representation
Of the company
Deciding, leadership organization (managerial styles), representation of the company
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Table 6. Human resources with sub-categories and examples in Management-TRIZ Resources in Management-TRIZ Concrete level Human Specific level Humans
Associated with humans
Interface between internal and external
(Potential) employees, employees with a specific training, people at the top level/at a lower level, people who are able to promote and motivate, stakeholder, shareholder, mediators, broker, experts
External
Retired people, public people (e.g. politicians)
Characteristics (personality)
Capability to develop, motivation, aims, wishes, expectations, power of judgment, autonomy, moral values, intelligence, creativity, reliability, trust ability, loyalty, flexibility, handling of stress, stability, hardiness, sensory capacity, visual thinking, experience of life, health
Qualification concrete
Education, training, specific know-how (e.g. project management, technical competence, languages, intercultural competency)
Table 7. Low-cost resources with sub-categories and examples in Management-TRIZ Resources in Management-TRIZ Concrete level Low cost Specific level Physical
Unskilled employees, trainees, timber, biomass, natural fibers
Visual
Logo, product presentation, work clothes, sunlight intensity, color, water
Sensitive
Music, sound, smell, temperature, wind, humidity
Behavior
Imitation, kindness, listen, motivation, authority
contains personality and concrete qualifications, while Mann (2002) uses the term in the context of space, time and interface. All in all, human resources represent the personal assets of a company.
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The low-cost resources are the last and special category on the specific level (see Table 7). In particular, those resources should be considered that are not regarded as relevant in first place or, which are taken for granted as
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they are available for free (Pannenbaecker, 2001). Low-cost resources are plentiful or easily added elements inside and around the system, which are not already used to their maximum. They possess a high availability and low expenditure, which they may bring out separately or in combination. They provide useful solutions with little effort. For example, friendliness as an inexpensive resource may be useful to positively influence a dissatisfied client. While Mann (2002) defines such resources in the context of space, time and interface for solving technical problems, they are divided into physical, visual, sensitive and behaviour-oriented in the context of Management-TRIZ.
categories are still useful in supporting the problem-solver in the field of resource discovery. Because of their abstract nature, this concept supports and encourages the problemsolver to start thinking in abstract categories. Therefore, this concept reflects the TRIZ philosophy. Besides this, a conceptual expansion of this fundamental concept is necessary to allow the application of this tool to management tasks adequately. Up until now, the resource analysis is only applied to technical and techno-economical problems. Therefore, a reorientation is required. The proposed classification and the three-step procedure are designed to offer a way to enrich the resource analysis and to show one option for solving management problems. The careful application of the resource analysis leads to:
Conclusion
new, unfamiliar thinking directions; careful preparation of problem-solving space; a number of starting points for creative solutions, as well as a combination of ideas.
Resources play an important role in the field of TRIZ as well as in the field of strategic management. Within TRIZ, the analytic tool resource analysis supports an inventor in solving predominantly technical or techno-economical problems. The Resource-based View is one theory to explain strategic management activities. In this approach, resources are defined as means for explaining sustainable competitive advantages of a company. One of the cornerstones of the TRIZ philosophy is that the search for resources takes into account negative as well as traditional positive resources in a system. The TRIZ-based resource understanding is very broad. Nevertheless, for an adaptation of the TRIZ-based resource analysis to management tasks, the specification of management problems are not considered sufficiently. The Resource-based View is discussed in this article, because particularly human and organizational resources are introduced, and a survey about the basic resource understandings and the fundamental approaches of classifying resources given. The different classifications of resources within and outside the field of TRIZ and their combination lead to a new classification of resources suitable for solving management problems. This classification is supplemented with a three-step process consisting of the elementary, concrete and specific levels, which may help the problem-solver to discover resources more easily. The adaptation of resources can help in various different management problem situations. The proposed classification of resources is built on the TRIZ-based resource concept of six categories (substances, fields, functional, informational, time and spatial) by Terninko, Zusman and Zlotin (1998), as well as Pannenbaecker (2001). These often-applied, abstract
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Acknowledgement The author would like to thank Darrell Mann for helpful discussions and support. Thanks also to Simon Dewulf, who had enabled the author to stay for a research sojourn at CREAX.
References Altshuller, G.S. (1984) Creativity as an Exact Science. Gordon & Breach Science Publishers, New York. Bamberger, I. and Wrona, T. (1996a) Der Ressourcenansatz und seine Bedeutung für die Strategische Unternehmensführung. Zeitschrift für betriebswirtschaftliche Forschung (zfbf), 48(2), 130–53. Bamberger, I. and Wrona, T. (1996b) Der Ressourcenansatz im Rahmen des Strategischen Managements. Wirtschaftswissenschaftliches Studium (WiSt), 25(8), 386–91. Barney, J.B. (1991) Firm Resources and the Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. Collis, D.J. and Montgomery, C.A. (1995) Competing on Resources Strategy in the 1990s: How do you create and sustain a profitable strategy? Harvard Business Review, 73(4), 118–28. Freiling, J. (2001) Resource-based View und oekonomische Theorie: Grundlagen und Positionierung des Ressourcenansatzes Gabler, Wiesbaden. Freiling, J. (2003) Resource-based View der Unternehmung. Technische Universitaet Chemnitz, 1–29. Fried, A. (2003) Was erklaert die Resource-based view of the Firm? Anforderungen an einen ressourcentheoretischen Ansatz aus Sicht des Strategischen Managements. Technische Universitaet Chemnitz, 1–38.
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Grant, R.M. (2002) Contemporary Strategy Analysis: Concepts, Techniques, Applications. Blackwell Publishers, Malden, MA. Grant, R.M. (1991) The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation. California Management Review, 33(3), 114–35. Hall, R.H. (1992) The Strategic Analysis of Intangible Resources. Strategic Management Journal, 13(1), 135–44. Hill, W., Fehlbaum, R. and Ulrich, P. (1994) Organisationslehre 1: Ziele, Instrumente und Bedingungen der Organisation sozialer Systeme. Haupt, Bern. Ideation International (2003) Knowledge Wizard. Version 2.9.4. Software application Southfield, MI. Ideation International (2003) WorkBench. Version 2.8.0. Software application. Southfield, MI. Mann, D. (2002) Hands-on Systematic Innovation. CREAX, Ieper. Moehrle, M.G. (2003) Implementation of TRIZ tools in companies: Results of a cluster analysis. The R&D Management Conference, Manchester. Montgomery, C.A. (1995) Of Diamonds and Rust: A New Look at Resources. In Montgomery, C.A. (ed.), Resource-based and Evolutionary Theories of the Firm towards a Synthesis. Kluwer Academic Publishers, Boston, 251–68. Mueller-Stewens, G. and Lechner, C. (2003) Strategisches Management: Wie Strategische Initiativen zum Wandel fuehren. Der St. Galler General Management Navigator. Schaeffer-Poeschel, Stuttgart. Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company: How Japanese Companies Create Dynamics of Innovation. Oxford University Press, Oxford. Orloff, M.A. (2002) Grundlagen der klassischen TRIZ: Ein praktisches Lehrbuch des erfinderischen Denkens für Ingenieure. Springer, Berlin. Pannenbaecker, T. (2001) Methodisches Erfinden in Unternehmen: Bedarf, Konzept, Perspektiven für TRIZ-basierte Erfolge. Gabler, Wiesbaden. Penrose, E.T. (1959) The Theory of the Growth of the Firm. Basil Blackwell, Oxford. Polanyi, M. (1982) Personal Knowledge: Towards a post-critical Philosophy. The University of Chicago Press, Chicago. Polanyi, M. (1985) Implizites Wissen. Suhrkamp, Frankfurt-am-Main. Prahalad, C.K. and Hamel, G. (1990) The Core Competence of the Corporation. Harvard Business Review, 68(3), 79–91.
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Sanchez, R. and Heene, A. (1996) A Systems View of the Firm in Competence-based Competition. In Sanchez, R., Heene, A. and Thomas, H. (eds.), Dynamics of Competence-based Competition. Theory and Practice in the New Strategic Management. Elsevier Pergamon, Oxford, 39–62. Schreyoegg, G. (1999) Organisation: Grundlagen moderner Organisationsberatung mit Fallstudien. Gabler, Wiesbaden. Staehle, W.H. (1999) Management: Eine verhaltenswissenschaftliche Perspektive. Vahlen, München. Steinmann, H. and Schreyögg, G. (2000) Management: Grundlagen der Unternehmensfuehrung. Wiesbaden, Gabler. Terninko, Z., Zusman, A. and Zlotin, B. (1998) Systematic Innovation: An Introduction to TRIZ. St. Lucie Press, Boca Raton. TRIZ Experts (eds) (1996–2001) TRIZ Resources of a System: The Types of Resources in TRIZ. From materials of Pevzner, L.Ch.; Kasymov, A. and Savransky, S.D. 1992–1996 Tsoukas, H. (1996) The Firm as a Distributed Knowledge System: A Constructionist Approach. Strategic Management Journal. 17(Special Winter Issue), 11–25. Wernerfelt, B. (1989) From critical resources to corporate strategy. Journal of General Management, 14(3), 4–12. Wernerfelt, B. (1984) A Resource-based View of the Firm. Strategic Management Journal, 5(2), 171–80. Zlotin, B., Zusman, A. and Kaplan, L. (1999) TRIZ Beyond Technology: The Theory and practice of applying TRIZ to non-technical areas. Ideation International, Detroit. TRIZ-Journal, 6(1), 1–47.
Sandra Mueller works as Research Associate to Professor Moehrle at the University of Bremen (Germany) and holds a degree in economics. She conducts research in the field of TRIZ, specializing in the adaptation of TRIZ tools to management tasks and has experience with TRIZ workshops in companies. Further scientific interests are scenario management in the scope of future research and entrepreneurship. In 2003 she spent a research sojourn at CREAX (Ieper, Belgium). Email:
[email protected]
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESGOAL SETTING THROUGH CONTRADICTION ANALYSISCREATIVITY AND INNOVATION MANAGEMENT
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Goal Setting Through Contradiction Analysis in the Bionics-Oriented Construction Process Bernd Hill The goal setting of technical development tasks and the uncovering of contradictions at the level of invention form the conditions for problem-solving. By including bionics in the construction process, the technical designer and/or product developer receives a rich arsenal of efficient biological structures as a suggestion for the problem solving. The way in which living nature can be used to help in the search for solutions is demonstrated using the example of the thermal insulation of house fronts.
Introduction
T
he use of TRIZ develops systematic thought, directs the thought process in a systematic and goal-orientated manner and emphasizes the capacity for organized thought. Building on this theory for the solution of inventive tasks, the author had developed a nature-orientated innovation strategy (Hill, 1999), which contains significant elements of TRIZ and ARIZ in a further developed form, such as evolutionary laws for the determination of the evolutionary status of the selected representative embodying the state of technology, and the problem of contradiction. Comprehensive evaluation of patents by Altshuller confirms the theory that for all inventions, contradictions of a technical or physical nature must be overcome. According to Altshuller, requirements can not be fulfilled by known solutions and physical effects simultaneously, so that a technicalphysical law stands in the way of the solution required or sought. With the nature-oriented innovation strategy, contradictions in their functional requirements are solved by means of biological effective principles and structures. Although biological evolution only appears to have directness and does not pursue goals, developmental contradictions within the phylogenesis can be uncovered by the human capacity for recognition, the resolution of which is © Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
directed towards the optimal fulfilment of life functions and preservation of the species. If product solutions with similar functional requirements to those found in the natural world are now generated, then biological structures that have emerged through the effects of the evolutionary process on the basis of recognizable developmental contradictions are also of significance for problem-solving. In order to generate a product solution at the level of invention, it is necessary to work through the underlying contradictions in the existing technology. Having done so, it is necessary to determine similar developmental contradictions that have led to structure formation in the natural world. Contradiction analysis and the functional biological structure on which it is based form the start point for problem-solving.
Bionics-oriented construction Natural inventions, used for human technology, have often formed the start point for the solution of technical problems. Human inventiveness has also, in many cases, produced technical solutions that have turned out to have already existed in the natural world for millions of years. The cost and effort of development could be considerably reduced if designers could use living nature as a source of ideas in a systematic way.
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The natural world exhibits an immense richness of multi-functional, self-organizing, ecologically and economically effective structures. Analysis of natural systems can bring new perspectives to the solution of a technical problem and promote solutions that stand out in their use of external energy sources, minimal use of materials and energy or recycling-friendliness, for example. The honeycomb structure of bee’s nests, as an example, offers a perfect template for light, stable and pressure-resistant constructions for aircraft wings and honeycomb-core walls and doors. A famous recent example is the selfcleaning ‘Lotus effect’, derived from the wax papillae of the leaves of the lotus plant. This effect is to be used in dirt-repellent building facades, windows and car bodies. Velcro fasteners on shoes, bags, clothes and tarpaulins borrow from the biological model of the burr. Fin propellers, bulbous bows on ships and riblet coatings (artificial shark skin) as biological solutions help reduce fuel consumption and thereby reduce the environmental burden of polluting substances. Nature offers us a multitude of models for the solution of technical problems. The scientific discipline of bionics1 established for this purpose is a technology of the future. Nachtigall draws our attention to the fact that natural constructions often have model features around which technology can orientate itself. If the natural world were to be systematically investigated for such constructions, in some areas of technology, faster progress could be made and better, more reliable and more sophisticated instruments could be created (Nachtigall, 1986). Bionics, as a way of designing technology orientated towards the natural world, requires the application of specific ways of thinking and acting in its implementation. The formation of analogies is a basic method of bionics. The formation of analogies is a means of inference through the transfer of problems for which a solution is sought or systems which are to be developed onto an analogous solved problem or a system that has already been realized. The conclusion of the analogy occurs through the intellectual transfer of functional characteristics of the as yet unknown, unclearly formulated object sought (technical system as goal) to the characteristics of the analogous object (biological system as starting point) (Hill, 1999).
The functional characteristics of the analogous system and those of the intellectually anticipated object sought can be represented as a conjunctive link. So the characteristics that lie in A and those that lie in B both belong to the average set D. There is analogy (similarity) if at least one characteristic belongs to both the set of the analogous object and the set of the object sought. The comparison of characteristic sets includes all those characteristics that are significant for the technical structure to be realized. With the aid of the analogy method, similarly functioning systems from the natural world are analysed and their relevant structures or sub-structures abstracted in order to discover the underlying principle. The principle discovered in this way can, through variation and/or combination of structure elements, be used in a suitable technical solution on the basis of the requirements, conditions and wants which are to be fulfilled. To this extent, this means of proceeding forms the basis for the increase in knowledge
Figure 1. The Essence of Analogy Formation
1
Bionics as a scientific discipline is the systematic study of the technical implementation and application of constructions, processes and developmental principles of biological systems (Neumann, 1993).
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Figure 2. Conjunctive Link
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Abstraction step n
Effect/ Principle
Concretizing step 1
Concretizing step 2 Abstraction step 2
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methodical means/ representational form
procedure steps ① aim determination 1.1 determination of the state of the art and uncovering lack
Abstraction step 1
Concretizing step n
Analogy object: Biological system
prepared technical solution
Biological system analysis
Technical system synthesis
Figure 3. Bionic Thought and Action Process (Orientation Model)
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systems analysis - functional analysis - structural analysis
1 2 3 4
determination of effectiveness factors 1.2 determination of the evolution conditions
evolution condition table - catalogs: evolution regularities
evolution condition table still to be passed through evolution evolution trends regularities x x x x x x x x x
determination of effectiveness factors and first solutions 1.3 determination of effectiveness factors and formulation of the effectiveness functions
E = f (x1 … xn )
y1
1.4 list of the requirement matrix and selection of relevant contradictions
necessary to be able to shape the technology. This cognitive route, which leads from the living notion (biological system or sub-system) to the abstract idea (principle) and from there to practice (technical solution), is the route not just of cognition, but also that of the remodelling of reality. A bionic thought and action process as a general orientation model of bionicsorientated problem solving can be inferred from these insights. The bionic thought and action process provides important stages of abstraction and actualisation, in order to generate the basic effective principle from the actual biological system and then to transfer these across to the relevant technical solution. This process is a component of steps 2.2 to 2.4 of problemsolving in the strategy model of the bionicsorientated construction for systematic and goal-orientated goal-setting and problem solving (see Figure 4). In this model, goal setting is of the utmost importance. Bio-strategic means of orientation in the form of catalogues of laws of biological evolution are used for the derivation of inventive tasks. It is also not about producing as many variant solutions as possible, but rather that the requirements for the task are made so exaggerated that contradictions which can lead to inventiveness in problem solving become apparent. To obtain starting points for solutions, various classes of analogy in the form of basic functions are useful as catalogue pages for the triggering of associations. Using the strategy model as a means of proceeding, the thought process that should lead to an inventive solution is directed in a goaloriented manner. By proceeding in this way, the designer organizes and improves the effectiveness of his thoughts. Through the support-
designation
........
yn
x1
requirement matrix
xn
1.5 designation of the paradoxes demand
x1
x2 y
specify the ideal trend
p a r a d o x
formulation of the task of development with inventive goal
② solution identification 2.1 form change transfer Determination the contradicting basic functions demands of the underlying store seperate support basic functions balk connect carry
2.2 Uncovering relevant biological structures with same or similar operation characteristics 2.3 Compilation of relevant structures in a table and a derivative of first solutions (principle solutions) 2.4 Transmission of the determined solutions into a technical solution according to the requirements, conditions (economic, technicaltechnological, ecological, social…) 2.4.1 Varying and/or combining relevant characteristics
2.4.2 Evaluation of solution elements and/or technical variants
orientation model: biological basic functions
material energy information
catalog sheets
structure catalog: forming material body segment of a pumpkin seed sprout drives flight snake spreading 1 seed bowl of ribs by 2 from each muscle 3 other power 4
analogy formation for releasing… associations biol. structure
1. 2. 3.
Initial solution
Rotor blade for wind wheel
size number
gen. characteristics
A1 A2 A3 A4
remark possibilities B1 B2 B3 B4 B5 A1B1 A1B2 A1B3 A2B1
solution criteria variant I variant II variant III 4 3 5 4 2 2 costs 3 2 3 11 7 10
Insurance of operation
2.5 Elaboration of the technical solution technical solution
table of biological structure representations association table variation method: - variation characteristics - size - number - situation - form - material - surface - transaction type - kind of conclusion combination method - morphologic tablet - morphologic box
evaluation method - point evaluation - gradated evaluation organization method - organization rules - design principles - model method
Figure 4. Strategy Model for Goal Setting and Problem Solving in the Bionics-Oriented Construction Process
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ing use of catalogues of evolutionary laws and representations of biological structure, mental barriers can be successfully overcome, powers of imagination increased and creativity promoted.
Goal setting for the use of evolutionary laws and contradiction The creative transfer of the orientation function – technology using the directional analogy with the natural world – to current technological solutions (state of technology) makes it possible to a limited extent to view technology from the point of view of biological evolutionary laws. It is not about the direct transfer of these laws to the state of technology, but rather, about gaining stimuli for further development towards greater effectiveness and ecological efficiency. ‘The comparative analysis of biological and technical evolution has demonstrated the existence of many surprising analogies. We should not be surprised that these analogies can be traced back in part to the same evolutionary factors and laws’ (Reichel, 1984). Through the examination of analogy, the opportunity arises to transfer insights regarding heuristically useful laws that are abstractable, and thereby open to comparison with technology. This makes it possible to define the future direction of development of the technological system being developed and to arrive at promising directions for solutions. We always therefore start from a point that embodies the most developed state of technology. The heuristic exploitation of evolutionary laws characterizes the following representation. Evolutionary laws also serve to find factors affecting effectiveness from the points of view of manufacturer and user, to confirm the developmental goals from the evolutionary point of view and to discern rough initial starting points for solutions (Linde & Hill, 1993).
Figure 5. Examination of Evolutionary Status
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Factors affecting effectiveness are technicaleconomic parameters, such as material and energy consumption, transport-economy, environmental friendliness, user-friendliness, assembly time, efficiency, reliability and so on. These parameters should be considered from the points of view of both user and manufacturer. The basic aim is to raise the effectiveness of a system. This depends on the parameter xj described above. E = f (x1 , x 2 , x 3 ,K xn )
(1)
Since the effectiveness of the system being developed is to be increased in comparison with the current state of technology, the values of the parameters show an increase. E≠ = f (x1 ≠, x 2 ≠, x 3 ≠,K xn ≠)
(2)
Each effectiveness factor xj is in turn dependent on physical or geometric variables yk. x j ≠ = f (y k ≠ or y k Ø)
(3)
Using these physical or geometric parameters, contradictions between the requirements can be found from a table of requirements. Effectiveness factors are target values, which show positive increases and are directly connected to the directions of increase or decrease of the y system parameter. Functional requirements for problem solving are derived from the y system parameter. The functional requirements are assigned to the appropriate basic function (forming, transforming, storing, blocking, connecting, transferring of materials, energy and information). These provide the starting point for the determination of significant biological structures from the catalogues (see step 2.2 in the
Figure 6. Contradictions as core element of goal setting
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GOAL SETTING THROUGH CONTRADICTION ANALYSIS
strategy model). These insights are demonstrated below by means of an example.
Recognition, formulation and resolution of contradictions Contradictions are uncovered by the human capacity for recognition in both the natural world and in technology. Ultimately, it comes down to increasing the effectiveness of the system. The effectiveness of biological structures is understood as the inter-relationship between maximization of the ‘survival function’ and the related computed minimization of energy use and biomass, with the survival function being designated as a complete function and including necessary sub-functions of reproduction, feeding, defence, movement, nest or burrow-building, information capture, processing and transmission and so on. This state of affairs represents a cost-benefit relationship that consists of keeping the cost in materials and energy in the carrying out of life-functions with regard to autogenesis as low as possible. Evolution often moves in the direction of higher effectiveness and it can be characterized by the effectiveness factors mentioned above such as reliability, stability, speed, sensitivity, tear-resistance, spatial requirements, energy use, use of materials, the ability to regenerate warmth and so on. These ‘performance parameters’ of biological systems are implemented through efficient structures. Through the effect of the evolution process, these structures are always constructed as well as they need to be and generally perform multiple functions Æ principle of multi-functionality. For this reason, a single effectiveness factor is rarely fully optimized. There can never be an absolute optimum, since certain life functions can change as a result of changing environmental conditions or adaptation to new habitats. For this reason, biological systems seek a phylogenetic compromise within the framework of the actual conditions and the totality of the environmental demands placed upon them. For example, if a blade of grass becomes too long as a result of growth disorders, it will break. Although a longer blade of grass will be able to take in more sunlight than a shorter blade, since it would have a larger surface, it will be quicker to break under the effects of the wind. Here too, the evolutionary process tends towards a compromise between the contradictory pressures – a blade of grass which has sufficient length and effective resistance to kinking as a result of a good arrangement of materials.
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There is therefore a contradiction between the actual prerequisites of achieving the goal of increasing effectiveness set and the actual unreliability of achieving this goal with these given prerequisites. That biological structures are suitable for the resolution of contradictions should not surprise us. Biological structures also fulfil contradictory requirements. During the evolution of bears and the splitting-off of the polar bear from the bear evolutionary tree, a contradiction can be recognized, which, through the action of the evolutionary process, has led to a more efficient structure of significance for the new habitat. The requirements of life in the new habitat were connected with increasing the amount of heat produced and a change in fur colour. A brown coat proved suitable for heat production, but unsuitable for melting into the white surroundings in the northern polar regions. If the progressive trend that leads to minimization of materials or to a maintenance of the same quantity of materials alongside a reduction in energy consumption in heat generation is followed, it can be seen that the difference in temperature between body temperature and body-like isolation chambers is slight. This is, however, only possible because polar bear hairs are hollow and serve as light channels, which allow the black skin to be warmed through absorption (resolution of contradiction). The light-channel system of the polar bear coat can be interpreted as a contradiction between the requirements of having a white coat for camouflage and simultaneously of using the available sunlight. These insights are stored in catalogue systems for problem solving. Through the complete function storage, transferable structures are arrived at as a starting point for solutions for technical heat insulation systems.
Figure 7. Determination of Contradiction in a Biological System
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Figure 8. Polar bear hair functions as a light channel with the sub-functions of light scattering, luminescence and total reflection (after Tributsch, 1990)
Figure 10. Transparent Heat Insulation (Stumpf & Voß, 2003)
Figure 11. Active Heat Insulation (Stumpf & Voß, 2003) Figure 9. Determination of Contradictions, Building Facade (with TUP)
Normal heat-insulation systems are aimed at minimizing heat loss through radiation from the outer surfaces of buildings using insulating materials. On the basis of this understanding of the polar bear’s skin and fur, transparent heat insulation (TUP) was developed. If this is defined as the state of technology, a low level of efficiency is recognizable. For the amount of heat arises: Q = m◊c◊Dt
(4)
With a higher temperature difference dt between the ambient temperature and the temperature on the inside of the buildings outer surface, the expenditure for Q will be very large. To resolve this contradiction, the integrated biological system of polar-bear fur and skin was used as the basic solution for the systematic variation. The start point for a solution from the natural world could be reached through variation of the mechanism. Solarwarmed water with a low temperature on the inside of the outer wall of buildings obstructs the transport of heat from the inside to the outside.
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Dt on the inside of the outer wall is very small and so, therefore, is the quantity of heating Q. Conductions of just 0.5 to 0.8 W/m2 of cooling area obstruct the transport of heat to the outside and allow a doubling of the duration of solar panel utilization. The application of the nature-orientated innovation strategy with core elements of evolutionary laws and contradictions for goal setting and solution catalogues yields new opportunities and possibilities for strategic product development.
Conclusions Newer investigations of Tributsch confirm that dispersion, total reflexion and luminescence are basic functions of the polar bear hair. If the hair of the polar bear is energized with a short-wave UV laser, you can find a wide luminescence maximum in it, while in comparison the hair of a white pony does not show such features. But of course still further basic research is necessary, to get more deeply into this problem. However, living nature as a source of inspiration supplies lots of interesting suggestions for solving technical problems for every practically active engineer.
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References Altshuller, G.S. (1984) Erfinden – Wege zur Lösung technischer Probleme. Verlag Technik, Berlin. Hill, B. (1999) Naturorientierte Lösungsfindung – Entwickeln und Konstruieren nach biologischen Vorbildern. Export Verlag, Renningen-Malmsheim. Linde, H.-J. and Hill, B. (1993) Erfolgreich erfinden. Hoppenstedt Verlag, Darmstadt. Nachtigall, W. (1986) Biostrategie – Eine Überlebenschance für unsere Zivilisation. dtv GmbH & Co., München. Neumann, P.C. (1993) Technologieanalyse Bionik. VDI Verlag, Düsseldorf. Reichel, R. (1984) Dialektisch-materialistische Gesetzmä ßigkeiten der Technikevolution. Urania Verlag, Berlin. Stumpf, H.-G.; Voß, B. (2003) Bionik – Transfer aus der Natur: Ein Vortrag zur Nutzung niedrigster Temperaturen aus Solaranlagen zur Verringerung/
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Beseitigung des Transmissionswärmeverlustes von Bauteilen. Steinfurt. Tritutsch, H. (1990) Light collection and solar sensing through the polar bear pelt. Solarenergy.
Bernd Hill is active as a Professor for technology and didactics in the area of physics at the University of Münster in Germany. In his research, he concerns himself with innovation strategies, technical creativity and systematic and applied bionics. He is one of the representatives of the bionics authority net in Germany. It is his task to develop education conceptions to the bionics and to integrate the applied bionics into product development processes.
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESPUTTING BIOLOGY INTO TRIZCREATIVITY AND INNOVATION MANAGEMENT
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Putting Biology into TRIZ: A Database of Biological Effects Julian F.V. Vincent, Olga Bogatyreva, Anja-Karina Pahl, Nikolay Bogatyrev and Adrian Bowyer Our goal is to make biological information available for engineers via a ‘biological patents’ database in TRIZ. However, biological functions need to be co-ordinated simultaneously at many levels of organization – from cell organelle to population to ecosystem. Each function has links with other functions on different organizational levels. To account for this, we made auxiliary 5D ‘conflict’ matrices for biological structures and environments, and for causes and limits of actions; these allow us to resolve data about organisms into engineering-like chunks of information and cover the primary TRIZ constituents of ‘function’, ‘effect’ and ‘conflict’. In this way we can also provide a framework for the rationalization and quantification of bionics.
Introduction
A
s with any fundamental science, biology involves observation, description, classification and analysis. Engineering, on the other hand, acts, prescribes, utilizes and manages. The transition from science to technology, or from theory to practice, which is the course of design, is difficult since there is often not enough information about all the relevant effects and the conditions of their manifestation. In other words, a designer requires a bridge between ideas and their physical implementation. This bridge should make knowledge transfer more predictable, reliable and manageable. Methods and procedures are therefore required to give us an answer not only to the question ‘what to do?’ but also ‘how to do it?’. We consider that the most appropriate method to play the role of this bridge is TRIZ – the Russian Theory of Inventive Problem Solving – or perhaps a new, integrated, method based on TRIZ. TRIZ was originally developed within the physical and chemical domains of matter (mechanical, geometrical, electrical and chemical phenomena) and engineering. More recent applications have been in management, advertising, architecture and business consulting. However, those developing and adapting TRIZ have ignored the vast area of biology.
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This comprises numerous components at various levels of organization, e.g. biosphere, landscape, population, individual, organsystem, organ, tissue, cell, organelle, etc. One unfortunate result is that there is hardly any literature on TRIZ and biology that is not trivial. We are therefore expanding the existing TRIZ framework to incorporate biological data and biomimetic thinking (Bogatyrev and Bogatyreva, 2003).
TRIZ and Biology The underlying concept is that billions of years of ‘research’ have gone into natural systems as life on the planet has evolved. This ‘research’ is, a priori, very likely to provide novel solutions to existing technical problems (1, 2, 3). Our goal is to make this biological information available to engineers by cataloguing and classifying the effects of any given action, mechanism or function, in all biological systems. To accommodate this information we needed to adapt the structure of biological information to suit the established structure of TRIZ. We started our work with the ‘contradiction’ matrix of functional conflicts which is the usual, relatively easy, first step in using and understanding TRIZ. This method of defining a problem goes back at least as far as Plato, who pointed out that at the heart of © Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
PUTTING BIOLOGY INTO TRIZ
every problem there is a conceptual or functional conflict of the type: ‘It must be stronger but may not be heavier’ or ‘It must be both waterproof and porous’. Once the problem has been stated in the terms of an apparent impasse, TRIZ provides a method for the resolution of the conflict by presenting ways in which similar types of conflict have been resolved by other people, often in other areas of science or technology. If the nature of the conflict is sufficiently generalized, it is possible to use resolutions from a wide variety of disciplines with the increased possibility of a better and more innovative solution. This method necessarily involves loss of detail in the interest of generality, which implies that until the detail of specific functions and observations has been smoothed, not to say glossed, over, those functions and observations cannot be accommodated within the system. However, since the functions performed within and by living organisms are by and large different from, and more complex than, those with which we are familiar in technology, we need to analyse and distil those functions very carefully to make them compatible with the systems and methods of TRIZ. This analysis and distillation is not a trivial process. Initially, we designed auxiliary conflict matrices for biological structures and environments, and for causes and limits of actions. These allow us to break natural data into engineering-like chunks of information and cover the primary TRIZ components of ‘function’, ‘effect’ and ‘conflict’. The matrix we are building is therefore fivedimensional – it takes account of: • an object and its parts (which are accounted for in the TRIZ contradiction matrix) and in addition; • the ultimate purpose of action; • the environment in which the object operates; • the limits and causes of action; • the resources and auxiliary systems involved. It incorporates the ideas of several TRIZ tools within a single context, and is thus not only a database of physical effects, but also a database of intention and motivation. Another TRIZ concept of which we make much use is the system operator. This rather pretentious term simply means that there are levels of hierarchy above and below (and before and after) almost any object being studied. These have already been mentioned for biology (organelle, cell, tissue, organ, organism . . .), and that the level of the object under scrutiny is always referred to as the system. The super-system represents the
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assemblage of which the system is a part (for a cell it is a tissue; for an organism it is a population) and the sub-system represents one of the components of the system (for a cell, an organelle; for an organism, an organ). Increasingly we are finding that this classification – which in TRIZ is usually regarded simply as a way of expanding the conceptual approach to a problem – is an integral part not only of understanding the problem but of divining where the solution to the problem might lie. In some instances in the following discourse, the term ‘entity’ is used instead of ‘system’.
Gathering and Presenting the Data The database is available on our web site, http://www.bath.ac.uk/~ensab/TRIZ/. We have not yet made this open for the addition of data, but will later allow people to register so that we can moderate the additions. Initially it consists of a series of forms that prompt for information by asking specific questions. The reports generated from the answers to these questions (the database) can also be accessed and, with suitable security measures, edited. Computationally, it is a suite of server-side C++ classes and functions running on Bath University’s UNIX systems that record and search the data, and a set of client-side HTML forms and Javascript programs for checking consistency and completeness of the input. These run in any browser (Bowyer et al., 2003).
Specific Features of the Database In order to be compatible with existing TRIZ tools, the database needs to have the following features that currently exist as fields in records or links between records within it.
Definition of Function and Effect Because of the recent growth of interest in interdisciplinary ‘function-design’ problems, and because of recent advances in the philosophy of the analysis of function within biology (as a result of the development of behavioural science, where one cannot avoid the words ‘goal’ and ‘purpose’), we can finally lay to rest any fears about teleology (Bekoff and Allen, 1995). Biological systems are without doubt teleological. Their goal is a condition that is in some way useful or desirable, enhancing survival of the individual. We can thus define the function of the biological system to be ‘the action needed to achieve a useful or desired condition’. In technical systems, the achievement of this goal is delegated to a technical device. The
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goal still remains the future condition of the system. Therefore, the function of a technical system is ‘the action needed to achieve a useful or desired condition with the help of a technical device’. This apparently pedantic reframing allows us to say that the result of the function of a technical system in a particular environment is a technical effect, and the result of a function of a biological system in a particular environment is a biological effect. The technical effect is equivalent to the use of tools, a phenomenon observed in many mammals, birds and insects. Technology is not a uniquely human product. Note in passing that the definition of effect includes the observer. From the engineer’s point of view, a biological effect is usually defined as a resource that could be used in a technical system. We have a different point of view – an effect is not a resource. The biological effect should be a description of the system, as we always need to know the hierarchical level of functioning and its past, resources used, and future (goal) (Bogatyreva et al., 2002). Any given effect could cause many other effects at many levels of biological organization – from cell organelle to animal population and ecosystem functioning. Conversely, it could be caused by yet other causes and effects on many other levels! For example, running can be said to be an effect of legs moving fast (we see this at the organismal level). But running has other effects at the sub-system level: increases in pulse rate and breathing, etc. These subsequent effects of ‘running’ are consequent upon ‘movement’ but are non-specific for a particular function – they occur while swimming too. In this network of causality we therefore need some points of reference, some special rules that will help us to build the framework for the database. To simplify matters we suggest: Rule 1 – systems exhibit ‘goal directedness’ that allows us to trace a line of effects through the network of causality. For example, the movement of a fish’s fins (a system of organs) affects the fish (the super-system) that then moves through its environment, water. The user of this effect is the fish: the effect arises from the interaction between the system and the environment. So, we come to Rule 2 – one sees an effect in the supersystem, one or more levels up the hierarchy from the working system. The next rule is about resources. The system cannot be its own resource – sky hooks never work. So, Rule 3 – resources can be obtained only from other external systems.
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Table 1. Characteristic Causes and Effects Cause
Effect
Internal External Static Dynamic Object changes Medium or other object changes Both change Reciprocal Not reciprocal Static Dynamic
Cause and Effect An effect, as defined in TRIZ, is because of an obvious cause under particular conditions. In biology on the other hand, we may see an effect, but must search for causes, and can only guess about goals. This is because biological systems have as their goal a useful or desirable future condition. In the database, therefore, some differentiation is necessary (Table 1). We consider that: • causes and effects can be static or dynamic; • effects can be reciprocal or non-reciprocal; • some effects can be result of several subsystems acting together in (inner or outer) contact, or without contact.
The Medium We now define in which of eight types of medium an entity lives and functions – air, ground, water, or their combinations, or in the biotic environment (i.e. living on, or from, another organism – such as a parasite or treedweller).
Interactions between Entity and Medium In defining the system, we want to describe how the entity or system of interest interacts with the medium in which it functions and how it interacts with other objects. We make these distinctions here.
Sub-entities, Super-entities and Resources In this field we indicate the relationship of the sub-system and super-system to a given system. The characteristics of biological systems we take into account are: 1. The hierarchy, which regulates resources, energy distribution and the capacity of the system in space and time.
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2. The inertia, which affects the likelihood of an effect being expressed on a different level of the hierarchy. The further the supersystem is from the effector in terms of hierarchical levels, the less likelihood there is that the effect will be expressed. This causes cumulative properties of biological effects. An effect can be seen only in the supersystem. And every super-system tries to compensate for the actions/effects of its sub-systems. But the only system that ‘wants’ to change is that which has a goal. The general inertia in biological systems opposes change. That is why an active effect is always violent at the levels of the supersystem and environment. These can be described in terms of resources (Table 2) that are essential for the effect char-
Table 2. Possible Interactions Between an Entity (= System, Object) and Other Objects or the Surrounding Medium Object-medium interaction Object-object interactions
Inner contact Outer contact Fixed contact No contact Any contact
acteristics. Resources can be obtained only from other external systems. They can be field (i.e. delocalized, such as sunlight) matter and information and can be obtained from sub-systems, super-systems and the environment. In the entry field of our database, we choose whether the field results from the medium in which the entity functions, from the super-system, or from the sub-system. We define whether it is dispersed in space or aggregated, and whether there is much or little of it (Table 3). We do the same for substance and information resources.
Table of Parameters We must know what prevents, or makes difficult, the achievement of the effect. In this field we define how action or function starts and is stopped. One first selects a parameter, which ‘causes’ action, and whether this happens by its being decreased (‘DOWN’) or increased (‘UP’). Then one selects a parameter that ‘prevents or limits’ action in the same way (example: for shivering, the event which ‘causes action’ would be ‘energy change’, ‘DOWN’, and the event that ‘prevents or limits action’ would be ‘temperature change’, ‘UP’). One can select as many sets (of cause and limit) as one likes for a given entity (e.g. shivering can also be decreased for objects of high mass). The selection is considered a
Table 3. Types of Distribution of Resource
Field Substance Information
From medium
From super-system
From sub-system
I II III IV I II III IV I II III IV
I II III IV I II III IV I II III IV
I II III IV I II III IV I II III IV
I, II, III, IV – space distribution description:
Notes: A field is a permeating resource such as ambient heat or a magnetic field, a substance is a discrete resource, information is an intangible resource such as a nervous signal. The resource may be distributed in a number of ways: I is ‘dispersed/abundant’, II is ‘dispersed/rare’, III is ‘aggregated/abundant’, IV is ‘aggregated/rare’ (the diagram indicates these differences). The resource can come from the medium, from the sub-system or the super-system.
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complete entry when the last (LOW/HIGH) button is pressed. Up to that point, the combination can be changed without affecting the database. So, in general, a biological effect is more complicated than a physical or a chemical one, partly because it includes both of the latter. This often gives it a non-predictable or ‘emergent’ quality. But we can make an analogy between effects in biologically emergent and the technically predictable systems of engineering, merely by recognizing that both biological and technical systems need a goal to pursue.
How to Use the Database At present you need to be registered to add to the database, but please apply to the authors of this article for registration. The use of the database is rather restricted by comparison with its ultimate possibilities, when we have rather more data in it. However, you can interrogate it in a number of ways, asking what is known about an organism (a whale, for instance), or how you might achieve a certain effect (e.g. jumping – you can use muscles or springs, which have different adaptations). One use has already been to compare the overlap between technology (represented by the classic TRIZ matrix) and biological functions to achieve the same technical result. The overlap is only about 10 per cent, showing that 90 per cent of biological functions remain to be incorporated – biomimetically – into technology.
Searching The software has not been written to look pretty – it is severely functional. To search the database, activate the Search button. There are no compulsory fields; one can fill in as many or as few as desired and the system will find all records that match. Leaving the form completely blank returns every record in the database. The result of a search for ‘cells’ growth factors’ is shown in Table 4. The meaning of each field is indicated after the section that is to be filled in, and contains embedded examples.
Adding Data As an example, let us step through the procedure for entering data on a bony fish (teleost) moving in water. The form for adding data to the database can be reached via the Add button. There are compulsory fields that must be filled in, such as:
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1. Author’s name, ‘Adrian Bowyer’; 2. Entity description, ‘Teleost fish’ (action of the fins and tail in controlling movement); 3. At least one reference, ‘R. McNeill Alexander Locomotion of animals, Blackie, Glasgow’; 4. The entity’s function, ‘Move Æ Locomotion Æ Turn’; 5. The entity’s level of organization (molecule, organelle, cell (Procaryote, Eucaryote), tissue, organ, system of organs, individual, social organization, ecological system) – ‘individual’; 6. The medium in which the entity performs this function – ‘water (liquid)’. Other fields are optional. However more accuracy will help the project, such as defining super- and sub-entities. For example if ‘fish’ is the system, ‘fins’ and ‘tail’ are both sub-systems; ‘fish plus its environment’ is a super-system. 1. Cause and effect characteristics: cause is external to fish – interaction of its fins with water, effect is external and nonreciprocal. 2. Entity/Media interactions: ‘outer contact’. All objects (fins, tail and fish body) are fixed and media is outside of them. 3. The table of parameters that cause and limit action. 4. Causal parameters: force on surroundings is increased (cause which is in media sphere), shape changing abilities of fins/ tail (increasing) fins/tail strength. 5. Limiting parameters – high fish speed and high flow speed. 6. Resource is medium (water) viscosity (type III see table 2) and in the fins structure (substance) – partially flexible and partially strong – (type I see table 2). 7. A picture of the entity
8. The effect description. ‘Undulation of fins and tail as parts of fish body in any combinations causes slow direct movement (manoeuvre) of the fish and the direction of the waves of undulation is always opposite to direction of movement’. 9. Links to physical TRIZ. 10. Any useful notes.
Conclusion Our goal, to make biological information available for engineers via a database of the
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Table 4. Record Details Index Author Creation Date Last Modification Date System Description
Reference
Function Level of Organisation Living Medium Cause Internal or External Contact Cause Static or Dynamic Effect Object Or Medium Changes Effect (not) Reciprocal Effect Static or Dynamic Notes
142 Olga Bogatyreva Mon Jul 7 17:11:54 2003 Mon Jul 7 17:11:54 2003 cells’ growth factors Stimulation of cells by growth factors triggers cascades of signalling that result in cellular responses such as growth, differentiation, migration and survival. The globular architecture of the growth factors is essential for receptor binding. Nicholas J. Harmer, Dima Chirgadze, Kyung Hyun Kim, Luca Pellegrini and Tom L. Blundell The structural biology of growth factor receptor activation Biophysical Chemistry, Volume 100, Issues 1–3, Pages 545–553 regulate inform signal molecule biotic external static object changes not reciprocal dynamic Many growth factors signal through receptor tyrosine kinases, leading to dimerization, trans-phosphorylation and activation of tyrosine kinases that phosphorylate components further downstream of the signal transduction cascade. In general, weak binary interactions between growth factor and individual domains of receptors are enhanced by cooperative interactions with further receptor domains, and sometimes other components like heparan, to give rise to specific multi-protein/domain complexes. For example: nerve growth factor (NGF) is a symmetrical dimer that binds four storage proteins (two -NGF and two -NGF) to give a symmetrical hetero-hexameric 7SNGF organised around the -NGF dimer. It binds the extracellular domains of two receptor molecules in a similar way, so dimerising the receptor.
effects of action between system components or whole systems, will involve a great deal of data- gathering and analysis. We are looking for any ‘effect’ resulting from an object’s natural functioning and can find it in the object’s interaction with a particular environment. Our database is therefore five-dimensional – it takes account not only of object parts (which are also accounted for in the classical TRIZ contradiction matrix) but also of the environment in which the object operates, the limits and causes of its action, the ultimate purpose
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of the action and the resources and auxiliary systems involved. It thus incorporates the ideas of several TRIZ tools within a single context and is not only a database of physical effects, but also a database of intention and motivation.
Acknowledgement The studies presented here were financed by the EPSRC.
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References Bekoff, M. and Allen, C. (1995) Teleology, Function, Design and the Evolution of Animal Behaviour. TREE, 10(6), 253–55. Bogatyrev, N. and Bogatyreva, O. (2003) Triz And Biology: Rules And Restrictions. Proceedings of TRIZCON 2003, Altshuller Institute, Philadelphia, USA, 16–18 March. Bogatyreva, O.A., Pahl, A.-K. and Vincent, J.F.V. (2002) Enriching TRIZ with Biology: The Biological Effects database and implications for Teleology and Epistemology. ETRIA World-Conference-2002, Strasbourg, 6–8 November. Bowyer, A., Vincent, J.F.V., Bogatyreva, O. and Pahl, A.-K. (2003) Data Gathering for Putting Biology in Triz. Proceedings of TRIZCON2003, Altshuller Institute, USA – 16–18 March.
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Julian Vincent is a biologist, currently Professor of Biomimetics in the Dept of Mechanical Engineering at the University of Bath. His ambition is to make biological design solutions available to engineers, deskilling the process of transfer. Olga and Nikolaj Bogatyrev are biologists with much experience in the behaviour of social insects; they are also probably the only biologists to have a formal training in TRIZ. Anja-Karina Pahl is a facilitator who sees no barriers between the sciences and spends her life teaching others the same vision. Adrian Bowyer is a Senior Lecturer in Mechanical Engineering with special interests in computing and technology transfer.
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-1690Blackwell Publishing Ltd, 2005.March 2005141ARTICLESTHE ORGANIZATIONAL INNOVATION LABORATORYCREATIVITY AND INNOVATION MANAGEMENT
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The Organizational Innovation Laboratory Michael Lewis and James Moultrie Organizational ‘innovation laboratories’, dedicated facilities for encouraging creative behaviours and supporting innovative projects, have received scant academic attention despite their increasing popularity with a range of different practitioners. This paper develops an initial theoretical explanation of the phenomenon, based upon notions of organizational learning and dynamic capabilities. This framework is then used as the basis for analysing the structure, infrastructure, benefits and dis-benefits of three UK-based laboratory facilities (mass service, government department, academic institution). Preliminary conclusions suggest that the ‘innovation laboratory’ can offer real benefits for organizations: reinforcing corporate commitment to innovation and creativity by providing a physical manifestation of dynamic capability and double-loop learning concepts. Although the physical design of the space is central to its functionality – emphasizing dislocation from day-to-day activity, eliminating hierarchy, encouraging participation – direct facilitation remains critical to successful operation. There are also dis-benefits associated with what can be substantial financial investments and there is some evidence that such facilities can have a relatively short useful lifespan. Given the limited nature of the empirical base, the paper concludes with some specific suggestions for further work.
Introduction
I
n the early 1980s, the US Corporation MG Taylor created the first facilities that were recognizable as innovation laboratories. Their Navigation Centres, or ‘NavCentres’, were collaborative workspaces designed to encourage organizational communication and learning. They provided flexible and innovative environments equipped with moveable furniture, multiple write-on surfaces, a research library, multimedia tools and appropriate ICT for group working. The ambition was to create an environment in which strategies for business growth could be developed in a fun, dynamic, rapid and novel way. It is in the last decade however that there has been a rapid growth in the number of ‘innovation laboratory’ applications. Indeed, such facilities have emerged as an increasingly popular managerial response to the various challenges associated with organizational capability development and learning (Smeds, 1997; Wycoff & Snead, 1999). Consider the Accelerated Solutions Environment (ASE) © Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
at Cap Gemini’s London Headquarters for instance. This creative workspace is designed to enable ‘rapid business decision making and the creation of innovative solutions’. Workshop sessions can last up to four days, and each session follows a three-stage process: scan (divergent search for information), focus (convergence towards a solution) and act (selecting, planning and implementing). This generic ‘design’ process is appropriate for a wide range of business issues, and reflects the diversity of the target audience. The space can be configured for small or large group sizes, up to a maximum of around 100 participants. The flexibility of the workspace is especially important for these large group activities. Strong facilitation remains critical to the delivery of successful results. Although consultancy firms are amongst the ‘lead users’ of such facilities, there are also many industrial and public-sector laboratories. Indeed, the sheer range of applications engenders confusion over the intent and contribution of any individual innovation laboratory and it is this lack of conceptual clarity,
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together with the relative paucity of related research that motivates this paper. As a recent phenomenon, there has been little attempt to understand the underpinning concepts and overall benefits of these facilities. Thus, this paper has the following three objectives: 1. to raise academic interest in this emerging phenomenon, and encourage discussion over the relative benefits of such facilities; 2. to make an initial attempt explaining the theoretical basis for these facilities; 3. to identify opportunities for future research. A preliminary conceptual framework is developed that draws together relevant literature under two key themes. First, the framework explores the physical nature of the laboratory, including the structural (e.g. architecture, interior design) and infrastructural (e.g. group brainstorming software, interactive displays) content of a ‘typical’ innovation laboratory. Second, the framework articulates the potential benefits and dis-benefits of an innovation laboratory in terms of dynamic capability and double-loop learning concepts. These frameworks are then used to structure analysis and discussion of the findings from three UK-based, but cross-sectoral case studies: Royal Mail (mass service); Department of Trade and Industry (government department); and University of East Anglia (academia). Given its preliminary nature, the paper concludes with specific suggestions for further work.
Structure A laboratory is a physical research setting dedicated to conducting specific types of experiment. For organizational applications, this normally means a separate room or set of rooms designed for spatial re-configuration (e.g. moveable barriers, cubicles and open spaces etc.) and participant observation (Griffin & Kacmar, 1991). In addition to this functionality, many innovation laboratories recognize that architecture; décor, layout, lighting etc. also have a crucial influence upon participant behaviour (Gardner, 2001; Holahan, 1982). For instance, in seeking to encourage group-wide creativity, many facilities eliminate the physical manifestations of traditional behaviour and hierarchy: such as rectangular rooms or tables and chairs oriented from front to back. Infrastructure A laboratory is the setting for an experiment: ‘a research study in which the variance of all or nearly all of the possible influential independent variables not pertinent to the immediate problem of the investigation is kept to a minimum’ (Kerlinger, 1986). The infrastructure to control and measure variables (Shure & Meeker, 1969) in most innovation laboratories comprises both simple devices such as large writing spaces, materials for visualization (post-it notes, paper, pens, cards), and sophisticated ICT to support group brainstorming (Nunamaker, Applegate & Konsynski, 1988) and distributed group working.
Conceptual Background The Shorter Oxford English Dictionary defines a laboratory as ‘a room or building set aside and equipped for scientific experiments or research (originally and especially in chemistry) for teaching science or for the development or production of chemical or medicinal products’. For many people, the image conveyed will be of a physical science laboratory, complete with lab coats, bench spaces, Bunsen burners and specialist equipment. However, this laboratory definition does highlight a number of generic characteristics that may inform the creation of a conceptual framework for innovation laboratories; including the structure and infrastructure of the experimental environment and the benefits/dis-benefits of the facility.
What is an Innovation Laboratory? An innovation laboratory comprises specific structural and infrastructural content.
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What Contribution Does an Innovation Laboratory Make? In many markets, competitive advantage is dependent upon the dynamic efforts a firm makes to improve what it currently does well and how it intends to innovate for the future. Likewise, public-sector service providers face intense pressure to become more effective and efficient, and this in turn creates drivers for innovation (Halachmi & Bouckaert, 1994; Osborne & Gaebler, 1992). Given this diverse context, most innovation laboratories represent a pragmatic response to intangible and ambiguous problems such as a need to be more creative or futureorientated and therefore the precise value of an innovation laboratory can be hard to assess. In an attempt to obtain a balanced assessment of the phenomenon, the preliminary process model argues that an innovation laboratory delivers the following generic benefits and dis-benefits.
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Benefits Dynamic capabilities (i.e. ‘the organisational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve, and die’, Teece, Pisano & Shuen, 1997) are defined in large part by how managers make judgements about the organization and its future (e.g. Teece & Pisano, 1994). An innovation laboratory provides a set of resources to be dynamically reconfigured dependent on the issue under consideration, thereby enabling an organization to create and enhance organizational routines by which managers can adapt their resource base (acquiring, shedding, integrating and recombining them) to generate new value-creating strategies (Grant, 1996). In other words, a key benefit of a laboratory is its contribution to double-loop learning (Argyris & Schon, 1978). If single-loop learning is essentially operational learning that does not question underlying values and norms, then double-loop learning comes from the sort of enquiries that question fundamental service or market positions or the underlying culture of the operation. This kind of learning requires an ability to challenge assumptions, seeking to re-frame questions and remain open to all sorts of contextual changes. Organizations need single-loop to create consistency and stability. But, because organizational design is an inaccurate and imperfect process, laboratories – which help to increase the ‘tangibility of the problems we think about and the trappings we work with’ (Weick, 1977, p. 126) – appear to provide a pragmatic focus for double-loop learning activities intended to prevent the organization from becoming too conservative. Dis-benefits Too much double-loop learning can have dysfunctional organizational effects. Constant questioning of norms and values, encouraging dissent from established ways of working or simply spending too much time ‘thinking instead of doing’ can create instability as a consequence of over reactions and over analysis. If a laboratory renders the organization too sensitive to its environment and at the same time encourages too much introspection, it can become very difficult to distinguish noise from real issues. The organization could become prone to the exaggeration of small errors and be overly responsive to fads and fashions: indeed a cynical response to such facilities is that they are themselves simply a highly visible and expensive (and therefore associated with significant opportunity costs) managerial fashion statement.
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Research Method Because of the exploratory nature of this study, a case study approach was adopted. Detailed case analysis of three UK-based innovation laboratories forms the empirical core of the work, and the rich data sets generated (Yin, 1994) were both appropriate and useful given the relative intangibility of the phenomena under consideration. Specifically, in line with the conceptual framework outlined above, the empirical work sought to investigate (a) the structural and infrastructural content, and (b) the benefits and disbenefits (in terms of learning and capability development) of these laboratories. Three principal factors influenced the case selection process. First, given the relatively limited number of potential case sites, a significant challenge was access and as a result, a ‘cascade’ approach was adopted: specifically, the Royal Mail case opened up access to two facilities that had drawn on their experiences (Department of Trade and Industry and University of East Anglia). Second, the UKsetting and shared DNA of the facilities helped to improve the comparability of the cases. Third, as an exploratory study, this approach provided evidence of different types of application and organizational context: corporate; government (policy); and university (staff development). Primary data were collected using face-toface, telephone and email interviews using semi-structured question sets – investigating the sub-elements defined by the conceptual frameworks. The authors conducted a total of 14 interviews with senior managerial and technical staff at each facility. Each interview lasted between 1.5 and 2.5 hours. Some were interviewed on more than one occasion and asked to comment (where appropriate) on other observations and opinions. To further improve the reliability and validity of the results, all notes were presented to respondents, giving them an opportunity to comment on (but not veto) the interpretation. In addition, tours of the facilities were arranged and a variety of secondary sources, such as project plans, selected internal and external reports, etc. were made available. To discover and examine key themes, data were analysed using ‘in-case displays’ (Miles & Huberman, 1994); with relevant issues coded under ‘Structure’, ‘Infrastructure’, ‘Benefit’ and ‘Dis-benefit’ categories. This technique (together with interview transcripts and interim discussion documents) provided for the gradual building up of an explanation for each case in the light of extant theory (Meredith, 1998).
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The Case Studies The case studies were selected from a range of sectors: corporate innovation; governmental policy ‘futurology’; and university staff development. Photographs illustrating various elements of these labs are included in Appendix 1.
Royal Mail Innovation Laboratory (RMIL) Royal Mail is the UK’s national postal service. It faces challenges in its heavily regulated core markets both from new technologies (e.g. email) and new entrants able to target the most profitable segments (‘we were the world’s best at getting second to market!’). In late 1996, the technical research group established a demonstration facility on a corporate training and development site, intended to ‘showcase’ the opportunities and threats created by new technologies: ‘we had been commissioned by lots of different bits of the business to look at specific technology-impact problems like “tell me more about PDA’s” and this meant that we had developed lots of related knowledge’. This original laboratory required two dedicated infrastructure staff (a technician and overall facility manager) with individual sessions facilitated by a range of different people. Following the successful experience of this pilot, a business case (no formal financial justification was made but a ‘strategic’ case was made using positive testimonials) was written by one of these facilitators for a more permanent and interesting facility. In addition to visiting other technology ‘showcases’, this member of staff visited a number of entertainment experiences (e.g. EPCOT) and had discussions with the Disney Imagineers. Opened in October 2000 (after five months design and nine months construction) the new innovation laboratory was designed to deliver different service processes: support sales of technical solutions by providing a ‘space to think about these technologies’; ‘represent’ the corporate innovation intent and enable generic problemsolving. RMIL sessions normally last a single day (four days maximum) and follow a fairly consistent schedule (i.e. start about 9.30 and finishing about 16.00).
UK Department of Trade and Industry, Future Focus Laboratory (DTIF) In 1997/1998, the UK Department of Trade and Industry (DTI) established a Futures unit in response to the incoming Labour administration’s concern that there was not enough future thinking happening in the Department. More generally there was recognition that
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policy and strategy could benefit from some form of future-focused thinking process. As part of the response to this intent, the then Director of the Unit (which later became the Future and Innovation Unit, and no longer exists), envisaged the creation of what is now known as futurefocus@dti. Based in part on ICL’s Future Focus facility (this no longer exists) and influenced by the principles of the Royal Mail, the DTI’s central London facility adds the unique element of a series of themed (e.g. governance, community etc.) and dramatised scenarios, addressing five, ten and thirty-year horizons. Initially conceived as a collaboration venture between various parts of the DTI and a range of business partners (e.g. Fujitsu, Silicon Graphics etc.) the space is now entirely under DTI control, with substantial corporate involvement. The facility opened in 2001 and although specific motivations have evolved, several of the key drivers – such as the need to build consideration of emerging technologies into the way policy was planned and made – remain important. Some workshops are designed to develop action plans, others intended to stimulate thinking or facilitate discussion: ‘facilitators only ask that sessions are future-focused’. DTIF sessions have never been shorter than two hours or longer than two days and never with fewer than six people (fifteen people maximum).
University of East Anglia, Staff Development Hub (UEAH) In 2000, the University of East Anglia (UEA) learning and resources centre won a substantial grant from the UK Higher Education Funding Council to support their staff development work. At about the same time, various members of UEA staff (including a team from the staff development group) visited the Royal Mail laboratory. As a result of this coincidence, considerable enthusiasm developed to use the funding to create a facility based on Royal Mail ‘principles’. Having some space available, the lab consortium (a partnership between the library, the staff development group and a local design consultancy) largely avoided the encumbrance of official university bureaucracy. A typical UEAH session is around half a day.
Discussion In this section the boundaries of extant research are explored by discussing the explanatory power of the two conceptual frameworks in the light of the case findings.
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What is an Innovation Laboratory? Table 1 summarizes the structural and infrastructural content of the case studies. All the cases were bespoke, architect-designed spaces, sharing features such as unconventional layouts, curved walls, non-hierarchical furniture (e.g. curved triangular tables, comfortable
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seating), computer-supported brainstorming and an emphasis on technology adoption. Although, given the inter-dependence of their designs, some similarities were anticipated, the sheer extent of the imitation was surprising – especially given the different organizational and sectoral contexts. There was some divergence at the detailed design level, but
Table 1. Structural and Infrastructural Content of Case Studies Case
Structure
Infrastructure
RMIL
Separate single-storey building. After a theme park-type entry space (e.g. participants enter a fake lift and star-filled tunnel) combination of three spaces: (1) curved coffee area with palm trees; (2) multiple (semi-flexible) working spaces; (3) exhibition spaces.
DTIF
Located within DTI main building and comprises a futuristic entry space (e.g. sliding doors) leading to: (1) immersive theatre with a 150-degree screen, (2) technology showcase room; (3) work room with curved walls.
UEAH
Part of library building. Three core elements, designed as an integrative concept (predominantly painted deep blue), make up design: (1) curved wall ‘laboratory’, known as the Hub, used primarily for open-ended problem solving type activities but also used for meetings, presentations, training etc.; (2) a technology showcase space and; (3) a ‘drop-in’ resource centre, including coffee and reading areas.
(1) Entry space shows single 15 min. (professionally produced) Audio Visual show; (2) working spaces supported by group decision support software, data projection, triangular tables, whiteboards, creativity toys, etc; (3) exhibit spaces display various technologies and props. All sessions controlled by in-house facilitators. (1) immersive theatre equipped with professional projection equipment and technician, (n.b. the filmed scenarios do not exploit the 150degree screen); (2) range of exhibits from commercial suppliers is used to raise awareness of potential advances, and; (3) a work room has curved walls, white boards, triangular tables, laptops, group working software and data projection. All sessions controlled by in-house facilitators. (1) Hub supported by group brainstorming software, projection facilities, whiteboards etc.; (2) technology showcase space hosts programme of monthly seminars, exhibits etc. from commercial suppliers and UEA departments to raise awareness of technological (e.g. software) advances, and; (3) ‘dropin’ resource centre, houses reference materials (documentary and ICT) relating to staff learning, management, personal and professional development. All sessions controlled by in-house facilitators.
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this was more an indication of geographic constraints and the financial support available rather than a different design philosophy. For instance, the corporate and governmental labs include expensive ‘edutainment’ components (RMIL ‘lift and transport’ and DTIF immersive theatre). Structure Fundamentally, the architecture and design of the physical surroundings set out to influence human behaviour: echoing Bitner’s (1992) ‘servicescape’ argument that ‘physical surroundings [can] facilitate organisational as well as marketing goals’. Indeed, the very need for a dedicated and designed space (i.e. not simply hiring conference facilities) is testimony to the perceived importance of a ‘dislocation’ effect that takes people away from their day-to-day experiences. RMIL managers argued for instance that there were ‘fewer conflicts because participants leave traditional animosities (e.g. hierarchy, experience based and functional) at the door’. Similarly, UEAH staff wanted the walls of their facility to ‘instantly communicate “write on me” and [together with the toys] reinforce the acceptability of play’. Their main space is elegantly designed and predominantly painted in a deep blue colour to instil a sense of calm. All of the DTIF doors use sliding mechanisms and are activated by large push buttons in order to suggest high technology and the future. Interestingly, although many studies have confirmed the impact of physical setting on the nature of small group interaction, participation and aggression (Holahan, 1982; Sundstrom & Altman, 1989; Sundstrom & Sundstrom, 1986) there was no explicit reference in any of the case studies to any underlying principles or theories motivating structural laboratory design choices. As evidence of this, all of the facilities exhibited surprising degrees of spatial inflexibility. DTIF were left with an ‘odd-shaped room’ after their initial technology partner pulled out, and UEAH staff went as far as to stress that their design goals would be different a second time around: the space would contain the same facilities, but there would be ‘a greater emphasis on flexibility and reconfigurability’. Even allowing for the very real constraints of the construction process, this is an extraordinary admission, given the strategic goals of such facilities. Infrastructure Although difficult to portray as a formal experimental intervention, all cases featured a
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range of high and low-tech infrastructural devices intended to encourage ‘innovativeness’. For instance, all used frivolous props, with UEAH in particular arguing that there was evidence of the effectiveness of toys (‘people fiddle and giggle . . . laughter and fun is important’), playthings (children’s guitar, glove puppets, Knex, etc) and magazines (for cutting and sticking images etc). In stressing the significance of such props, interviewees were echoing Weick’s (1977) call for a ‘junk-laden’ laboratory to ‘invite activities involving novel combinations, which in turn . . . encourage hypothesis generation and discovery’ (p. 126). Equally, all the labs have lots of writing space on walls and employed essentially similar ICT (information, computing and technology) infrastructure (‘Group Systems’ brainstorming software, networked laptops, large-screen data projection etc.). In terms of perceptions, the technology is an important symbol of the ‘new’ cutting edge. The functionality of the brainstorming software in particular – shared parallel data input, anonymity, a full record of the discussion session, options to categorize and group ideas, voting and sorting of ideas – was highlighted as important by several interviewees: ‘the Information technology is important and probably what makes this kind of space different to other rooms’. This appears to support the literature arguing that such systems make decision processes more productive (i.e. more ideas) and inclusive, while increasing participant satisfaction (Gallupe et al., 1992). More problematically, the feature-rich laboratory infrastructure sometimes meant that the basic function of specific spaces had to remain fairly constant (e.g. breakout room or plenary space). There were some cases (e.g. RMIL audio-video capability) where infrastructure helped improve spatial flexibility, whereas the most expensive (i.e. multimedia rich) infrastructural components (e.g. the RMIL entrance show and the DTIF immersive theatre) represented the most inflexible features in their facilities. At the same time, despite the expensive physical facilities, most interviewees stressed the importance of human facilitation in enabling the laboratory to work effectively. In contrast, all the RMIL facilitators were volunteers who received no additional payments: expressing a variety of motivations from CV building, to taking an opportunity to be exposed to problems/issues across the whole of the business, meet external clients and so on. Their training is essentially an explanation of the ‘kit’ rather than any particular processes or facilitator skills. The DTIF facilitators plan and run all events and are also part of the man-
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agement ‘team’, providing input and support to marketing and development, and customer relationships. Interestingly, this aspect of the infrastructure is one key area of divergence between the designs. The RMIL target audience (i.e. its potential demand) for example, was determined by an informal ‘return on effort’ calculation and because the lab staff ‘choose’ to invest five or six days in setting up for a single day session, only a certain scale of problems justify the expenditure and therefore it tends to be used by budget-holders and middle/senior managers. Conversely, the UEAH facility adopts a much more flexible and lowkey approach to using their facility and therefore, although the initial target for the lab for was 25 sessions, over 40 have now been run (including repeat business) with more than 50 per cent utilization.
What Contribution Does an Innovation Laboratory Make? The conceptual framework articulated the potential contribution of an innovation laboratory in terms of learning and capability. Across the cases it rapidly became clear that it was necessary to consider organizational learning at two levels of analysis. First, the ‘experiments’ conducted in the innovation laboratory are obviously intended to promote learning for the individual/group/project using the facility – interestingly, most interviewees argued that the process was more significant than specific outcomes. Second, the design and the implementation of the laboratories can be viewed as learning initiatives in their own right. Benefits Laboratory experimentation has a long tradition in the social sciences (Haney, Banks & Zimbardo, 1973; Rijsman, 1969; Roth, 1988; Weick, 1965, 1977) but the approach has also been subject to serious criticisms: researchers interested in practical managerial issues have been particularly concerned with overly simplistic and artificial settings (Argyris, 1975) and the corresponding lack of external validity for any findings (Gordon, Slade & Schmitt, 1986). Although there was limited evidence of theoretical or methodological models underpinning individual innovation laboratories, benefits can be evaluated by considering how they deal with some of these academic criticisms. For instance, whereas traditional research laboratory subjects were undergraduate students (the process was therefore derided as ‘sophomore science’), participants in the four case studies were all practitioners
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engaged with real problem-sets. The RMIL staff specifically emphasized their desire ‘to be problem-led rather than prescriptive’ and all the facilities were able to articulate basic categories of relevant problem (i.e. problem types that appear to generate benefits) for their space. RMIL argued that product/service development; internal and external relationship management and strategic planning worked particularly effectively in the lab because they all require dislocation, team building, communication, creativity and creative problem-solving. DTIF ‘deal with a very wide variation in groups/backgrounds/objectives’ and do not specify ‘what we expect participants to take away from sessions, or how they use the facility. We only ask that sessions are future-focused’. They identified different types of event: scenario building; focus groups; consultation workshops; team building; project scoping and planning exercises (e.g. stakeholder analysis, risk assessment, evaluation, identifying skills and expertise needed). One experienced UEAH facilitator commented that ‘I have been in staff development for many years, and the Hub really makes you feel that you get genuinely closer to helping people find solutions, more so than other methods’. Stated more formally, the type of problem typically under investigation in an innovation laboratory (e.g. new service development) creates ‘high fidelity between the laboratory and the field’ (Ilgen in Griffin & Kacmar, 1991, p. 303). Other perceived liabilities can also be reconceived as assets: from the architecture and design deliberately reinforcing the artificiality of the setting, to embracing the fact that ‘participants are apprehensive about being evaluated but so are ambitious employees. Participants in laboratory groups seldom know one another intimately, but the same is true in organisations where . . . temporary problem solving units are the rule’ (Weick, 1977, p. 124). Equally, academic experiments are often dismissed for being too short (often within class times) whereas DTIF and UEAH sessions typically last two days, and RMIL sessions can last up to four days. Dis-benefits Given that all interviewees were either managers with responsibility for, or staff working within, a laboratory there was little explicit reflection (even after prompting) upon the downsides of a laboratory. Despite this limitation of the research, several potentially negative themes can still be inferred from the case material. In particular, with respect to the avowed innovation/learning/capability development intent, most expected mecha-
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nisms to close the learning loop were missing. Whilst there was some evidence in some of the cases of capturing session outputs – for example, the UEAH software collates brainstorm data and any drawings, sketches and writing on the wall are photographed and combined into a group report – there were limited examples of empirical and conceptual reflection by the laboratory ‘controllers’ or managers. Indeed there was even variance in the extent to which the controllers recognized the importance of ‘process’ control: RMIL experience suggests that groups use as much of the space as possible but only undertake four activities per day, and UEAH were trying to codify the most successful/appropriate techniques (e.g. drawing a timeline of historical state and desired future state) for inclusion in a facilitators’ guide. Across all the cases, formal evaluation was surprisingly ad-hoc: ‘I’m not aware that any kind of evaluation was built into the original [DTIF] project plan’. Although individual DTIF sessions are very carefully planned and closely facilitated, there is very little specification of ‘what we expect participants to take away from sessions, or how they use the facility . . . It’s hard to evaluate what we do, at least in quantitative terms, since an identifiable piece of policy . . . would always be the work of the people who made it, regardless of where they were when it was plotted’. Likewise, RMIL deploy electronic feedback forms, and a corporate follow-up questionnaire is despatched after all projects (i.e. not specifically designed for use in the laboratory) but no real use is made of this data (except that most facilitators like to score 8+ /10). The reliance upon ‘happy sheets’ as feedback also suggests potential disadvantages with overly supportive, ‘feel-good’ processes that build consensus and are seen as a positive outcome for the laboratory, even though this may be the inappropriate outcome for the participant organization. Extending this concern, UEAH staff claimed that it was difficult to describe a typical session (‘it is an “experience” and the overall shape of a session will depend on the goals of the organiser’) and anyway argued that the explicit goal was often different from the implicit goal. This ambiguity of purpose raises interesting questions of the ethics of such interventions – another widespread critique of traditional academic experiments (Argyris, 1975). Although the case studies were not longitudinal, there is evidence to suggest that how the organization manages its laboratory is particularly significant with respect to any discussion of dynamic capabilities. For example, the long-term viability of a facility appears to be influenced as much by its oper-
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ating context as by its effectiveness in encouraging organizational learning. For instance, the RMIL facility recently changed focus as a result of efficiency-led reorganization within the Royal Mail. The lab initially enabled the technology research group to explore opportunities for new technology within the business. Although an emphasis on creativity and innovation remains, the facility now falls under the auspices of the Human Resources Department, with a greater emphasis on supporting staff and business development. Unlike a management consultancy (e.g. CapGemini), where laboratory investment can be more directly related to fee-generation, client satisfaction and marketing benefits, further management changes at a the Royal Mail (a mass-service provider under intense cost-pressure) could easily lead to the closure of its innovation laboratory. Similarly, the public funding of DTIF and UEAH renders both laboratories (but DTIF in particular with its high-cost central London location) vulnerable to political (small p and large P) changes: almost regardless of the benefits they seem to deliver. In this respect, the innovation and creativity manifest in these facilities can actively work against them if organizational ‘fashion’ shifts. Perhaps reflecting the underlying insecurity felt by laboratory personnel, most interviewees argued that ‘the facility remaining open is a good indication of its success’.
Concluding Comments Before drawing any conclusions from the case material, it is important to highlight some of the work’s limitations. This was an exploratory study based upon a small, predominantly UK-based, selection of the total potential sample population. Although the semi-structured question set followed two conceptual frameworks, there was no formal testing of research hypotheses. Equally, combining theoretical and empirical elements means that each could have been more fully explored. Furthermore, in condensing hours of interview notes into a series of observations and quotes, the researchers’ interpretation of events is a significant ‘reality’ filter. Lastly, although the cases were selected to explore laboratory characteristics and benefits across a range of different contexts, the range of organizational types had limited impact on specific laboratory designs. Noting these limitations, four preliminary conclusions can be drawn. First, the physical form of an innovation laboratory is significantly more than an aesthetic
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issue. Indeed, the cases suggest that it is integral to the functionality of the facility, especially with respect to generating some form of participant dislocation prior to undertaking laboratory activities (i.e. it appears to enable rather than diminish group creativity). Less clear is the extent to which there are specific designs for dislocation and creativity (i.e. curved walls, particular colours) or any need for a full-blown ‘Disney-type’ experience. More pragmatically, the research suggests it is very important to avoid creating structures (e.g. big curved walls, 150-degree screens) that inadvertently minimize the future flexibility of the space. This emphasis on flexibility is also relevant for the aesthetic design of the space, although all relatively recent creations, some laboratories were already showing signs of becoming dated (attempts to look futuristic date particularly quickly). Second, the combination of high and lowtech infrastructure is equally important in determining the effectiveness of an innovation laboratory: for example, the extent to which a specific facility is ‘junk-laden’ appears to reflect the level of creativity expected from a particular session. Although the choice of specific infrastructure should complement the physical design, three generic elements stand out: ‘off-the-shelf’ computer and technologybased tools to support non-hierarchical group brainstorming; multiple writing surfaces and non-hierarchical furniture (e.g. triangular tables). As with the physical layout of the facility however, the research revealed a surprising number of examples where the implementation of the infrastructure had unnecessarily constrained the overall flexibility of the laboratory (e.g. computing network that required tables to be fixed to the floor). Facilitation remains arguably the most important element of even the most high-tech laboratory, and surprisingly this was the area where the research revealed the least well-developed set of heuristics for determining good and bad practice in different applications. Third, the benefits of an innovation laboratory appear strongly contingent on the specific application and the operating context. Successful applications appear to be those where the laboratory and the ‘problem’setting are closely related, such as team-based new product development or inter-organizational collaborations; the research suggests they work because they are by definition dislocating and creative and predicated on teambuilding and close, frequent communications. It is less clear whether the presence of an innovation laboratory influences the whole organization’s innovative performance (i.e. assists in the development and deployment
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of dynamic capabilities) or the role that the laboratory plays in the organization’s wider innovation process. Here the operating context seems to be particularly significant. A consultancy firm for instance, is already a creative environment, and as such the value of the laboratory is quickly and widely accepted (e.g. CapGemini has now built 16 ASE facilities around the world). In larger corporate and governmental applications however, there is clear evidence that the facilities are valuable for individual projects, but this kind of deliverable makes it much harder to justify the ongoing expense, especially against the backdrop of changed managerial priorities and sometimes overt cynicism. Of course, once the physical site has been built there is an argument that this sunk cost helps ‘lock the commitment to innovation’ into the organization. Thus, perhaps the most significant benefit of each facility is the degree to which it is a physical reinforcement of the strategic intent of the organization to be innovative or creative. Lastly, there are also dis-benefits. From the evidence of this research, acknowledging that the facilities were not designed to meet academic research goals, it is interesting to reflect upon the lack of both session-by-session and (most significantly from a strategic perspective) aggregate evaluation. The absence of formal feedback processes appears to undermine the fundamental double-loop learning motivations of the original laboratory investments. As a result, there was no evidence of too much double-loop learning, but there was a suggestion that the priority of too many sessions had become to make participants feel good – surely an unrealistic expectation if making a true commitment to innovation and change?
Future Work The preliminary theoretical discussions and empirical findings presented in this paper highlight many areas that warrant further work. However, it is proposed that the following three areas merit particular attention: 1. A more broadly based survey of the laboratory phenomenon is clearly necessary. This study has revealed many more corporate (e.g. Phillips, BT, Boeing), consultancy (e.g. CSC, IBM, Accenture) and academic (e.g. SimLab Helsinki, Technogenesis, UltraLab) facilities in Europe and North America, and accessing and analysing this broader data set would allow more general conclusions to be drawn. A generic typology of labora-
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tories could then be developed, and specific comparison could be drawn between sectors and across countries. 2. Given the nature of the initial data collection, there was limited emphasis placed upon specific experiments within the laboratory spaces. Some form of empirical investigation triangulating between facilitator, participant and observer feedback would offer a much richer insight into the advantages and disadvantages of different types of experiment and the interrelationship with the different laboratory
characteristics (e.g. what difference do curved walls really make?). 3. From a theoretical perspective, future research could also help develop the dynamic capability model. Preliminary evidence suggests that laboratory spaces can potentially enable organizations to reconfigure their resource base to innovatively respond to opportunities in a timely manner. There is scope for understanding these underlying learning routines, whilst also providing concrete examples of these learning routines in practice.
Appendix 1: Images of Organizational Laboratories
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Image 1: The entrance to the Royal Mail Innovation Lab. Designed to replicate a ‘lift’ giving the impression of traveling to somewhere special
Image 2: The entrance to DTI Future Focus. Designed to feel futuristic, with calm lighting and sliding doors
Image 3: The Hub at UEA. Curved walls, cool colours and professionalism reinforce core values
Image 4: A typical creative problem solving room which curved write-on walls, IT-supported brainstorming and tables for small groups
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References Argyris, C. and Schon, D. (1978) Organisational learning. Addison Wesley, Reading, MA. Argyris, C. (1975) Dangers in Applying Results from Experimental Social Psychology. American Psychologist, 30, 469–85. Bitner, M.J. (1992) Servicescapes: the impact of physical surroundings on customers and employees. Journal of Marketing, 56, April, 57–71. Gallupe, R.B., Dennis, A.R., Cooper, W.H., Valacich, J.S., Bastianutti, L.M., and Nunamaker, J.F. (1992) Electronic Brainstorming and Group Size. Academy of Management Journal, 35(2), 350–369. Gardner, G. (2001) Lab Practicals. FX: Design, Business and Society, February, 56–60. Gordon, M.E., Slade, L.A. and Schmitt, N. (1986) The ‘science of the sophomore’ revisited: from conjecture to empiricism. Academy of Management Review, 11, 191–207. Grant, R.M. (1996) Towards a knowledge-based theory of the firm. Strategic Management Journal, 17(Summer Special Issue), 109–22. Griffin, R. and Kacmar, K.M. (1991) Laboratory research in management: Misconceptions and missed opportunities. Journal of Organisational Behaviour, 12, 301–11. Halachmi, A. and Bouckaert, G. (1994) Information and public sector productivity: an international symposium. International Journal of Public Administration, 17(1). Haney, C., Banks, C. and Zimbardo, P. (1973) A study of prisoners and guards in a simulated prison. Naval Research Reviews, September, 42–59. Holahan, C.J. (1982) Environmental Psychology. Random House, New York. Kerlinger, F.N. (1986) Foundations of behavioural research: educational psychological and sociological enquiry. Holt, Reinhart and Winston, London. Meredith, J. (1998) Building operations management theory through case and field research. Journal of Operations Management, 16, 441–54. Miles, M.B. and Huberman, A.M. (1994) Qualitative Data Analysis: An Expanded Sourcebook, 2nd edition. Sage, Newbury Park, CA. Nunamaker, J.F., Applegate, L.M. and Konsynski, B.R. (1988) Computer-aided deliberation: Model management and group decision support. Journal of Operations Research, 36, 826–48. Osborne, D. and Gaebler, T. (1992) Reinventing Government: How the Entrepreneurial Spirit is Transforming the Public Sector. Addison-Wesley, New York. Rijsman, J. (1969) The Leuven Laboratory for Experimental Social Psychology. Administrative Science Quarterly, 14(2), 254–9. Roth, A.E. (1988) Laboratory Experimentation in Economics: A Methodological Overview. The Economic Journal, 98(393), December, 974–1031. Shure, G.H. and Meeker, R.J. (1969) A ComputerBased Experimental Laboratory. Administrative Science Quarterly, 14(2), 286–93. Smeds, R. (1997) Organisational Learning and Innovation though Tailored Simulation Games: Two Process Re-Engineering Case Studies. Knowledge and Process Management, 4(1), 22–33.
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Sundstrom, E. and Sundstrom, M.G. (1986) Work Places. Cambridge University Press, Cambridge. Sundstrom, E. and Altman, I. (1989) Physical Environments and Work-Group Effectiveness. Research in Organisational Behaviour, 11, 175–209. Teece, D. J. and Pisano, G. (1994) The dynamic capabilities of firms: An introduction. Industrial and Corporate Change, 3(3), 537–56. Teece, D.J., Pisano, G. and Shuen, A. (1997) Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18(7), 509–33. Weick, K.E. (1965) Laboratory Experimentation with Organisations. In March, J.G. (ed.), Handbook of Organisations. Rand-McNally, Chicago, pp. 194–260. Weick, K.E. (1977) Conceptual Notes: Laboratory Experimentation with Organisations: A Reappraisal. Academy of Management Review, January, 123–138. Wycoff, J. and Snead, L. (1999) Stimulating innovation with creativity rooms. The Journal for Quality & Participation, March/April. Yin, R.K. (1994) Case Study Research: Design and Methods, 2nd edition, Sage Publications, Newbury Park, CA.
Michael Lewis (MEng, PhD) is Professor of Operations and Supply Management at the Bath University School of Management. He teaches MBA, MPA and undergraduate courses in operations management, service management, operations strategy and the management of technology and innovation. Prior to joining the Bath faculty in 2004, he was senior lecturer at Warwick Business School and a researcher in the Manufacturing Institute of the University of Cambridge. He has also been a Visiting Professor at the McDonough School of Business, Georgetown University. In addition to various academic and practitioner journal articles, he is co-author of Operations Strategy (2002, Financial Times Prentice-Hall) and co-editor of Critical Readings in Operations Management (2003, Routledge) and the Encyclopaedic Dictionary of Operations Management (Blackwell, 2005). His current public and private-sector research interests include the practical and reputational implications of operational and supply failure, product-service systems and innovation laboratories. James Moultrie (
[email protected]) teaches and researches product design in the Institute for Manufacturing at Cambridge University, where he is interested in product design, design management and product management. His main research activities concern the management of industrial design, improving design practice, creativity in product development and also product aesthetics.
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Dialogue-Based Evaluation as a Creative Climate Indicator: Evidence from the Pharmaceutical Industry Mats Sundgren, Marcus Selart, Anders Ingelgård and Curt Bengtson This paper examines how different forms of performance evaluation relate to aspects of the creative climate in a major pharmaceutical company. The study was based on a large employee-attitude survey that was distributed to all company employees. The study analyses survey results from 5,333 employees at five R&D sites. The results indicate that management’s evaluation of employees (either dialogue-based or control-based) relates to the type of motivation (intrinsic or extrinsic) that drives employees, to their style of thinking (value-focused thinking) and on their attitudes to organizational creativity. The paper then discusses implications of these findings for HRM.
Introduction
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n influential senior pharmaceutical R&D manager probably would not say: ‘We need more control and less creativity and innovation’. Management most likely communicates – and often in highly positive terms – the importance of having a culture that enables long-term success and innovation. But the predominant way in which pharmaceutical R&D managers evaluate and reward is based on their abilities to have control. The main focus of the present study is to focus on the limitations of managerial control systems in terms of their ability to provide a creative climate in the organization. It is argued that different forms of dialogue-based systems may be more successful in this respect. The creative climate and the adoption of ideas into innovation is an immensely important asset and success factor for any organization that heavily depends on its intellectual capital (Dougherty, 1999; Hargadon & Sutton, 2000). This is particularly true for pharmaceutical organizations (Horrobin, 2002). More knowledge about its predictors is therefore needed. A creative climate refers to factors that stimulate or block creativity and innovations in everyday life (Ekvall, 1996, 1997). They include an organization’s leadership styles,
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visions, objectives, goals, strategies, resources, personnel policies, beliefs, values, structures and systems. All these factors are crucial for how people view the climate in which they work. A creative climate, regarded as a culture, may be defined as a system of shared meaning held by members that distinguishes the organization from other organizations (Schein, 1985). It is largely as a result of what the organization has done before and the degree of success it has had with those endeavors (Schein, 1983). For instance, there is an important link between the creative climate and innovation (Ekvall, 1987, 1995), which is reinforced by organizational success over time. Based on a diagnosis of the creative climate, a distinction can be made between between innovative, average and stagnated organizations based on product performance and success of the organization as a whole (Ekvall, 1987). Innovative organizations develop more new products and services, and generally get them to the marketplace more quickly, while organizations that have become stagnant are often unable to handle new product or service development effectively. In order to demonstrate this connection between creative climate and innovation, having developed the ‘Creative Climate Question© Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
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naire’ (CCQ), Ekvall (1987) subsequently validated it by comparing CCQ scores for Swedish companies that were independently rated as ‘innovated’, ‘average’ or ‘stagnated’. The CCQ scores were as expected. This vital link between creative climate and innovation may, from a wider perspective, be regarded as a key feature of organizational creativity (Woodman, Sawyer & Griffin, 1993). Amabile (1988, 1997) suggested that five environmental components affect creativity in organizations: • encouragement of creativity – information and support for new ideas must be communicated openly between all the different levels in the organization; • autonomy – individual freedom and control must be an integral part of day-to-day work; • resources – basic materials and information for the work must be available; • pressures – positive challenges must be imposed and negative perceptions of workloads should be avoided; • organizational impediments to creativity – influences of conservatism and internal strife must be reduced. In the present study, we apply four of these components to build up our own creativity climate factor, namely encouragement of creativity (novel ideas are allowed to fail), autonomy (novel ideas are appreciated), resources (time may be invested in new ideas) and pressures (innovation is recognized).
Impact of Performance Evaluation on Aspects of Creative Climate Shalley and Perry-Smith (2001) found that there might be a connection between employees’ self-rated creativity and how they are evaluated. Employees see some forms of evaluation as mainly providing information to improve performance. It has also been found that other forms are perceived as primarily measuring performance relative to a set standard, that is, actions taken to exercise control that may create obstacles to creativity. Recent research suggests that situational factors can affect behaviour related to creativity in two ways: one controlling and the other informational (Shalley & Perry-Smith, 2001). Both have potential to influence the way in which individuals perceive their own competence and self-determination for a specific task (Deci & Ryan, 1980, 1985; Ryan, 1982). The discussion concerning informational versus controlling evaluations furthermore resembles Zhou’s (1998) notion of feedback style (informational versus controlling). The style of administrating rewards, rather than the rewards
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themselves, is the key issue for judging or perceiving rewards as either informational or controlling. Recent research also reveals that the effect of reward seems to be dependent on how the person interprets it. For example, it has been suggested by Hennesey and Amabile (1988) that interpreting a promised or given reward as an attempt at controlling may lead to lowered intrinsic motivation. We argue that there is a need for using an operational subset of informational evaluation to make this concept more understandable to practitioners. Dialogue-based evaluation may be regarded as such a sub-set. Flexible, nonformalized evaluation of the work task characterizes this type of assessment. It involves making rewards more informational by acknowledging appropriate behaviour, without using rewards to try to control behaviour (Deci et al., 1994; Deci, Nezlek & Sheinman, 1981). Dialogue-based evaluation is guided by, and combined with, giving information, and thus creating an opening for exchanges of ideas and opinions. If dialogue has substance and strength, it will uncover deeper meaning that necessitates exposing values, and, at least, implicitly, keeping them at issue (Blake, 1996). A dialogue may thus be looked on as an organizational inquiry (Duncan & Weiss, 1979) or as a strategic conversation (Van der Heijden & Eden, 1998). In contrast to discussion, which sometimes preserves the status quo for individuals by its vertical control-based nature, dialogue is a communal activity through which collectives learn and change (Preskill & Torres, 1999; see also Hodgkinson & Sparrow, 2002). Dialogue is an important precondition for advanced horizontal learning forms in organizations, such as co-configuration. This particular learning form creates knowledge and transforms an activity by crossing boundaries and tying knots between different forms of activity systems (Engeström, 1999, 2004). The horizontal aspect of learning puts a heavy emphasis on actions of bridging, modelling, textualization, objectification, conceptualization and visibilization. It hereby provides a new perspective on the essence of work life creativity. According to Engeström, horizontal learning, based on dialogue, is a precondition of situationally constructed social spaces, arenas and encounters needed in new forms of expansive learning at work. A controlling or control-based evaluation can be defined as a work evaluation characterized by the use of formalized standards and forms. Rules used to direct the individual to act in a certain way guide this assessment, which is less likely to involve sharing information and knowledge or exchanging ideas. Competence feedback, delivered in a control-
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ling style, often makes external constraints salient. This implies that certain types of outcomes that the individual must obtain, or certain levels of creativity that he or she must achieve, are highlighted (Zhou, 1998). When confronted with competence feedback delivered in a controlling style, and interpreted by them as attempts at control, people generally experience feelings of external causality. They feel that there is someone else controlling their behaviour and actions. So it is likely that they interpret this style as attempts at inhibiting and restraining. This may increase extrinsic motivation at the expense of intrinsic motivation, and thus reduce creativity (Amabile, 1999).
Research Objectives and Hypotheses Compared to control-based evaluation, it is believed that dialogue-based evaluation is better able to encourage creativity, increase autonomy, and reduce conservatism and internal strife among employees. This belief relies on a series of experimental findings (e.g. Deci & Ryan, 1980, 1985; Ryan, 1982; Shalley & Perry-Smith, 2001) that point in this direction. We believe that these findings should also be valid in organizational contexts, with emphasis on matters such as whether it is perceived that the organization is establishing a climate and culture in which: (i) new ideas are appreciated; (ii) time is invested for testing new ideas; (iii) people receive appreciation for innovation; and (iv) new ideas can fail without penalty to the originator. It may be argued that dialogue-based evaluation and control-based evaluation by definition need not be dichotomous in practice and that interactions might exist. But previous research (Shalley & PerrySmith, 2001) shows that the two types of evaluation are conceptually distinguished from each other. We thus regard it as important to treat these two forms of evaluation as distinct concepts from a scientific viewpoint, although this distinction may not always be as clear-cut in practical life. Hence the following hypothesis is made: H1: Dialogue-based evaluation will better predict the existence of creativity than control-based evaluation. However, both evaluation forms will serve as reliable predictors of creativity. Building on the connection between types of evaluation and creativity, it is not difficult to understand why other types of performance evaluation used by an organization have been reported to have different influences on intrinsic motivation, which has been said to be a key factor for creativity (Amabile, 1988; Amabile
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et al., 1996; Shalley & Perry-Smith, 2001). Several studies link motivation to acts of creativity (Burke, 1983; Gardner, 1993; Kirton, 1989; Koberg & Chusmir, 1987; Payne, 1987). Intrinsic motivation can be defined as the motivation to work on something because it is interesting, involving, exciting, satisfying or personally challenging. It has been suggested that a variety of rewards significantly undermine free-choice intrinsic motivation (Deci, Ryan & Koestner, 1999). Other studies suggest that it is a myth that financial incentives should erode this type of motivation (Cameron & Pierce, 1994; Eisenberger & Cameron, 1996). These results must be regarded as an important extention to the general claim made by Deci, Ryan and Koestner (1999). Extrinsic motivation, which is its counterpart, may be defined as motivation for work driven by the desire to attain some goal apart from the work itself – such as achieving a reward or position or meeting a deadline (Amabile, 1997; Amabile et al., 1996). Several studies indicate that this latter type of motivation is not as conducive to creativity as intrinsic motivation (Amabile, 1986, 1999; Deci & Ryan, 1996, 2000). However, Amabile goes further than this. She states that extrinsic motivation reduces intrinsic motivation, and thus creativity is reduced. Here, the assumption is that when the controlling aspect is predominant, there will be a negative effect on intrinsic motivation. When the informational aspect is strong, and positive information is expected, conveyed or perceived, then intrinsic motivation will remain stable or increase (see Bass & Avolio, 1994; Bryman, 1996; Buchanan, 2001; Tichy and Devana, 1986). From the above reasoning, the following hypothesis is made: H2: Dialogue-based evaluation will better predict the existence of intrinsic motivation than control-based evaluation. Apart from having an impact on intrinsic and extrinsic motivation, dialogue-based and control-based evaluations are also assumed to have an impact on another type of motivation called value-focused thinking. According to Keeney (1992), value-focused thinking not only manifests a creative thinking style or a creativity technique, it also serves as the key type of motivation by which creativity may be coupled with decision-making (see also Selart & Boe, 2001). People should let themselves be guided by objectives, while asking themselves ‘how?’ rather than limiting themselves to a few options when they make decisions. A person that is driven by value-focused thinking creates his or her own decision alternatives to the extent that these alternatives become ‘tailormade’ to the individual goals and objectives.
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The opposite thinking style can be labelled ‘alternative-focused thinking’, in which the decision- maker does not play an active part in the design of the alternatives, and thus makes the choices from pre-set menus. However, goals and objectives may not necessarily be individual. In organizations, it is more likely that they are formed on an organizational or group level. In this setting, asking others for suggestions becomes a vital part of an organization’s value-focused thinking. By focusing on goals and objectives, people will be better able to find imaginative decision alternatives that are tailored to their problems (Keeney, 1992). For human resource management (HRM) to be able to encourage this kind of thinking among employees, it is believed that HRM must rely on dialogue-based evaluation rather than on control-based evaluation. Through dialogue, in the form of feedback, managers can give employees a wider range of perspectives. This is mainly achieved through conversation. Note that we do not suggest that dialogue-based evaluation is the only way in which management can encourage valuefocused thinking. Other factors, such as creativity training and organizational culture, are also important dimensions in this context. Hence, this hypothesis is made: H3: Dialogue-based evaluation will better predict value-focused thinking than control-based evaluation.
Methods Design Overview This study explores and examines different factors in the work environment that are relevant to creativity and innovation in a large pharmaceutical company. The global R&D organization of the company AstraZeneca includes about 10,000 employees and is primarily located in six major sites in Sweden, the UK and USA. The R&D organization is mainly divided into two large units – discovery and development – both of which are represented at most of the research sites. The data used for the analysis are taken from a recent global employee questionnaire survey at AstraZeneca. This survey addressed the entire AstraZeneca organization, including marketing, production and research companies and had 138 items, which covered a wide range of organizational issues, such as organizational belonging, education background, opinions about daily work life, communication, management and external competitors. International Survey Research Ltd in the UK conducted the survey and was
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instructed by one of the authors of this study about which items were to be included. The author also had an influence on the wording of those. The questionnaires were available in electronic and paper forms. The distribution between electronic and paper in the overall survey was 27 per cent and 73 per cent, respectively. Local AstraZeneca co-coordinators were responsible for communication, and sending reminders about returning the paper and electronic questionnaires. Data were collected from September to November 2000. More than 38,000 employees were invited to respond to the survey at AstraZeneca; the overall response rate was 59 per cent (for the entire organization) and 53 per cent (for the global R&D organization).
Respondents and Model The approach of using keys or constructs for understanding and evaluating factors for creativity has been used before (Amabile, 1988, 1999). The respondents in this analysis come from 5,333 employees, including the Development and Discovery organizations within five R&D sites (three in Sweden and two in UK), and thus representing a majority of the R&D sites and more than 50 per cent of the company’s global R&D organization. Sites from other countries were excluded mainly because they were too small. Thirty-one items were extracted from the global survey study based on their relevance to five categories: motivation, value-focused thinking, control-based evaluation, dialogue-based evaluation and items specifically related to creativity.
Instruments and Scaling In all 31 items selected, the respondents were asked to indicate to what extent each statement described their work environment on a scale ranging from 1 to 5: agree (1), tend to agree (2), don’t know (3), tend to disagree (4) and disagree (5). Three items related to control-based evaluation (numbered 7, 8, and 9 in Table 3) were reversed after people responded to them, and corresponding definitions of scale labels were shifted (e.g. original question: ‘The performance targets I have in my job have been established with my input’. Inverted question: ‘The performance targets I have in my job have not been established with my input’). This was done to clarify their correlation with controlled- based evaluation.
Statistical Methods Exploratory factor analysis was used to examine the validity of items and factors. Multiple
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regression analyses were used to test the hypotheses. Multiple analyses of variance (MANOVA) were made to evaluate whether the effects of background variables (education, gender, managerial role and R&D site belonging) on the dependent variables (creativity, motivation and value-focused thinking) were reliable. SPSS 10.0 was used for all statistical analyses except for the factor analysis, which used SAS 8.1. A prerequisite for making a factor analysis was to test the data for sphericity using Bartlett’s test. The results of this test show that the matrix fulfilled the necessary requirements (p < 0.001) for making this kind of analysis. An exploratory factor analysis was done with varimax rotation (see Table 1) to further establish the validity of our measures. Table 1 shows that all factors demonstrated internal consistency (alpha values 0.60–0.90). Eigenvalues of one or higher were used to determine the number of factors. According to Hair et al. (1998) factor loadings of more than 0.50 are considered significant when the sample size is larger than 100. Accordingly, the criteria for item retention were based on factor loadings of 0.50 or higher and cross-loadings lower than 0.30. The factor analysis (see Table 1) resulted in six factors with eigenvalues of one or more and the elimination of five items.
Internal Consistency of Control- and Dialogue-Based Evaluation Data from the questionnaire indicated that most employees felt that established objective norms existed as regards evaluations of performance in the organization. Table 2 presents the three measures of control-based evaluation taken from the questionnaire. These included reports of whether work performance was evaluated fairly, whether employees felt that they were held accountable for delivering results and whether it was felt that performance targets were clear (a = 0.76). Sixty-one per cent of the respondents reported that their immediate manager provided regular feedback on their performance (31 per cent reported in the opposite direction). This indicates that dialogue-based evaluation is used extensively in the organization. As many as 74 per cent of the employees reported that they use performance targets. The fact that 85 per cent of these 74 per cent (i.e. employees using performance targets) think that targets were established with input from the employees themselves constitutes another indication of the high prevalence of dialogue-based evaluation within the organization. Table 2 gives the nine measures of
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dialogue-based evaluation extracted from the questionnaire. As can be seen, most of these measures dealt with issue of whether the employees felt that they received sufficient information on their performance from the immediate manager or from the team (a = 0.92).
Internal Consistency of Creativity Analytical concepts were established to measure creativity, which included four measures of whether the employees felt that the organization had established an innovative culture and climate (a = 0.77). Participants generally reported that the creative climate and culture of the organization was satisfactory, but that the time allocated for generating new ideas could be improved. Table 3 presents the four items included in this factor.
Internal Consistency of Extrinsic and Intrinsic Motivation The questionnaire included items on pay equity in the organization. Based on the results, it is argued that such equity gives quite a good indication of the extent to which control-based evaluation is exercised overall within the organization. In this study, an estimated 49 per cent of the employees reported that they were paid fairly in relation to their performance. But just 18 per cent felt that the organization offered outstanding rewards for outstanding performance. A majority of the employees reported that the organization provided a creative climate that stimulated their intrinsic motivation. This might be interpreted to mean that the tasks in many cases served as a basis for motivation. Table 3 gives the four measures of extrinsic motivation (a = 0.74) and the two measures of intrinsic motivation that were used in the study.
Internal Consistency of Value-Focused Thinking Questionnaire data showed that 71 per cent of the respondents felt that the aspirations and values of the organization were very clear to them (17 per cent reported in the unfavorable direction, and 88 per cent of the respondents had a clear understanding of the goals and objectives of the group (8 per cent reported in the unfavourable direction), and 75 per cent of the respondents indicated that they had a clear understanding of the goals and objectives of the organization (14 per cent reported in the unfavorable direction). Table 3 shows the nine measures of value-focused thinking used in the study (a = 0.84).
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Table 1. Factor Analysis of Perceptions of Items Included in the Study (Item no.) abbreviated items
Dialogue Valuebased focused evaluation thinking (values)
(1) Authority to do 0.26 my job well (2) Sense of 0.27 personal accomplishment (3) Adequate use of 0.13 recognition (4) Salary 0.03 compared to other organizations (5) Pay in relation 0.14 to performance (6) Rewards versus 0.11 performance (7) Input on -0.22 performance targets (8) Agreement on -0.42 development plan (9) Clear -0.19 performance targets (10) Communicating 0.65 clear vision (11) Objectives and 0.62 future direction (12) Supporting 0.81 individual (13) Personal 0.79 consideration (14) Effective 0.75 communication of ideas (15) Respecting 0.78 diversity and differences 0.69 (16) Encouraging personal development (17) Trust in team 0.77 capabilities (18) Openness on 0.74 feedback (19) Involvement in 0.59 planning of the team
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Intrinsic motivation1 & org. creativity
Valuefocused thinking (belief)
Extrinsic Controlmotivation based evaluation
0.07
0.40
0.22
0.07
-0.18
0.17
0.32
0.25
0.01
-0.14
0.12
0.30
0.04
0.54
-0.05
0.12
0.03
0.03
0.81
-0.04
0.06
0.06
0.08
0.78
-0.10
0.16
0.33
0.01
0.69
-0.05
-0.09
-0.02
-0.04
-0.06
0.83
-0.03
-0.14
-0.05
-0.09
0.49
-0.11
-0.10
-0.23
-0.11
0.77
0.11
0.11
0.37
0.13
-0.06
0.13
0.11
0.38
0.15
-0.09
0.03
0.14
0.09
0.07
-0.16
0.06
0.11
-0.01
0.06
-0.12
0.07
0.11
0.14
0.07
-0.06
0.10
0.15
-0.01
0.04
-0.05
0.08
0.14
0.06
0.06
-0.18
0.08
0.16
0.10
0.00
-0.10
0.08
0.12
0.01
0.06
-0.06
0.07
0.05
0.22
0.10
-0.10
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Table 1. Continued (Item no.) abbreviated items
(20) Understanding values (21) Supporting values (22) Inspired by values (23) Translating values to everyday work (24) Understanding team objectives (25) Understanding unit objectives (26) Understanding functional objectives (27) Understanding AZ objectives (28) Culture for new ideas (29) Time for testing new ideas (30) Culture for recognition (31) Culture where ideas can fail Initial eigenvalue Percent of variance Coefficient alpha for final scales
Dialogue Valuebased focused evaluation thinking (values)
Intrinsic motivation1 & org. creativity
Valuefocused thinking (belief)
Extrinsic Controlmotivation based evaluation
0.10
0.80
0.08
0.17
0.10
-0.09
0.10
0.82
0.11
0.07
0.09
-0.07
0.13
0.76
0.29
0.03
0.09
0.00
0.15
0.69
0.28
0.12
0.12
-0.05
0.31
0.07
0.09
0.69
-0.02
-0.20
0.16
0.20
0.17
0.78
0.04
-0.07
0.10
0.36
0.14
0.70
0.09
-0.02
0.03
0.65
0.04
0.36
0.16
-0.07
0.14
0.22
0.68
0.15
0.08
-0.10
0.10
0.15
0.74
0.06
0.16
0.00
0.17
0.13
0.71
0.05
0.30
-0.06
0.18
0.11
0.67
0.05
0.06
0.02
2.00 6.4 0.77(***)††
1.47 4.7 0.84(***)†
1.31 4.3 0.74(***)
1.15 3.7 0.77(***)
9.33 30.0 0.91(***)
3.26 10.5 0.85(***)†
Notes: Bold numbers indicate items forming the factor (varimax rotation, rotated factor pattern, N = 5333). ** =p < 0.01; *** = p < 0.001; (†) = Coefficient alpha (Cronbach) calculation is based on items 20 to 27; (††) Coefficient alpha (Cronbach) calculation is based on items 28 to 31; (1) r = 0.36** (Pearson).
Results Hypothesis Testing To test H1, we used regression analysis of creativity on the two types of evaluation (controlbased and dialogue-based). As seen in Table 4, dialogue-based evaluation was a significant (and the strongest) indicator of creativity. Control-based evaluation had a low and negative predictability of cre-
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ativity. The overall regression was significant (F = 514 , p < 0.01, R2 = 0.17). To test H2, we regressed extrinsic and intrinsic motivation on the two types of evaluations (control-based and dialogue-based). As shown in Table 4, both types of evaluation are significant indicators of extrinsic motivation, and the overall regression is significant (F = 247, p < 0.01, R2 = 0.09). The two types of evaluations are also significant indicators of intrinsic motivation, with a significant overall regression in
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Table 2. Dependent Variables, Items, Scaling, Mean and Standard Deviation Intrinsic motivation1 (agree) to 5 (disagree)
Item no.
Mean
Std.dev.
I have sufficient authority to do my job well My work gives me a sense of personal accomplishment
(1) (2)
1.89 1.98
1.03 1.07
Extrinsic motivation1 (agree) to 5 (disagree)
Item no.
Mean
Std.dev.
AstraZeneca makes adequate use of recognition other than money to encourage good performance From what I hear, our pay is as good as or better than the pay in other organizations in our industry I believe I am paid fairly in relation to my performance AstraZeneca offers outstanding rewards for outstanding performance
(3)
3.58
1.17
(4)
3.54
1.23
(5) (6)
2.89 3.54
1.35 1.08
Value-focused thinking1 (agree) to 5 (disagree)
Item no.
Mean
Std.dev.
My immediate manager involves me in planning the work of our team Regarding AstraZeneca’s overall aspiration and values: They are very clear to me I support them They inspire me I can translate these to my everyday work I have a clear understanding of the goals and objectives of: My team My local unit (e.g., manufacturing site, marketing company) My functional area (e.g., R&D, marketing, and operations) AstraZeneca
(19)
2.18
1.33
(20) (21) (22) (23)
2.30 2.08 2.78 2.86
1.08 0.90 1.08 1.12
(24) (25)
1.73 2.18
1.02 1.08
(26)
2.22
1.08
(27)
2.19
1.04
Organizational creativity1 (agree) to 5 (disagree)
Item no.
Mean
Std.dev.
AstraZeneca is establishing a climate/culture in which: New ideas are appreciated Time is invested for testing new ideas People receive recognition for innovation New ideas can fail without penalty to the originating person
(28) (29) (30) (31)
2.37 3.04 2.93 2.67
1.07 1.16 1.10 1.06
Note: n = 5,333.
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Table 3. Independent Variables, Items, Scaling, Mean and Standard Deviation Control-based evaluation(*)/5 (agree) to 1 (disagree)
(Item no.)
Mean
Std.dev.
The performance targets I have in my job have not been established with my input My manager and I have not agreed on a plan for my further development at work The performance targets I have in my job are not clear
(7)
1.69
1.02
(8)
2.74
1.45
(9)
1.78
0.94
Dialogue-based evaluation/1 (agree) to 5 (disagree)
(Item no.)
Mean
Std.dev.
(10)
2.53
1.29
(11)
2.29
1.15
(12) (13) (14) (15) (16)
1.85 1.74 2.21 2.00 1.83
1.10 1.06 1.25 1.33 1.04
(17) (18)
1.78 2.38
1.02 1.27
My immediate manager: Communicates a clear vision for the future role of our team Ensures that our short-term objectives are in line with future direction Supports me Is considerate of me as a person Effectively communicates his/her ideas Respects diversity/individual differences Encourages me to take responsibility for my own development Displays confidence in our team’s capabilities Is open to feedback on his/her own strengths and weaknesses Note: (n = 5,333). * These items and their corresponding scales were inverted.
Table 4. Results from Multiple Regression with Intrinsic Motivation, Extrinsic Motivation, Value-focused Thinking and Organizational Creativity as Dependent Variables, Standardized Coefficients (b) Dependent variables/ Indicator variables†
Intrinsic motivation
Extrinsic motivation
Value-focused thinking (values++belief)
Organizational creativity
Control-based evaluation Dialogue-based evaluation F Adjusted R2
0.13*** 0.39*** 659 0.20
0.14*** 0.20*** 247 0.09
0.15*** 0.33*** 575 0.18
-0.09*** 0.36*** 514 0.17
Note: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, n = 5,333. † r = -0.37 (Pearson), p < 0.01, between the indicator variables.
this case as well (F = 659, p < 0.01, R2 = 0.20). In agreement with H2, dialogue-based evaluation was found to be a better indicator of intrinsic motivation than control-based evaluation. Interestingly, much to our surprise dialogue-based evaluation was also a better indicator of extrinsic motivation than control-
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based evaluation, although the difference was not as significant. To test H3, we regressed value-focused thinking on the two types of evaluation (control- and dialogue-based). Table 4 shows that both types of evaluation were significant indicators of value-focused thinking and that the
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overall regression was significant (F = 575, p < 0.01, R2 = 0.18). In agreement with the hypothesis, dialogue-based evaluation proved to be a better indicator of value-focused thinking than control-based evaluation.
Analyses of Background Variables A series of multivariate analyses of variance (MANOVA) were made that compared levels of education (PhD or lower), gender, position (management or non-management) and different site affiliations (three Swedish R&D sites – SWE1, SWE2, SWE3 – and two British R&D sites – UK1 or UK2) with creativity, motivation and value-focused thinking (see
Table 5). Site affiliation had a significant effect on creativity (F = 16.6, p < 0.01) as did level of education (F = 6.7, p < 0.05); (see Table 6). Taken together, these findings suggest that site affiliation and education are factors that are important for the way in which respondents perceived the organizational climate for innovative thinking. With regard to site affiliation, Malnight’s (2001) study of Eli Lilly and Hoffmann LaRoche indicates that internal diversities between research centres (i.e. R&D sites) constitute one important factor that researchers should investigate to find emerging structural patterns in the organization. With regard to education it was found that employees who held post-graduate degrees (i.e. PhD)
Table 5. Means* for Dependent Variables versus Background Variables (R&D Sites, Education, Managerial Role and Gender) Dependent variables/ sample categories R&D sites Swe 1 Swe 2 Swe 3 UK 1 UK 2 Education PhD Other Management Manager role Non manager role Gender Female Male
Intrinsi cmotivation
Extrinsi cmotivation
Value focused thinking (values++belief)
Organisational Creativity
1.87 1.87 1.89 1.93 1.97
3.65 3.61 3.77 3.00 3.34
2.30 2.36 2.23 2.13 2.23
2.79 2.55 2.78 2.72 2.90
1.89 1.92
3.53 3.42
2.20 2.30
2.79 2.71
1.87 1.94
3.44 3.51
2.14 2.37
2.75 2.75
1.94 1.87
3.43 3.52
2.26 2.24
2.74 2.76
Note: * Estimated marginal means. Means refers to 1 (agree) to 5 (disagree), n = 5,333.
Table 6. Main Effects for Dependent Variables by R&D Sites, Education, Managerial Role and Gender Dependent variables/ sample categories
Intrinsi cmotivation
Extrinsi cmotivation
Value focused thinking(values++belief)
Organization creativity
R&D sites Education Managerial role Gender
ns ns ns ns
(0.000)*** (0.002)** (0.024)* (0.006)**
(0.000)*** (0.001)** (0.000)*** N.S
(0.000)*** (0.012)* N.S N.S
Note: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, ns = not significant, n = 5,333.
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perceived the creativity climate as less favourable than those who did not hold such a degree. The analyses further showed that level of education and R&D site affiliation had a significant effect on value-focused thinking (F = 8.4, p < 0.01 and F = 5.8, p < 0.01), as did management position (F = 73.4, p < 0.001). Gender had a significant effect on extrinsic motivation (F = 6.9, p < 0.01), and management position had a marginally significant effect (F = 4.6, p = 0.03). Another finding was that position had a significant effect on intrinsic motivation (F = 3.9, p < 0.005). Level of education, site affiliation and position are factors that matter most for valuefocused thinking, because these factors influenced how the organizational climate for innovative thinking was perceived. Employees with a post-graduate education generally perceived creativity climate as more favourable than those with a lower level of education. In the case of motivation, position alone appeared to be the key independent factor, regardless of whether motivation was extrinsic or intrinsic in nature. Level of education was not as important here.
Discussion In the present study, we tested whether different forms of performance evaluation (dialogue-based and control-based) affect different motivational factors (intrinsic and extrinsic) and perception of creative climate in AstraZeneca R&D. The results indicate that dialogue-based evaluation makes sense as a creative climate indicator in the pharmaceutical industry. From a methodological perspective, this study is subject to several considerations. First, items were not originally developed for this study, so the constructs developed – for example, dialogue-based evaluation and control-based evaluation – were not formalized or communicated in the organization at the time of the survey. Second, as a consequence, there is imbalance in the number of items in different constructs. But despite these considerations, we argue that the study represents an interesting opportunity to explore important issues relevant to creative climate in a specific organizational context that have potential to reveal what otherwise will remain hidden.
Effects from Type of Evaluation on the Creative Climate The study suggests that type of performance evaluation (dialogue-based or control-based
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evaluation) in a multinational pharmaceutical organization matters a great deal when accounting for a broad range of factors that are associated with judgements of creative climate. These factors include creative climate, intrinsic motivation, extrinsic motivation and value-focused thinking. Previous research has shown that type of expected evaluation (informational or control-based evaluation) has an effect on creative climate and intrinsic motivation in experimental settings (Shalley & PerrySmith, 2001). Accounting for these experimental findings, it was revealed that dialoguebased evaluation appeared to be a better indicator of creative climate than controlbased evaluation. Furthermore, our results on predictability capabilities of different types of evaluation (dialogue-based versus control-based) regarding different types of motivation (intrinsic and extrinsic) agree with recent findings on organizational learning versus performance goals (Elliott & Dweck, 1988; Heyman & Dweck, 1992). In line with our findings, the results of these studies suggest that factors of competence and ability are associated with reduced intrinsic motivation and that new skills are associated with enhanced intrinsic motivation. Our results indicate that dialogue-based evaluation has a relationship with intrinsic motivation. The results also suggest that dialogue-based evaluation at least marginally appears to better predict value-focused thinking than control-based evaluation. This finding lends support to the idea that value-focused thinking is a factor closely connected to creative climate (Keeney, 1992). The reason for this relationship may be that values have the ability to guide our decisions. Creative climate and productivity may thus be present in a search for new alternatives because values may be reformulated into objectives that are assumed to stimulate goal-directed behaviour. This may be achieved by employees being guided by the mindsets of the culture of the company. According to Malnight’s (2001) analysis of the work of Ely Lilly and Hoffmann LaRoche (pharmaceutical companies), mindsets can be made up of general company style, ways of doing things, values and common practices. But in many organizations, conditions for dialogue and learning in an organizational setting may be subject to mixed-message situations. Managers are in intense relationships with their superiors, and their careers and salaries depend on these relationships. So managers who will be evaluated in, for example, a leadership development process, are often trapped in situations in which their superiors
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and the intentions stated within the organization at large may express two different objectives. An organization’s general goals may preach open dialogue, learning and development, while business plans and individual performance evaluations are most often more results oriented. At the same time, this mixedmessage situation seems to be a ‘non-topic’ in many organizations. The manager might be unable (or unwilling) to comment on such a mixed-message situation being expressed, especially if he or she considers this to be a ‘non-topic’. Because of a fear of punishment, he or she cannot (or will not) reveal conflicting messages that are being sent out. Needless to say, mixed messages may in this way distort open dialogue, reflection and thus doubleloop learning (see Argyris & Schön, 1978, for a further discussion). Needless to say, managers may themselves be collaborating in such mixed messages to their R&D scientists.
Gender, Age, Position and Site-Affiliation Effects on Creative Climate This study revealed that level of education and position are factors related to employees’ judgements of how creative climate was perceived. It also found that these factors were related to value-focused thinking. In the case of motivation, position appeared to have a greater impact than education, regardless of whether motivation was extrinsic or intrinsic in nature. Clearly, our results indicate that both dialoguebased evaluation and control-based evaluation generally are able to reliably predict the variance observed in the dependent measures. But results of the ANOVA suggest that interactions between gender, age, and position and type of context exist and that these interactions may have a bearing on the dependent measures. From a practical view, these results suggest that HRM must take into account that it is easier to make managers more motivated in different development programmes by focusing on position-related factors than by focusing on issues related to higher education.
Generalizability At the theoretical level, it is interesting to note that we were able to replicate the findings of experimental studies in which expected evaluation was used as the independent variable (Shalley & Perry-Smith, 2001), even though this study used judgements of evaluation as the independent variable. An explanation for this similarity in results may be that experimentally manipulated expectations and real organizational judgements may work in the same way
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because they are based on the same kind of life experience. This experience may not necessarily have been gained in work life. Thus, in terms of external validity we argue that the study quite forcefully corroborate previously experimental results. On the internal side, all the applied measures also revealed a good internal consistency, which indicates a high degree of reliability. However, because of the explorative nature of the study, the conceptual validity of our constructs did not meet the highest set criteria with regard to all the included items (Hair et al., 1998). A possible explanation for this may be that the survey was developed by a professional survey organization that was using scientific methods, but without having any scientific aims. In the present study, creative climate was measured by letting the employees make selfratings of how they perceived the creative climate of the organization. For several reasons, this has been the most commonly used research method in the field of creative climate (see e.g. Amabile, 1995; Amabile et al., 1996; Ekvall & Ryhammar, 1999). A major reason for this is that it has been found that self-ratings of creative climate represent valid predictions of innovation in research-based organizations (see Ekvall, 1997). Use of this method of measuring creative climate strengthened the idea that creative climate is coupled with intrinsic motivation. In fact, the factor analysis results revealed that creative climate and intrinsic motivation were collapsed into the same factor that was also found by Shalley and PerrySmith (2001). This study discusses the influence of evaluation type on creative climate and creativityrelated behaviour, and perhaps in some environments, the level of creative climate allows (or even forces) managers to use less controlbased evaluations. This may have been the case in several reported ultra-creative phases of the studied organization; for instance, at the former Astra R&D site in Mölndal, Sweden between 1975 and 1985 when, according to official statistics, one of the best-selling drugs in the world was developed (Sundgren & Styhre, 2003). So we think that it is an important task for future research to investigate other more context-specific ways to explain correlations observed between evaluation type and level or quantification of creativity.
Implications for Practice At the practical and managerial level, this study has several implications relevant to pharmaceutical R&D. Based on results from this study, two important messages to HRM can be communicated:
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1. The dominating way to evaluate employees is based on a rather fixed apparatus of standardized measures derived from clear inputs (e.g. number and quality of patents or reports, meeting deadlines or other fixed performance aspects) and outputs much related to extrinsic motivation factors such as salary and promotion. So the concept of dialogue-based evaluation has potential for providing increased understanding of intrinsic motivation and for becoming a vehicle for a more elaborated view of motivation. 2. A clear message to management is to challenge the attitude toward the traditional notion of evaluation and to reflect on how to improve communication and knowledge exchanges in the organization. Dialoguebased evaluation can be seen as a tool for exploiting ideas and knowledge in the organization – an activity that otherwise might fall between stools. So dialogue-based evaluation promotes awareness of how to influence creative climate and support creativity. An organization faces several challenges when trying to put dialogue-based evaluation into practice: (1) it would require more time and effort than standardized methods; (2) it requires a new kind of organizational competence that involves behavioural change on the part of individuals and the managerial system; (3) it challenges the traditional transactional leadership model in the sense that it emphasizes relations and requires a more open exchange of ideas rather than just delivering according to fixed processes. So participants (managers and employees) would become more actively involved in providing information – thus creating an opening for exchanges of ideas and opinions and thus imposing a dialogue that questions organizational values and norms. In the day-to-day business, dialogue-based evaluation might take the form of more flexible relations between managers and individuals for discussing ideas (including visions, hopes, concerns and feelings) without relating to performance and output, and thus creating a balance between extrinsic and intrinsic motivation. The main message for management of the organization would be not to jump into a new change initiative before finding out what the key drivers are for intrinsic motivation. Drivers for intrinsic motivation are probably different for different parts of the organization. In the present case of AstraZeneca, representing large complex R&D organizations, it is
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important to acknowledge that creativity is under the influence of two major aspects; regulations and scientific breakthroughs. To drive creativity in such an organization, extrinsic motivation is not enough. Dialogue-based evaluation may be a new vehicle for managing different types of intrinsic motivation to promote creative climate. Like AstraZeneca, other large pharmaceutical R&D organizations are probably forced to deliver projects, products and services quickly and efficiently. Daily control and monitoring of organizational activities have become more detailed and sophisticated, while there are many attempts to empower employees and implement new organizational routines and standard operating procedures to improve the firm’s knowledge-based resources. Today, we speak of adhocracies, boundary-less organizations, post-bureaucratic organizations and the like. So there are two opposing forces at work: (1) organizational life is becoming increasingly managed, monitored and controlled and (2) newly developed managerial practices emphasize the need for commitment, coherence, and the ability to make use of one’s creativity and skills in the existing organizational activities. We argue that dialogue-based evaluation can bridge and reduce discrepancies between the assumed and politically correct culture versus the enacted and true culture and thus become one way to manage creativity in an age of management control.
Acknowledgements The authors thank Armand Hatchuel, Bård Kuvaas, Flemming Norrgren, Geir Overskeid, Tudor Rickards, and Rami Shani for valuable discussions and comments on previous versions of the paper.
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Ekvall, G. (1997) Organizational conditions and levels of creativity. Creativity and Innovation Management, 6(4), 195–205. Ekvall, G. and Ryhammar, L. (1999) The creative climate: its determinants and effects at a Swedish university. Creativity Research Journal, 12(4), 303– 10. Elliott, E.S. and Dweck, C.S. (1988) Goal: an approach to motivation and achievement. Journal of Personality and Social Psychology, 54, 5–12. Engeström, Y. (1999) Innovating learning in work teams: Analyzing of knowledge creation in practice. In Engeström, Y. Miettinen, R. and Punamäki, R-L. (eds.), Perspectives on Activity Theory. Cambridge University Press, Cambridge. Engeström, Y. (2004) New forms of learning in co-configuration work. London School of Economics Department of Informations Systems, London. Gardner, H. (1993) Creating Minds. Basic Books, Newbury Park, CA. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998) Multivariate Data Analysis. Prentice Hall, Upper Saddle River, NJ. Hargadon, A. and Sutton, R.I. (2000) Building an innovation factory. Harvard Business Review, 78(3), 157–66. Hennesey, B. and Amabile, T. (1988) The conditions of creativity. In Sternberg, R.J. (ed.), The Nature of Creativity. Cambridge University Press, New York. Heyman, G.D. and Dweck, C.S. (1992) Achievement goals and intrinsic motivation: their relation and their role in adaptive motivation. Motivation and Emotion, 16, 231–47. Hodgkinson, G.P. and Sparrow, P.R. (2002) The Competent Organization. The Open University Press, Buckingham. Horrobin, D.F. (2002) Effective clinical innovation: an ethical imperative. Lancet, 359, 1857–58. Keeney, R. (1992) Value-focused thinking: A Path to Creative Decisionmaking. Harvard University Press, London. Kirton, M.J. (1989) Adaptors and innovators at work. In Kirton, M.J. (ed.), Adaptors and Innovators: Styles of Creativity and Problem Solving. Routledge, New York. Koberg, C.S. and Chusmir, L.H. (1987) Organizational culture relationships with creativity and other job-related variables. Journal of Business Research, 15, 397–409. Ledbetter, W.N., Snyder, C.A. (1985) Assessing the organizational climate for OA implementation. Information and Management, 8(3), 155–70. Malnight, T.W. (2001) Emerging structural patterns within multinational corporations: toward process-based structures. Academy of Management Review, 44(6), 1187–1210. Payne, R. (1987) Individual difference and performance amongst R&D personnel: some implications for management development. R&D Management, 17, 153–66. Preskill, H. and Torres, R. (1999) The role of evaluative enquiry in creating learning organizations. In Easterby-Smith, M. Burgoyne, J. and Araujo, L. (eds.), Organizational Learning and the Learning
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Organization: Developments in Theory and Practice. Sage, London. Ryan, R.M. (1982) Control and information in the intrapersonal sphere: an extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450–61. Schein, E.H. (1983) The role of the founder in creating organizational culture. Organizational Dynamics, Summer, 13–28. Schein, E.H. (1985) Organizational Culture and Leadership. Jossey-Bass, San Francisco. Selart, M. and Boe, O. (2001) On practitioners’ usage of creativity heuristics in the decision process. In Allwood, C.M. and Selart, M. (eds.), Decision Making: Social and Creative Dimensions. Kluwer, Boston, pp. 197–210. Shalley, C.E. and Perry-Smith, J.E. (2001) Effects of social-psychological factors on creative performance: the role of informational and controlling expected evaluation and modeling experience. Organisational Behaviour and Human Decision Processes, 84(1), 1–22. Sundgren, M. and Styhre, A. (2003) Creativity – a volatile key of success? Creativity in new drug development. Creativity and Innovation Management, 12(3), 145–61. Tichy, N.M. and Devana, M.A. (1986) The Transformational Leader. Wiley, New York. Van der Heijden, K. and Eden, C. (1998) The theory and praxis of reflective learning in strategy making: In Eden, C. and Spender, J-C. (eds.), Managerial and Organizational Cognition: Theory, Methods, and Research. Sage, London. Woodman, R.W., Sawyer, J.E. and Griffin, R.W. (1993) Towards a theory of organizational creativity. Academy of Management Review, 18, 293–321. Zhou, J. (1998) Feedback valence, feedback style, task autonomy, and achievement orientation: interactive effects on creative performance. Journal of Applied Psychology, 83, 261–76.
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Mats Sundgren is a Senior Informatics Scientist, Clinical Science, AstraZeneca R&D Mölndal, Sweden. He has PhD a in Technology Management from the Fenix Research Program at Chalmers University of Technology, Sweden. He takes a special interest in management of organizational creativity in pharmaceutical R&D. E-mail:
[email protected]. Curt Bengtson is Associate Director and Head of Sourcing & Employer Branding, Sweden HR Centre, AstraZeneca, Södertälje Sweden. He has a PhD in Plant Ecophysiology from Göteborg University and is a parttime management consultant and coach with a special interest in how individual leaders change and develop their businesses and how they learn from their experiences. E-mail:
[email protected] Anders Ingelgård is Associate Director for the Health Economic and Quality of Life scientists in the UK and Sweden, Clinical Science, AstraZeneca R&D, Mölndal Sweden. He has a PhD in Psychology from Göteborg University. Main research activities include quality of life, survey design, stress management and change management. E-mail: anders.ingelgå
[email protected] Marcus Selart is Associate Professor in Organizational Behaviour at the Norwegian School of Economics and Business Administration. His interests include judgement and decision-making in organizations, applied creativity, management and cognition, expertise, justice in organizations and behavioural risk-taking. He is the author of several articles and books within these areas. E-mail:
[email protected]
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Blackwell Publishing Ltd.Oxford, UK and Malden, USACAIMCreativity and Innovation Management0963-16902005 Blackwell Publishing Ltd, 2005.March 2005141BOOK REVIEWSBOOK REVIEWSCREATIVITY AND INNOVATION MANAGEMENT
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Book Reviews Jan Verloop (2004), Insight in Innovation: Managing Innovation by Understanding the Laws of Innovation. Elsevier, Amsterdam. 168 pp, ISBN 0444516832.
The competitiveness of a company is linked to the ability of producing and transforming innovations into a market success. Jeroen van der Veer, Chairman of the Royal Dutch/Shell Group, sees innovation as a motor for the development of new and thrilling products and it is far more than just a Eureka moment (p. xi). It is how people think, work and deliver products, as well as a key business issue. But what is innovation and how can an innovation process be conducted? How can an innovation be managed and what is needed to successfully complete it? What benefit will evolve from an innovation and how can its design be sustainable? Jan Verloop, former Innovation Manager of Shell Global Solutions, gives answers to these questions. In total there are seven chapters, which illustrate the management of radical innovations in a large company from a practitioner’s point of view. After a short introduction into the history of innovation and its universal stages, different innovation models are introduced and interwoven with historical phenomena (Chapter 1). The fundamental terms of innovation are illustrated (Chapter 2), as well as the management of innovation (Chapter 3). The first three chapters form the first part of the book, which will help the reader to understand the innovation process and its management. The second part takes up specific topics that link innovation with other managerial themes. Entrepreneurship and innovation are tightly connected (Chapter 4) and the potential of an innovation may be derived from its value (Chapter 5). Sustainability is another specific topic that is addressed (Chapter 6). The book ends with a short reflection of a CEO’s role in the innovation process (Chapter 7). Verloop’s book is based on twelve basic patterns, the so-called ‘Laws of Innovation’. These patterns establish a sub-structure inside the chapters. Each chapter consists of at least one pattern. In the following, these patterns are used to illustrate the core statements. © Blackwell Publishing Ltd, 2005. 9600 Garsington Road, Oxford OX4 2DQ and 350 Main St, Malden, MA 02148, USA.
Pattern I: Innovation is the business process for creating new and insightful ideas and bringing them successfully to the market New ideas are the most important source of innovations and it is necessary to secure their market success. To get a deeper understanding of the term innovation itself, Verloop offers definitions from Schumpeter, Drucker and Shell (p. 6). In his opinion there are four key features of innovation: (i) insight, (ii) new combinations, (iii) entrepreneurship and (iv) adding value. Insight is more than just creativity; it is the comprehension of the technical merits and the requirements of the market. In addition to the insight, new combinations are needed, which can be made between different technologies or technologies and markets. An innovation is nothing without an entrepreneur, who supports the idea and takes the necessary steps to bring this idea to the market. The market alone determines the fate of an innovation; nevertheless a successful innovation offers the customer an additional value.
Pattern II: The innovation process has three distinct stages: ideation, development and investment; the prime requirement for Stage 1 is insight, for Stage 2 a champion and for Stage 3 an entrepreneur After noting the four key factors of an innovation, the question arises how the innovation process should be set up. Verloop defines three stages (pp. 7–8): (i) stage 1 – idea generation and crystallisation, (ii) stage 2 – development and demonstration, and (iii) stage 3 – investment and preparing for launch: i)
Stage 1 is a creative phase in which ideas are nurtured, turned around, combined,
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taken apart and reassembled (p. 7). Awareness of the market and customer needs should be gained and the values of stakeholders have to be measured. The main requirement for this stage, insight, is a key feature of innovation. As a result this stage produces a set of well-balanced ideas ready for further reflection in Stage 2. ii) Radical innovations are high-uncertainty and high-risk projects. One major task is to reduce the risk before moving on to the next stage in the innovation process. Adjustments between new findings, present budget customer value, cost, ecology and other limits have to be made. For efficient operation, a champion is necessary, who covers the project and accept responsibility for it. Main results of the champion’s work are a prototype as well as a sound business plan. iii) In Stage 3, the set-up of capabilities like marketing and sales potential, production and distribution capacities as well as a detailed business plan needs to be done. An entrepreneur secures the assignment of the necessary investment and the creating of capabilities for the launch of the product or service.
Pattern III: Innovation is opportunity-driven, an opportunity is a value-creating link between (potential) customer needs and (emerging) business and technological capabilities The three stages of the innovation process gave a short view on the main tasks and requirements to create a radical innovation. The background to why an innovation is created needs to be examined. Verloop provides the reader with two basic innovation models: (i) the classical innovation model and (ii) the bridge-building model of innovation (pp. 11–17). The classical innovation model is technology-driven and arranged sequentially from a science domain, a technology domain and a business-to-a-society domain. In contrast to this classical innovation model, Verloop offers a bridge-building model of innovation according to Schumpeter. This model starts with the needs of the market place and is strongly linked to the capabilities and strengths of the company. An innovation does not necessarily begin in a laboratory or the marketing department. It is opportunity driven, and is based on a multi-
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disciplinary approach with teamwork as an integral element.
Pattern IV: Innovation management distinguishes only two types of innovation: inside-the-box and outside-the-box, based on whether the pathway to the customer is known at the start So far, the concept of innovation has been separated into three segments: key factors of innovation (Pattern I), stages of the innovation process (Pattern II) and innovation models (Pattern III). Now that the reader knows the basic patterns of innovation, the question about how to manage an innovation arises. To manage an innovation efficiently, knowledge about its configuration is needed. Verloop divides innovation, according to the customerorientated approach of Ansoff, into two types: (i) inside-the-box and (ii) outside-the-box innovation (pp. 22–26). An incremental innovation managed and funded by a business unit is called inside-the-box innovation. This is a must-do activity that can be handled without any help from outside the company. It simply operates at product-strategy level and does not produce much attention. In contrast, the outside-the-box innovation is game-changing. This radical innovation is a strategic option for the company and has to be integrated into the company strategy. It is steered and funded corporately, and needs to be developed in co-operation with strategic partners.
Pattern V: Outside-the-box innovation requires external partners with complementary capabilities to find and develop the best route to the customer Outside-the-box innovation offers a variety of options that may highly affect a company’s success (p. 27). This type of innovation holds a high growth potential and a significant impact on the bottom line. Nevertheless, it is of high risk, and this type of innovation can seldom be created without the help of external partners. To produce a radical innovation, external partners with complementary capabilities have to be identified and a win-win situation has to be created. Trust and confidence in the partner involved is a basis to keep the co-operation fair. In addition there have to be activities to secure the best route to the potential customer.
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Pattern VI: Diversity is essential to stimulate creativity, creativity needs time and analyses to mature into insight, innovative ideas are based on insights Going back to Stage 1 of the innovation process draws attention to the necessary environment for growing innovative ideas. Stage 1 comprises of generation, capturing and nurturing of ideas. To produce this kind of ideas, creativity is needed. It is embedded in ideation partners such as: (i) grassroots, (ii) experts, (iii) management, (iv) customers, (v) external stakeholder and (vi) partners and alliances (p. 50). They might give different input into the ideation process and produce the diversity needed.
Pattern VII: Managing risks and learning from failures are key factors for success in radical innovation Sources for radical innovations are identified, but how can a radical innovation prosper? An innovation culture is required (pp. 68–69). This means a combination of innovation structure with good management practices. One major aspect is a blame culture, which regards stopping a project in time as success and sees failure as an opportunity to grow. Risks in an innovation project have to be appreciated not only in the innovation team but also throughout the whole company as a key factor of success.
Pattern VIII: Entrepreneurship is the scarcest resource in game-changing innovation Pattern VIII marks the transition from Stage 2 to Stage 3 in the innovation process – from development and demonstration to investment (pp. 73–74). This transition needs a change in commitment. To keep the option for a business opportunity that evolves from the outside-the-box innovation has to be secured by a risk-taker. An entrepreneur is a very scarce resource, but increases the probability of commercial success and financial commitment.
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value of an innovation lays in the creation of new business options as well as offering the customer new choices. To value an innovation project, Verloop introduces three values domains: (i) innovation domain, (ii) customer domain and (iii) strategy domain (p. 92). Value derived from the potential business value is assessed in the innovation domain. Needs and purchasing power of the customer is reviewed in the customer domain. The last value domain is based on strategy information. The value is measured on how the company will extract value from the customer domain.
Pattern X: The purpose of innovation is to create desired, valuable change The author arguments negatively (p. 118): ‘If you don’t want change, don’t innovate’. A valuable change can be made if the new products are able to unfold value. This may only happen if they fit into the future. To ensure this fit, sustainability has to be taken into consideration. There is a direct and significant link between innovation and sustainability. Both aspects are orientated towards change and future. Not regarding sustainability may lead to useless innovations.
Pattern XI: Sustainable innovation has three drivers – technology, business and society – that need to be in balance to create new choices for the customers of today, without compromising the options for the future Sustainability as key factor for a valuable change puts a challenge to radical innovation projects (pp. 124–25). Regarding three value drivers, technology, business and society, the sustainable innovation has to face three basic challenges. It must: • create options that do not interfere with the prevailing system of society; • unfold equitable value to customers and stakeholders; • fit in the supporting ecosystem. Disregarding these challenges may lead to failure and rejection from the customer’s side.
Pattern IX: The value of innovation is in the creation of new options for the company and new choices for the customer
Pattern XII: There is no successful innovative company without a committed CEO
The entrepreneur is needed to promote and patronise a radical innovation. In addition, the
Innovation projects need a strong leader, who is willing to steer, to protect and to set rules.
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The CEO of a company should carry out these main functions (p. 137). The CEO’s goal should be to select preferred domains for innovation, and decide how and when to use them. The CEO should safeguard the overall innovation effort and ensure the space and infrastructure for radical innovations. He should be the promoter and pacemaker for innovation projects, which result in a successful and innovative company.
experience, which provides a well-rounded theoretical framework. This framework is accompanied by several very valuable examples from Shell, which contribute to the high value of the book. No recipes for success are offered, but a large number of important hints on how to successfully manage innovation are given. This book is a must-have for practitioners, and also provides a good basis for students and academics in innovation management.
Recommendation: Readable – a musthave for practitioners
Anja Geritz Institute for Project Management and Innovation University of Bremen
[email protected]
The reader will find a practical and inspirational book with a focus on hands-on
Stefan H. Thomke (2003), Experimentation Matters: Unlocking the Potential of New Technologies for Innovation. Harvard Business School Press, Boston, MA. 307 pp, ISBN 1-57851-750-8, US$23.80. Experimentation is an important activity within an innovation process and its design is fundamental for the learning and knowledgebuilding process. Experimentation supports the development and improvement of products, processes, systems and organizations. There are two forms of experimentation. The traditional experimentation consists of using real-world objects, and its usually high costs often limit innovation. In contrast, the new experimentation is based on new technologies such as computer modelling and simulation. It seems to lower the high costs of traditional experimentation. Stefan H. Thomke, associate professor of technology and operations management at the Harvard Business School, deals with the questions of why and how new technologies are transforming innovation processes. He describes in detail the power of new experimentation, but also stresses the necessity to manage, organize and structure the innovation process. Therefore, experimentation will not be seen as an isolated phenomenon, but as a part of a larger organizational effort toward innovation. The author divides his book ‘Experimentation Matters’ into two parts. Part I, ‘Why Experimentation Matters’, builds the intellectual foundation (Chapters 1 to 4) of why experimentation matters, where it matters and how new technologies have the potential to transform innovation today and in the future. Thomke shows impressively how new technologies can amplify the impact of learning, and how important it is to manage experimentation. In Part II, ‘Unlocking Potential by Managing Experimentation’, (Chapters 5 to 7)
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he proposes that experimentation can be managed effectively by six principles. These principles lead managers to unlock the potential of experimentation technologies and embed them into their organization. The two parts are framed by a motivating introduction and an incisive epilogue, as well as a comprehensive bibliography. All of the chapters are illustrated by various tables and figures and accompanied by informative notes as well as sources of literature. This fact contributes to the value of the book, which is interesting both for academics and practitioners.
Part I: ‘Why Experimentation Matters’ The use of experimentation has been well known for a long time. In his magnum opus, The Logic of Scientific Discovery (published in 1934) Karl Popper described science as being based on hypotheses, which may be tested through experiments and can turn out to be false. Such falsification contributes to the progress of scientific insight – the old assumption was wrong and a better hypothesis might be posed. Stefan H. Thomke also discusses the importance of experimentation in detail in the first part of his book. He shows that, traditionally, the high cost of experimentation limits innovation. Statistical methods and new technologies such as computer simulation and modelling are lifting the cost constraints by changing the economics of experimentation. But these technologies are not only slashing cost and time, but also making possible what-
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if experiments that have been either prohibitively expensive or nearly impossible to carry out before. Therefore managers must not only understand the power of new technologies for experimentation, but also how they impact the processes, organization and management of innovation. Thomke shows with an illustrative example, how new technology platforms for experimentation can form the basis for fundamentally rethinking innovation processes. He describes in detail, for the integrated circuit industry, how these new technology platform work. In this industry, an important part of an experimentation strategy is switching between different modes to maximize learning from experimentation. Within the scope of the first part, Thomke characterizes the basics of any experimentation process as a four-step iterative cycle: design, build, run and analyse. He indicates that seven factors enhance the power of experimentation and influence the innovation process: fidelity, cost, iteration time, capacity, strategy, signal-to-noise ratio and type of experiment. New technologies for experimentation in particular, such as simulation, computer modelling and combinatorial technologies, have a very significant impact on all these factors. They accelerate the process of experimentation and as a consequence, managing experimentation becomes more important than ever before.
Part II: ‘Unlocking Potential by Managing Experimentation’ Based on the intellectual foundation in Part I, in Part II Thomke summarizes six principles for managing experimentation. He explains how to unlock the potential of new experimentation technologies and how to accommodate them into an organization. The six principles are: (1) Anticipate and exploit early information through ‘front-loaded’ innovation processes. Thomke shows that experimenting often and at an early stage is very important to avoid high expense of late-stage failures, as they can emerge in cars, aircraft, drugs and software development. Early experimentation is essential for creating a lot of ideas and concepts, which will ultimately result in better products and services. At this stage new technologies are very powerful to test what will work and what will not work as early as possible. Thomke describes very impressively examples from Microsoft, Boeing and Toyota. All of these companies saved huge amount of money through early experimentation.
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(2) Experiment frequently but do not overload your organization. A lot of experiments generate massive amounts of information, which has to be processed, evaluated, understood and used in planning of further experiments. This activity can overload an organization. Therefore managers need to prepare their organization for the full effects of more frequent experimentation, particularly in ‘front-loaded’ innovation processes. A good experimentation strategy is necessary. Thomke gives an answer and lists different possibilities for lowering the risk of organizational overload: rapid information transfers between groups, a focus on quick decision making and the development of new tools (e.g. in bio-informatics for drug discovery). (3) Integrate new and traditional technologies to unlock performance. ‘Integrating’ is the keyword for combining new and traditional technologies. Integrating enhances overall performance, while also enjoying the benefits of cheaper and faster experimentation. Thomke discusses a successful example of ‘integrating’ by the German car company BMW, gaining strategic product advantages. BMW speaks of remarkable opportunities for process change and productivity improvement through clever combining of new and traditional technologies in the product development processes. (4) Organize for rapid experimentation. The ability to experiment quickly is integral to innovation, and rapid feedback is important for effective learning. Quick experiments will influence decisions and as a consequence the organization must also be capable of responding quickly. Thomke shows how rapid experimentation by BMW resulted in fundamentally new insights that made their cars much safer. (5) Fail early and often but avoid ‘mistakes’. There is a difference between failures and mistakes. Experiments resulting in failures are not failed experiments, because they generate new information. In contrast, mistakes refer to wrong actions that result from poor judgement or inattention. Therefore early failures are necessary in order to tap into the potential of new experimentation technologies, because they can result in learning. But mistakes should be avoided, because they produce only little new or useful information and are therefore without value. (6) Manage projects as experiments. Thomke suggests a different way of thinking about experimentation. Projects can be conceived of as experiments themselves.
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When projects become experiments, they require careful planning and strategic investment into an infrastructure that supports experimentation for learning. It is helpful to use the same seven factors enhancing the power of experimentation (see above). This will drive learning within projects and hence a methodology for continuous improvement of a development process is provided. After describing and introducing the six principles of managing experimentation within companies, Thomke concludes his book with the exploration of how these new capabilities, and technologies such as computer simulation, can be leveraged beyond organization (e.g. collaborating with suppliers and customers). He shows how some companies
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putting experimentation technologies into the hand of customers results in faster development of new products and services.
Recommendation Experimentation Matters is an exciting and instructive book for everyone interested in innovation management. Read this book and you will be able to manage your own projects as experiments. Dr. Lothar Walter Institute for Project Management and Innovation University of Bremen
[email protected]
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