Contribution to the design of a matrix to analyse and classify problem solving methods according to performance criteria

Diploma Thesis, 2006

204 Pages, Grade: 1,0


Table of contents

1. Framework and context of the work
1.1. Objective and structure of the work
1.2. Keywords:
1.3. On the necessity of an analysis and classification matrix – context of the work
1.4. Limitations of this work
1.5. Problem, complexity and innovation – elaboration on central notions
1.5.1. Problems, problem solving and Problem Solving Methods
1.5.2. Complex systems, complexity and systems thinking
1.5.3. Innovation and invention

2. Problem Solving Methods (PSM)
2.1. Analysed and compared methods
2.1.1. Axiomatic Design
2.1.2. Quality function deployment (QFD)
2.1.3. Robust Design, Taguchi Method
2.1.4. Systematic Approach of Pahl and Beitz (SAPB or P&B)
2.1.5. TRIZ – Theory of Inventive Problem Solving
2.1.6. Value Engineering (VE)
2.2. Short suggestion of some other interesting methods
2.3. Positioning of the methods in the problem solving process or innovation process making use of the W-model

3. Development of criteria for classification, analysis and evaluation of Problem Solving Methods
3.1. Performance and characteristic of Problem Solving Methods
3.2. Criteria and characteristic to describe the problem

4. Development of the analysis and comparison matrix
4.1. Example problems and their solution concepts
4.1.1. Convey glass disc problem
4.1.2. Disperse varnish problem
4.1.3. Glass polishing problem
4.1.4. Powder Injection Moulding (PIM) problem
4.1.5. Tea ball problem
4.1.6. Supplemental problem
4.2. Evaluation of the performance of Problem Solving Methods
4.2.1. Validation of Axiomatic Design
4.2.2. Validation of QFD
4.2.3. Validation of Robust Design
4.2.4. Validation of the Systematic Approach of Pahl and Beitz
4.2.5. Validation of TRIZ
4.2.6. Validation of Value Engineering
4.3. Overall Analysis and Classification Matrix
4.3.1. Composition of the overall analysis and classification matrix
4.3.2. Depiction of the AC-matrix
4.3.3. Proceeding to read the matrix statement

5. Validation of the analysis and comparison matrix concept
5.1. Outline for the solution of a final example problem
5.2. Validation example problem
5.2.1. Description of the problem: Development of car tires for all-year usage
5.2.2. Statement of the matrix
5.2.3. Solution concepts
5.2.4. Conclusions
5.3. Validation of the concept with the help of the Validation Square
5.3.1. The Validation Square
5.3.2. Application of the Validation Square concept on the ACM

6. Conclusion, outlook and summary
6.1. Review of critical points and difficulties
6.2. Outlook
6.3. Summary

List of literature

Appendix A

Recommendations for the fast reader:

For an overview on the thesis the fast reader is recommended to glance over chapters 1 and 3 and to read chapters 4.3, 6.2 and 6.3.

List of abbreviations

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List of figures

Figure 1: Consultancy wisdom /54/

Figure 2: Current research directions concerning improvement of Problem Solving Methods

Figure 3: Domain model of Axiomatic Design /34/

Figure 4: Zigzagging in Axiomatic Design /34/

Figure 5: Illustration for the Information Axiom /54/

Figure 6: House of Quality /102/

Figure 7: Evolution of quality control /119/

Figure 8: Quality Loss Function /61/

Figure 9: Stages in the design cycle /119/

Figure 10: P-diagram

Figure 11: Design process according to SAPB, simplified version /36/

Figure 12: Solution process of TRIZ

Figure 13: A simple S-field, composed of a field and two substances

Figure 14: Thinking in time and space: 9-boxes draft

Figure 15: W-model classifying different stages in innovation process /111/

Figure 16: Solution level of example problems: conceptual level

Figure 17: P-model of the convey glass disc problem

Figure 18: Possible conveyor for hot material

Figure 19: Black Box depicting of convey glass disc problem

Figure 20: Principles 15 and 35 /4/

Figure 21: Sketch of a dispersion bowl /96/

Figure 22: P-model of the disperse varnish problem

Figure 23: Black Box depicting of disperse varnish problem

Figure 24: Su-field model of varnish dispersion

Figure 25: P-model of the glass polishing problem

Figure 26: Functional diagram of polishing glass lenses

Figure 27: Black Box depicting of polish glass lens problem

Figure 28: PIM process

Figure 29: P-model of the PIM problem

Figure 30: Screenshot of a web-based function catalogue /73/

Figure 31: Patent research on espacenet /71/

Figure 32: Black box depicting of PIM problem

Figure 33: P-model of tea brewing with tea ball

Figure 34: Patented tea ball (US patent D 439,470 S) /71/

Figure 35: „Dressman“ ironing apparatus /84/

Figure 36: Different design solutions for irons

Figure 37: Composition of the AC-matrix: W-part, C-part and P-part

Figure 38: Presentation of the overall AC-matrix

Figure 39: Steps to read the AC-matrix statement

Figure 40: Validation Square /68/

Figure 41: Possible method combination in overall innovation process /115/

List of tables

Table 1: Examples for complex systems /10/

Table 2: General Axiomatic Design matrix /34/

Table 3: Possibilities of coupling in Axiomatic Design /34/

Table 4: 39 Technical Parameters of TRIZ

Table 5: 40 Inventive Principles of TRIZ

Table 6: Extract of the Altshuller Matrix

Table 7: Funtional Analysis Table

Table 8: Development of VE

Table 9: Positioning of PSMs in innovation process according to W-model

Table 10: Characteristics of example problems used for method evaluation

Table 11: Characterisation of convey glass disc problem

Table 12: Design matrix of the convey glass disc problem

Table 13: Planning array for experiments on conveying glass disc

Table 14: Problem analysis in time and space (convey glass disc)

Table 15: Characterization of disperse varnish problem

Table 16: Design matrix of the disperse varnish problem

Table 17: Planning array for experiments on dispersing varnish

Table 18: Problem analysis in time and space (disperse varnish)

Table 19: Characterization of glass polishing problem

Table 20: Planning array for experiments on polishing glass lenses

Table 21: Requirement list for glass polishing

Table 22: Problem analysis in time and space (glass polishing)

Table 23: Functional analysis of glass polishing

Table 24: Component function cost table for glass polishing

Table 25: Results of the brainstorming session on glass polishing

Table 26: Characterisation PIM problem

Table 27: Planning array for experiments on PIM

Table 28: Problem analysis in time and space (PIM)

Table 29: Characterisation of tea ball problem

Table 30: House of Quality of tea ball problem

Table 31: Requirements for an improved tea ball

Table 32: Br@inwriting results for an improved tea ball design

Table 33: Problem analysis in time and space (tea ball)

Table 34: Component function cost table for tea ball

Table 35: Characterisation of iron shirt problem

Table 36: Voice of Customer Table (Iron shirt problem)

Table 37: Design matrix of the ironing shirt problem

Table 38: Component function cost table for ironing shirts

Table 39: Methods used to solve example problems (marked in grey)

Table 40: General performance of Axiomatic Design

Table 41: Problem-specific performance of Axiomatic Design

Table 42: General performance of QFD

Table 43: Problem-specific performance of QFD

Table 44: General performance of Robust Design

Table 45: Problem-specific performance of Robust Design

Table 46: General performance of SAPB

Table 47: Problem-specific performance of SAPB

Table 48: General performance of TRIZ

Table 49: Problem-specific performance of TRIZ

Table 50: General performance of Value Engineering

Table 51: Problem-specific performance of Value Engineering

Table 52: Attribution of rating credits depending on the degree of congruency

Table 53: Example for the calculation of a rating sum

Table 54: Tire requirements /47/

Table 55: Characterisation of validation example problem

Table 56: Congruency check between method performance and problem character

Table 57: Voice of the customer table for all-year tires

Table 58: Component function cost table for all-year tires

Contribution to the design of a matrix to analyse and classify Problem Solving Methods according to performance criteria

- How to choose the fitting Problem Solving Method for a specific problem-

Contribution à l’élaboration d’une matrice d’analyse et de classification dédiée aux méthodes de résolution de problèmes selon des critères de performance

Beitrag zur Erstellung einer Matrix zur Analyse und Klassifizierung von Problemlösungsmethoden nach Leistungskriterien

1. Framework and context of the work

Cadre et contexte du travail

Rahmen und Kontext der Arbeit

1.1. Objective and structure of the work

Nowadays products get more and more complex, product life cycles tend to become shorter, knowledge within a company is huge, but distributed…– Facing this industrial background ways and means have to be found and described to master the situation and to stay competitive.

When it comes to idea creation, solution of design problems and creation of innovations a broad range of methods exists: Axiomatic Design, Quality Function Deployment, Robust Design, Systematic Engineering Design, TRIZ, Value Engineering, etc.

Problem in this context has to be differentiated from a mere task where a standard solution approach is known and applied.

In this work the most frequently used methods are to be identified, described and evaluated. Special attention is paid to the method TRIZ (Theory of Inventive Problem Solving).

For method analysis and comparison and to facilitate the problem-driven choice between the methods, criteria have to be described to position the method within the innovation process, to evaluate the performance of the applied method and to characterize the problem. Different example problems have to be solved by the use of the methods in order to evaluate their performance.

In this work the new approach to assess problem-specific method performance is stressed.

Finally the consistent assessment has to provide a matrix showing the methods’ strengths and weaknesses. Thereby hints for integrating and combining ideas of single methods might be gained. The concept of a comparison and analysis matrix has to be justified by a final case.

The outline of this report is as follows: In chapter 1 the necessity of an analysis and classification matrix is explained and central notions are presented. In chapter 2.1 the most currently used methods are introduced. The different stages of an innovation or problem solving process are presented in chapter 2.2 and methods positioned within this process, followed by a description of possible criteria to classify methods in chapter 3. Example problems are solved in chapter 4. Based on literature research and the gained insights when working on example problems, the Problem Solving Methods are classified according to their characteristics and evaluated according to their performance to solve specific problems. This chapter is summarized by a matrix displaying the findings. One exemplary problem follows in chapter 5 allowing some conclusions on validity of the findings. A summary and outlook in chapter 6 conclude this work.

Objectif et structure du travail

De nos jours on peut constater que les produits deviennent de plus en plus complexes, que le cycle de vie produits diminue, que la connaissance dans une entreprise est immense mais dispersée… - Pour maîtriser cette situation et pour rester compétitif, des méthodologies doivent être trouvées, décrites et appliquées.

Quant à la génération d’idées, la résolution des problèmes de construction et l’innovation, des méthodes diverses existent : Axiomatic Design, Quality Function Deployment, Robust Design, Systematic Approach of Pahl and Beitz, TRIZ, Analyse de Valeur, etc.

Dans ce travail des méthodes usitées et courantes sont identifiées, décrites et évaluées, avec une attention particulière par la méthode TRIZ (Théorie de la Résolution des Problèmes d’Innovation).

Pour rendre possible une comparaison et pour faciliter le choix entre les méthodes, des critères doivent être décrits. Cela sert à positionner la méthode dans le processus d’innovation, à évaluer la performance de la méthode générale et la performance liée au caractère du problème. Des problèmes exemplaires doivent être résolus par les méthodes pour juger leur performance.

La nouveauté de ce travail, évaluer la performance des méthodes liées au caractère du problème, est soulignée.

Finalement les évaluations des méthodes doivent être rassemblées dans une matrice qui présente les points forts et faibles des méthodes.

Le concept de la matrice d’analyse et de classification doit être validé par un problème exemplaire final.

Description de la structure du rapport:

Dans le chapitre 1 la nécessité d’avoir une matrice d’analyse et de comparaison et des notions centrales sont expliquées. Les méthodes les plus fréquemment utilisées sont présentées dans le chapitre 2.1. Dans le chapitre 2.2 les étapes différentes des processus d’innovation ou de résolution de problèmes sont présentées et les méthodes sont positionnées selon ces étapes. Dans le chapitre 3 des critères pour classifier et évaluer les méthodes sont décrits. Des problèmes exemplaires sont résolus dans le chapitre 4.1. La classification et l’évaluation des méthodes de résolution de problèmes (chapitre 4.2) sont basées sur une étude bibliographique et des connaissances gagnées pendant la résolution des problèmes. Le chapitre est résumé par la matrice d’analyse et de classification. Dans le chapitre 5 un problème final permet de conclure sur la validité du concept. En vue du travail effectué, un résumé et une ouverture sur ce thème est proposé en conclusion dans le chapitre 6.

Zielsetzung und Struktur der Arbeit

Heutzutage werden Produkte immer komplexer, Produktlebenszyklen immer kürzer, das Wissen im Unternehmen ist immens, aber verstreut… - Um wettbewerbsfähig zu bleiben und die Herausforderungen des Marktes anzunehmen, müssen Methoden gefunden, beschrieben und angewandt werden.

Um Ideen zu finden, Konstruktionsprobleme zu lösen oder Innovationen zu schaffen gibt es zahlreiche Methoden: Axiomatic Design, Quality Function Deployment, Robust Design, Methodisches Konstruieren, TRIZ, Value Engineering, etc.

In dieser Arbeit werden die gebräuchlichsten Methoden identifiziert, beschrieben und bewertet. Besondere Aufmerksamkeit kommt dabei der TRIZ-Methode zu (Theorie des erfinderischen Problemlösens).

Im Rahmen dieser Arbeit muss dabei ein Problem von einer bloßen Aufgabe unterschieden werden. Bei einer Aufgabe ist der Lösungsweg standardisiert und vorgegeben.

Um einen Methodenvergleich und eine Methodenauswahl in Abhängigkeit vom Problem zu ermöglichen, müssen Kriterien definiert werden: Diese Kriterien dienen dazu, die Methode den verschiedenen Phasen des Problemlösungsprozesses zuzuordnen und die Methodenleistung zu bewerten. Um die Methodenleistung auch problem-spezifisch bewerten zu können, müssen auch Kriterien zur Charakterisierung des Problems bestimmt werden.

In dieser Arbeit werden verschiedene Beispielprobleme mit den Methoden gelöst, und dadurch die Methoden-Leistungsfähigkeit bewertet. Betont wird dabei der neue Ansatz, die Methodenleistung ausgehend von den Problemeigenschaften zu bewerten. Am Ende soll dem Konstrukteur eine Matrix zur Verfügung gestellt werden, welche die Stärken und Schwächen der Methoden veranschaulicht und eine gezielte Methodenauswahl ermöglicht.

Dadurch können auch neue Ideen für die Kombination verschiedener Methoden gewonnen werden.

Das Konzept der Vergleichs- und Analysematrix wird zum Schluss durch ein Fallbeispiel bestätigt.

Dieser Bericht ist wie folgt strukturiert:

Nachfolgend wird in diesem Kapitel der Kontext dieser Arbeit präsentiert und es werden zentrale Begriffe erklärt. In Kapitel 2.1 werden die am häufigsten verwendeten Problemlösungsmethoden vorgestellt. Diese werden in Kapitel 2.2 den verschiedenen Phasen eines Innovations- oder Problemlösungsprozesses zugeordnet. In Kapitel 3 werden Kriterien zur Klassifizierung der Methoden vorgeschlagen. In Kapitel 4 wird die Vergleichs- und Analysematrix entwickelt. Dazu werden zuerst Beispielprobleme gelöst. Die dabei gewonnenen Erkenntnisse führen - ergänzt durch eine Literaturrecherche - zur Klassifizierung der Methoden und zur Bewertung ihrer Eignung, ein bestimmtes Problem zu lösen. Den Abschluss des Kapitels bildet eine Darstellung der Matrix mit allen Ergebnissen.

Betrachtungen zur Gültigkeit des Konzeptes werden in Kapitel 5 an hand eines Validierungsbeispieles entwickelt.

Eine kurze Zusammenfassung und ein Ausblick beschließen in Kapitel 6 diese Arbeit.

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1.2. Keywords:

innovation, problem solving methods, TRIZ, evaluation criteria, method comparison

innovation, méthodes à résoudre des problèmes, TRIZ, critères d’évaluation, comparaison des méthodes

Innovation, Problemlösungsmethoden, TRIZ, Bewertungskriterien, Methodenvergleich

1.3. On the necessity of an analysis and classification matrix – context of the work

Sur la nécessite d’une matrice d’analyse et de classification – contexte du projet

Über die Notwendigkeit einer Analyse- und Klassifizierungsmatrix – Kontext der Arbeit

Enormous numbers of books have been written on hundreds[i] of Problem Solving Methods (PSM), numerous conferences have been hold as well. But still application in industry to promote innovations and tackle intricate problems lacks.

Consultants may be hired to advice on innovation and problem solving techniques, see Figure 1, not necessarily leading to better problem solving performance or a better method choice.

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Figure 1 : Consultancy wisdom /54/

So, what is the challenge when it comes to Problem Solving Methods? To answer this question, the elements involved in the problem solving process shall be regarded first.

- Elements:

Knowledge: Knowledge, experience and know-how in a company or in a business sector are immense. To give good solution support, this knowledge has to be made accessible via methodology.

Method: A PSM is composed of various tools and suggests a course of action, i.e. a systematic approach in steps to develop a solution. To cover the whole process of problem solving in product development it is common practice to combine methods.

Tools: The tools to handle and treat the problem are in direct contact with the problem.

Men: Finally there are men acting as problem solver and the problem itself.

Each element and the interfaces in-between have some critical aspects. Those aspects determine current research activities on PSMs and the way to improve problem solving.

- Ways of improvement:

Knowledge: The access to knowledge is incomplete. Even though it is impossible to give access to all existing knowledge, it can be improved, e.g. by the application of databases and knowledge management.

Method [ii]: The method might be not powerful or unsuitable to solve a specific problem. Therefore either new methods are created, two existing methods combined or super[iii] -methods developed, containing tools of various methods.

Tools: Tools lack solution strength. So tools are improved e.g. by adapting them to new economic demands or by integrating computer support.

In this work a fourth way is suggested: to start from the problem itself, see Figure 2. A central question is how to determine which Problem Solving Method supports the

companies’ needs adequately and fits the best with the problem’s nature.

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Figure 2 : Current research directions concerning improvement of Problem Solving Methods

Most business actors complain that methods characterization and application possibilities are unclear /22/. To respond to the absence of clearness at that point, this work shall give some assistance by an analysis and classification of PSMs according to their problem-specific performance. A positioning and evaluation of such kind, taking into account the problem’s nature, is supposed to be new. Also Ehrlenspiel states the absence of a complete “method construction kit”[iv], a decision aid taking into account the nature of the problem, the methods performance and its application phase /21/.

1.4. Limitations of this work

Limites du travail

Begrenzung der Arbeit

Problems occur in all spheres of life: in economy, in our personal life, in technology, in design etc.

“Design has many meanings to different people. These include the conception of a new process, a new product, a new use of a physical effect, the preparation of detailed drawings or tools for the workshop, a manufacturing plan, or even a marketing strategy. Design is involved in essentially all engineering activities. Also, the elements of creativity and innovation are involved in all types and levels of design whether it is design for improved reliability and functional life, improved aesthetics, reduced cost, improved ergonomics, or manufacturing methods.” /52/

In this work technical problems related to physical products’ design are regarded. Problems that are merely related to processes, functions or customer benefits shall be excluded.

When having to deal with any problem the use of creativity methods is quite common. However, creativity methods like brainstorming have one crucial drawback: psychological inertia. Solutions that are developed in a creativity session are depending on the professional background of the creativity team and therefore lacking objectivity. Creativity sessions are characterised by high quantity of solutions in contrast to solution quality. These weaknesses shall be overcome by Problem Solving Methods. Thus this work concentrates on systematic Problem Solving Methods applicable in product design.

When talking about methods, the complete methodology comprising philosophy and different tools is meant. As methods evolve and new tools are added constantly, only the major tools and the most common variant can be considered in this work.

There is a large number of Problem Solving Methods. In this work only those most commonly used are analysed. Additionally some more are mentioned to give an idea of further possibilities. However it was difficult to find out which methods are most widely used.

According to /83/ most widely used creativity methods in the late 1980s were brainstorming, various checklists and PERT (taken as basis for project management), which are no extensive methods.

Most widely used innovation management tools today are according to /22/ project management, business plan development, corporate intranets and benchmarking.

According to a research done by the Fraunhofer Institute for Production Technology, innovation methods like TRIZ are only marginally (3%) used for process optimisation /92/.

When dealing with the different methods, it arises that the use of problem solving techniques differs according to industry, company size and country.

A survey published by the European Union /22/ on the use of innovation management techniques reveals, that

“Increasing flexibility and efficiency

Managing knowledge effectively

Improving productivity and time-to-market

Improving relationships with suppliers

Gathering on-line marketing information

Facilitating teamwork

Integrating different sources of customer information

Reducing costs by using IT-based solutions

Eliminating redundant processes”

were seen as contribution to companies’ competitive advantages. Though, the focus of this survey was on innovation management techniques, it can be assumed, that similar tendencies could be gained for a survey on Problem Solving Methods: expectations concerning time, quality, cost and organisation.

1.5. Problem, complexity and innovation – elaboration on central notions

Problème, complexité et innovation – élaboration des notions centrales

Problem, Komplexität und Innovation – Gedanken zu zentralen Begriffen

Problem, problem solving, complexity and innovation are central notions in this work. In this chapter some short definitions and differentiations shall contribute to a common understanding.

1.5.1. Problems, problem solving and Problem Solving Methods

“Ein Individuum steht einem Problem gegenüber, wenn es sich in einem inneren oder äußeren Zustand befindet, den es für nicht wünschenswert hält, aber im Moment nicht über die Mittel verfügt, um den unerwünschten Zustand in den wünschenswerten Zielzustand zu überführen.“ /19/

Problems exist in all spheres of life: in economy, in our personal life, in technology, etc. However problems of whatever kind have three characteristics: an undesired initial state, a desired final state and barriers in between. These barriers prevent transforming the initial into the final state directly. /21/ and /42/

By this definition problems can be differentiated from mere tasks. For tasks the approach and mean to find the solution is already known and the solution is gained by reproductive thinking; what is described by the notion routine. For problems the approach is still unknown, a way to find a solution has still to be discovered. Therefore methods can provide help.

The barriers characterizing a problem can be due to several aspects:

- The ways and means for transformation are still unknown and have to be found.
- The means are known, but there exist too many possibilities.
- The goal is only vaguely known, contradictions still have to be discussed and the goal defined.

By this definition it can be seen, that the notions problem and task are a bit vague and subjective. A task for a person with vast experience can be a problem for a person lacking this experience.

Further characteristics of problems are among others field, complicatedness and uniqueness, what is elaborated later.

Based on the given definition of a problem, problem solving can be seen as the creative and inventive process of getting to know how to proceed from a given state to the desired goal.

In addition to the technical solution of the problem quality, costs and time have to be considered in engineering as well. Consequently possible Problem Solving Methods have to tackle all those aspects to support a successful product development.

When dealing with technical problems and PSMs it can be seen that the notions “problem solving”, “engineering design process” and “creativity” are used in an arbitrary way. (Altshuller e.g. published a book with the interesting title “Creativity as an Exact Science”. At first sight it seams to be contradictory to characterize creativity as exact. But when getting familiar with Altshuller’s TRIZ theory it is obvious, that his idea of creativity is problem solving.) The notions shall be separated from each other in order to have a common understanding.

Problem solving implies the idea of overcoming barriers by finding a solution in a more or less systematic and structured process, deciding on a concept that dissolves the problem.

The engineering design process is understood as a process containing the steps Conceptual Design, Embodiment Design and Detail Design according to Pahl and Beitz /42/, i.e. providing a detailed solution in the end. This solution is ready to be produced.

Otherwise the use of creativity to deal with technical problems is less results oriented and structured, referring more to psychological stimulation of men’s mind, applying lateral and divergent thinking.

To summarize, Problem Solving Methods provide solution approaches, stimulated by creativity techniques and detailed in design process. In general PSMs are characterized by three major steps: problem analysis including goal definition, solution generation and solution choice. /21/

When speaking subsequently of problems or problem solving, design problems respectively their solutions are meant.


1.5.2. Complex systems, complexity and systems thinking

As many problems tend to be rather complex than simple a short introduction on the concepts of complex systems, complexity and systems thinking shall be given. For some examples on complex systems see Table 1. Solving problems of complex systems is demanding, common mistakes are summarised in Appendix A1.

Table 1 : Examples for complex systems /10/

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A complex system is formed by a number of elements, which are arranged in a certain structure. Taking a look at the system on different levels can reveal different structures. The structures of the system and the interaction between its elements are subject to continuous change. Interaction takes place in non-linear manner that can not be easily predicted.

It is important to differentiate between a complex system and a complicated system, e.g. a machine tool with predictable and linear behaviour. Assuming that simplicity is the contrary of complexity, a complicated system can be characterized by “simplicity combined with a certain amount of logical depth” /26/.

A complex system can be defined according to Pavard and Dugdale as follows

“A complex system is a system for which it is difficult, if not impossible to restrict its description to a limited number of parameters or characterising variables without losing its essential global functional properties.” /93/

Complexity itself is branded by some characteristics. As the field of complex systems is still quite new, the characteristics that are mentioned vary. Here are some very essentials:

- Non-Linearity

Patterns and behaviour emerge in a complex system out of existing relationships, i.e. change in complex systems occurs in a non-linear manner. Everything has the possibility to influence or affect whatever part of the system, not necessarily in a special sequence.

- Order/Chaos Dynamic

Predicting the next step of a system usually is easy. To predict steps further in the future turns out to be nearly impossible; for it gets the more complicated the more steps are taken into account. This unpredictability is called chaos, also characterized by a high number of elements and interrelationships. The classical example is that by the flapping of a butterfly wing a tornado in some other part of the world might be caused. A tiny change in the initial conditions can have an immense impact.

- Self-Organisation

As a response to environmental feedback, complex systems change automatically in order to increase their efficiency and effectiveness. As examples can be named Natural Selection or cells which are forming an organ by performing different tasks.

- Emergent Properties

Properties, i.e. behaviours and patterns emerge unpredicted out of the relationships of a complex system.

A purely complex system would be irreducible, i.e. it would be impossible to display or simulate it in a way different to reality. But as can be seen in daily life, systems of different degree of complexity do exist. (They can be reduced and simplified without loosing important properties in order to simulate, understand and improve them. Actually, a reduction to the crucial elements and relations is obligatory to be able to handle complex systems.) Therefore every true system can be placed on a scale between pure complexity and absolute simplicity. This quantitative understanding of complexity shall help to characterize a problem later in this work.

The approach to deal with complex systems is called systems thinking, based on system dynamics of MIT’s professor Jay Forrester. The essential idea is to analyse the system as a whole (but maybe simplified) with its interconnectivities, with positive and negative feedback loops. It is about regarding beyond mere linear relations. Thereby, the systemic nature of the problem can be taken into account.

/19/, /10/, /26/, /57/, /66/, /98/, /103/

1.5.3. Innovation and invention

A central notion in nowadays economy is “innovation”. “Innovate or die” indicates the relevance attributed to innovations /83/. The worldwide competition, high complexity of products, short product cycles, more demanding customers etc. are all said to require innovative solutions. Innovations are considered crucial on the way to global competitiveness; this idea even reached politics, where it is trendy to speak about innovations and innovation leadership. But what are innovations – in contrast to inventions. And what are their conditions? The definition of Dosi (according to /22/), who labels innovation as a problem solving process, might be too narrow. Therefore a definition and differentiation shall give assistance.

According to Schumpeter, innovation means novelty:

„Innovation ist die Planung, Erzeugung und Durchsetzung neuer Produkte, neuer Produktqualität, neuer Produktionsverfahren, neuer Methoden für Organisation und Management sowie die Erschließung neuer Beschaffungs- und Absatzmärkte.“

(Joseph A. Schumpeter, 1911, quoted according to /35/)

The basic idea of Schumpeter differentiating between invention and innovation was summarized by Ramge:

„Die Erfindung (Invention) wird erst zur Innovation, wenn sie marktfähig ist - Invention ist die Transformation von Geld in Wissen, und Innovation ist die Umwandlung von Wissen in Geld.“ /45/

What Schumpeter realized as well, was that most modernisations are only achieved by recombination of already existing products and ideas. So innovations not necessarily base on an invention. But in every case they are characterized by a discovery. Cultural openness and tolerance can facilitate these discoveries, according to research done by Jared Diamond /35/.

Two conclusions for problem solving can be drawn from this reflection on innovations.

First, the facilitation of innovations and the philosophical approach of a Problem Solving Method are crucial to its performance.

Second, success of visions and innovations is dependent on how existing knowledge is used, i.e. whether Problem Solving Methods give access to knowledge bases.

Or in other words, innovation processes have to be handled in a multidimensional way, integrating “methodology, work practice, culture and infrastructure” /91/.

2. Problem Solving Methods (PSM)

Méthodes de résolution des problèmes


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In this chapter Problem Solving Methods are presented by giving a short definition of each method, introducing their history, state-of-the-art usage, major tools and proceedings. A standard work is given as a reference for more detailed information. The presentation aims not at presenting the complete proceeding of the method but aims at establishing a basis for later example problem solving and method evaluation.

The evaluation shall facilitate the choice of an appropriate method for a specific problem and product development phase. Thereby methods can be applied where useful and with more success, for

“the various methods should only be applied when they are required and useful. Work should never be done for the sake of systematics or for pedantic reasons alone.” /42/

In the following chapter the methods analysed and compared in this work shall be presented. Afterwards some more methods shall be suggested in short.

2.1. Analysed and compared methods

Méthodes analysées et comparées

Analysierte und verglichene Methoden

In this chapter those methods analysed and compared in this work are presented.

2.1.1. Axiomatic Design What is Axiomatic Design?

Axiomatic Design is a very systematic approach developed by MIT’s professor Nam P. Suh for product development. Customers’ attributes are transferred to functional requirements, those transferred to design parameters which determine process variables. The method organizes the connection of the different domains, see Figure 3, by the use of matrices and axioms.

"Axiomatic Design methods work to make design more creative, reduce the random solution search process, minimize iterative trial-and-error processes and determine the best design among those proposed." SUH in /79/

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Figure 3 : Domain model of Axiomatic Design /34/ Development and state of the art of Axiomatic Design

Suh started to develop his ideas of Axiomatic Design in the late 1970s; his main works were published in the 1990s. His aim was to develop a theoretical framework for design. The framework should include principles that define good design. After a decade of work, he found two axioms: on independence and information. Those were extracted and abstracted from good design practice. Axiomatic Design should be supported by the use of software facilitating the development process. Current success stories are reported in /79/. Tools and procedures of Axiomatic Design

Axiomatic Design aims at avoiding common design mistakes like having more Design Parameters than Functional Requirements (more than necessary), concentrating on symptoms (missing concentration on the causes), too high information content (lack of robustness), etc. Therefore Axiomatic Design incorporates different domains and concepts.

Axiomatic Design is characterized by four domains of thinking, the step from one domain to the next called “mapping” and three main concepts, which are hierarchy, Zigzagging and axioms.

The Customer Domain describes customer and market requirements regarding the product, process or system. Functional Requirements are deduced from the Customer Domain. This domain can be compared to the product concept catalogue. In order to reach the next domain, technical solutions are developed, allowing the fulfilment of the functions, called Physical Domain, comparable to the requirement specification sheet. These specifications finally have to be transformed to the Process Domain, containing a description of the necessary processes to produce a product or to deliver a service.

Functional Requirements (FR) are described making use of a break-down structure, i.e. main functional requirements consist of more lower-level requirements. The same for the Design Parameters (DP): A Functional Requirement is realized by a Design Parameter, consisting of more low-level parameters. Corresponding pairs have to be found before proceeding on to a lower level as there is interdependence between them and lower levels. This top-down development process is referred to as “Zigzagging”, depicted in Figure 4. Alternately the questions “What?” and “How?” are answered. The ensemble of main Functional Requirements and their break-downs is called FR-set.

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Figure 4 : Zigzagging in Axiomatic Design /34/

The development of an optimum solution is supported by two axioms. The Independence Axiom states, that a solution where one Design Parameter fulfils independently one Functional Requirement is ideal. The Axiom is applied on a FR-Set to analyse and valuate found solutions. Therefore a nxn -matrix A containing FRs’ and DPs’ relationships is formed:

{FRs} = [A] {DPs}

The question that has to be asked is “Does Design Parameter DPi affect FRj?” An x indicates a strong impact, otherwise a 0 is used, see Table 2 for an example. (In the case of a non-square matrix A the design is coupled or redundant.)

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Table 2 : General Axiomatic Design matrix /34/

Instead of a simple x or 0, the matrix can contain mathematical relationships to describe the relationship as well. If the causality between FRs and DPs, i.e. the nature of relationship, is not obvious, a detailed analysis has to be carried out.

In total there are three different characteristics of the relationship matrix indicating

1.) Uncoupled design
2.) Decoupled design and
3.) Coupled design, see Table 3.

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The ideal solution is characterized by a matrix indicating affects only in the diagonal, i.e. one Design Parameter affecting one single Functional Requirement. Decoupled design is still acceptable, coupled design has to be avoided.

Table 3 : Possibilities of coupling in Axiomatic Design /34/

The next step of deciding on the best design between different solutions is supported by the Information Axiom, which states that the best solution contains the least information, i.e. only necessary and useful information. Or in other words: Simple solutions are the best.

Information content is determined by applying formulae reverting to the probability that a Design Parameter will fulfil a certain Functional Requirement. The most usual example to explain the Information Axiom shall be given, see Figure 5. The faucet in the design variant on the right is characterized by uncoupled design and less information content, allowing easier handling.

Based on both axioms, general design laws (theorems) and general design rules (corollaries) were deduced assisting in design process, e.g. the corollaries “Decoupling of Coupled Design”, “Minimization of FRs”, “Integration of Physical Parts”, “Use of Standardization”, “Use of Symmetry” and e.g. the theorems “Coupling due to insufficient number of DPs”, “Decoupling of Coupled Design” or “Need for new design” /62/.

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After deciding on the DPs a mapping process to determine the PVs has to follow. The course is equivalent to the mapping of FRs into DPs.

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Figure 5 : Illustration for the Information Axiom /54/ Literature

Suh, Nam P.:

Axiomatic Design: Advances and Applications.

New York: University Press, 2001.

/34/, /39/, /54-56/, /58/, /62/, /79/, /117/

2.1.2. Quality function deployment (QFD) What is QFD?

Quality Function Deployment is a method translating the “voice of the customer”, i.e. the customers’ needs, into technical specifications necessary for product development. Or as Akao, the “father” of QFD put it:

”QFD is a method for developing a design quality aimed at satisfying the consumer and then translating the consumers demands into design targets and major quality assurance points to be used throughout the production phase” AKAO in /1/. Development and state of the art of QFD

QFD dates back to the late 1960s when it was developed by Yoji Akao. It was first implemented at the Japanese Mitsubishi Heavy Industries Kobe Shipyard in 1972 with some partial implementations beforehand. At that time it was usual practice to control quality during and after manufacturing. But Akao and his colleague Shigeru Mizuno wanted to develop a method that would integrate quality in the product already before it was manufactured. Their idea was to “listen to the voice of the customer” in order to be able to design a product according to customers’ needs. In contrast to the traditional approach “the best you can get is nothing wrong” QFD’s positive definition of quality is going beyond /97/. To grant this holistic idea of quality, by the time many tools were developed and composed to the comprehensive method of QFD. The ideas of Value Analysis were integrated as well.

The introduction of QFD in Europe and America did not start but in 1983 when Akaos work was published in English.

Today QFD is widespread in Japan and the U.S. and used in every industry: aerospace, manufacturing, communication, software, IT, transportation, chemical and pharmaceutical, food, service and especially in automotive and electronics. According to a survey 32% of Japanese companies used QFD in 1996 compared to 69% of American companies /1/. QFD is both used for existing and new products and processes, which include a customer aspect. Tools and procedures of QFD

QFD includes “Seven Management and Planning Tools” and the “House of Quality”. The latter is by mistake often seen as QFD. As QFD is going beyond problem solving till production, only the aspects related to problem solving are described in the following.

Those seven tools are:

- Affinity diagrams

Affinity diagrams are used to get insight and order into qualitative information like customer requirements. Each aspect or statement is written on a small card, several cards are grouped under one associated headline according to their affinity. Thereby a hierarchy of requirements can be displayed.

- Relations diagrams

Relations diagrams are used to discover priorities, root causes of problems and unspoken customer requirements. To discover unspoken requirements the tool of Gemba[v] Visits frequently is integrated into QFD. The idea of that tool is to go to the place of the customers and watch them using the product and thereby gain hidden insights /101/.

- Hierarchy trees

Similar to the affinity diagram a hierarchy tree displays the structure of interrelationships between the different statements. However the approach is different: It is built top-down in an analytical manner. This allows to determine incompleteness and to make amendments.

- Matrices and tables

Matrices are used in several QFD tools and are an essential part of QFD. In general the rectangular grid of cells allows a comparison of different items to display relationships. In a prioritisation matrix the relative importance of an item is weighted as well as the relationship with a second item. By multiplication of those numerical weightings a rating of the priority of the items can be gained.

A central table in QFD is the Voice of the Customer Table (VOCT) taken as basis to build the House of Quality. The what, where, when, why and how of the use of a product are described in relation to the demographics of the customer and his “voice”. In doing so, market segments can be differentiated. In a second table those statements are reworded and endorsed by the demanded quality, measurable quality characteristics, necessary functions, required reliability and possibly some comments.

- Process Decision Program Diagrams (PDPC)

PDPC are used to study potential failures of new processes and services.

- Analytic Hierarchy Process (AHP)

Pair wise comparisons on hierarchically organised elements or requirements contribute to achieve a set of priorities and to select from various alternatives.

- Blueprinting

Blueprinting is used as a tool to depict all processes that are carried out to provide a product or service.

- House of Quality (HOQ)

Central tool of QFD is the House of Quality (HOQ). It unites customer requirements, market research and benchmarking data with measurable engineering targets. In the course of a QFD project all those components dealt with together form the HOQ. The general HOQ contains six elements, see Figure 6.

1. Customer requirements (Hows)

Requirements derived from customer statements are listed. QFD takes up the idea of the Kano model, that there exist basic, functional and delight requirements.

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Figure 6: House of Quality /102/

2. Technical requirements (Whats)

Measurable product characteristics relevant to fulfil the functional requirements are listed.

3. Planning matrix

The planning matrix illustrates the customer perceptions gained in market surveys including the relative importance of the customer requirements and the performance of the company and its competitors to meet these.

4. Interrelationship matrix

This part of the HOQ displays the assessment of the degree of interrelationship of customer requirements and technical characteristics. Usually symbols are used to visualize the result of team discussions.

5. Technical correlation matrix (roof)

The roof matrix is used to identify which technical requirements support or impede each other in the product design.

6. Technical priorities, benchmarks and targets

The priorities of technical requirements, a technical benchmark of competitors’ products, the difficulty involved in reaching the requirement as well as target values are found in that lower part.

- Steps of QFD

The steps of QFD are orientated towards the tools already described:

0. Define goals and objectives
1. Deduce top level product requirements from customer needs (possibly conduct survey)
2. Develop product concepts to satisfy requirements
3. Valuate product concepts and make choice
4. Partition into subsystems and transfer higher level requirements and characteristics
5. Derive lower-level requirements and specifications
6. Determine process steps of assembly or manufacturing
7. Set up quality and process controls to grant processes for critical parts
8. Implement concepts

Final remark: It is common to integrate further methods and tools in QFD. QFD is seen more as a framework to streamline the whole process of product or process development. Literature

Akao, Yoji (Editor):

Quality Function Deployment: Integrating Customer Requirements into Product Design.

Translated by Glenn Mazur.

Cambridge, MA: Productivity Press, 1990.

/1/, /17/, /21/, /77/, /97/, /101/, /102/

2.1.3. Robust Design, Taguchi Method What is Robust Design?

Robust Design, also called the Taguchi Method, is a quality engineering technique that combines Statistical Process Control (SPC) with the idea of Design of Experiments (DoE) and new quality related management techniques. The method strives for quality improvement and process robustness, i.e. making the product performance insensitive to variation without eliminating the causes of variation. Taguchi’s Robust Design has to be distinguished from Design of Experiment though there are many similarities. Whereas DoE mostly aims at understanding the interactions of different factors, Robust Design aims at creating robustness. Development and state of the art of Robust Design

Robust Design was developed by Genichi Taguchi starting in the 1950s, Robust Design becoming popular in the 1980s. His work was a reaction on traditional SPC and DoE including the perspective of losses when not meeting the target. In the evolution of quality control, see Figure 7, Robust Design can be classified as a “quality through design” method.

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Figure 7 : Evolution of quality control /119/

Today Robust Design is quite often used in addition to Six Sigma, which tackles manufacturing and service-related processes by improving quality and saving costs. Robust Design contributes the design-approach to Six Sigma to further improve quality and save costs. Robust Design is also used with Design for Manufacturing (DFM): After applying DFM the proper set of values can be found by Robust Design.

Robust Design is used in most different industries: automobiles, telecommunications, electronics, software, etc. Some typical applications of Robust Design were given by /9/: injection moulding, welding, chemical and wire-bonding processes, diesel injector or biscuit length. Tools and procedures of Robust Design

The method of Robust Design can be described by two main ideas:

1. Quality should be measured by the deviation from the target value, see Figure 8. This represents losses by low quality better than measuring the conformance with tolerance limits. This idea is contrary to the classical SPC.
2. Quality has to be built in the product or process, respectively quality loss has to be avoided by proper design. Quality can not be achieved by inspections or rework. Robust Design aims at reducing the effect of factors causing variation and thereby avoids rework to defect parts. To pursuit this idea consequently, the design process is separated into three stages, see Figure 9.

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Figure 8 : Quality Loss Function /61/

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Figure 9 : Stages in the design cycle /119/

The goal of Robust Design is to support the decision on the settings of the control factors to grant a robust design, characterized by a low deviation from the target at low costs.

Robust Design’s five basic tools are primarily used for parameter and tolerance design:

- P-Diagram

The P-diagram is used to visualize and classify different parameters of a system and to understand their relevance. The factors are classified into noise, control, signal, i.e. input and response, i.e. output factors. The noise factors are separated into inner, outer and between product noises.

Control factors are those that can be easily controlled by the engineer, such as cycle time, material choice, temperature, number of units, etc. Noise factors are those, which can be controlled only with difficulty or cannot be controlled at all, such as outside temperature, humidity, etc. The P-diagram is visualized with a black box chart, see Figure 10.

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Figure 10 : P-diagram

- Ideal Function

0The ideal function is used to describe the ideal form of the signal-response relationship mathematically.

- Quadratic Loss Function / Quality Loss Function

The Quadratic Loss Function is used to quantify the loss that is caused by a certain deviation from the target value.

- Signal-to-Noise Ratio (S/N)

The S/N is used as a metric to decide on optimum levels of control factors. Static ratios are differentiated from dynamic ones. Stable problems either have no or a fixed signal value. Dynamic problems in contrast are characterized by an ideal function of signal and response. To calculate the S/N-ratio there are three different equations, depending on the nature of the problem, stemming from three different loss functions: “smaller is better”, what can be used for the number of defects, “nominal is better”, what can be used for the dimensions of a mechanical part or “larger is better”, what can be used for material strength.

At this stage the decision on control factors is crucial. When deciding on the wrong (and less influential) ones, the following results are worthless and invalid.

- Orthogonal Arrays

The orthogonal arrays are the core of Robust Design. They contain the design of experiments and information gained through multiple runs of experiments.

Those tools are used in several steps to achieve a robust design, i.e. to determine the parameters.

1. Problem formulation

This step contributes to the understanding of the problem and its nature. The P-diagram is developed, the ideal function defined (depending on the output performance characteristics), the S/N determined (after identifying the signal and noise factors) and the experiments planned (selection of factor levels, design of an orthogonal array).

2. Data collection, modelling and simulation

The experiments can be either conducted in reality or with a simulation model. The latter can contribute to more economic experiments.

3. Factor Effects Analysis

The effects of the control factors are calculated statistically and the results are interpreted in order to select the optimum settings of the control factors.

4. Prediction and validation of the new settings

The performance of the product or process design for the optimum settings is calculated. The calculation is approved by a further run of the experiment. Literature

Phadke, Madhav Shridhar:

Quality Engineering Using Robust Design.

Englewood Cliffs, NJ: Prentice Hall PTR, 1989.

/9/, /15/, /61/, /94/, /119/

2.1.4. Systematic Approach of Pahl and Beitz (SAPB or P&B) What is the Systematic Approach of Pahl and Beitz?

The Systematic Approach of Pahl and Beitz was developed in Germany in the 1970s based on VDI guideline 2221 “Methodology for the development and construction of technical products”. It is a systematic approach, breaking the product down into functional modules. Each module can be designed following an outline for a systematic design process. Solutions are found by the help of rules and catalogues. The decision for one alternative is taken based on use-value analysis.

SAPB is just one method out of a broad range of systematic construction[vi] methods among which those of Roth (1982), Koller (1985, 1986, 1989), Rodenacker (1976) and Hansen (1965). Development and state of the art of the Systematic Approach of Pahl and Beitz

The VDI guideline 2221 and the work of Pahl and Beitz form the basis for the proceeding during any design process. In Germany the use of this approach is custom, internationally the approach is highly appreciated and accepted.

Originally developed for a paper-based approach, the integration of Software catalogues, CAX and prototyping systems is widespread. Tools and procedures of the Systematic Approach of Pahl and Beitz

The proceeding of design problem solving is divided into several phases; see
Figure 11:

- Clarification of the task
- Conceptual Design
- Embodiment Design
- Detail Design

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Figure 11 : Design process according to SAPB, simplified version /36/

The proceeding in general can be characterized as a process from qualitative to quantitative.

- Clarification of the task

In this phase information on the product has to be collected, in particular constraints and requirements. They are finally leading to a design specification.

- Conceptual Design

An analysis of the requirements, identification of the problem and its abstract formulation found the basis for this phase. Components of abstract formulation are functions (and sub-functions) and the description of modules. The abstract formulation widens the horizon, enlarges the solution space to be analysed and avoids sticking to the first solution. Furthermore it reduces the system’s complexity, as only an extract of the whole system is regarded at a time. I.e. before searching for a solution, the problem may be divided into sub-problems according to the product’s functional structure. Solution finding is supported by creativity methods like brainstorming, analytical methods like patent and literature research and design catalogues (containing physical and chemical effects and machine elements). References for design catalogues are given in /42/. To combine solution variants of different sub-problems, morphological matrices are used. The result is the description of the Working Interrelationship[vii]. With the help of use-value analysis the best variant is determined, which will be developed further. Use-value analysis is a very central part of SAPB as the right decision influences the remaining design process and related costs. To give an idea of use-value analysis, the different steps shall be presented:

1. List the rating criteria
2. Assign weighting factors to the rating characteristic
3. Assign operational measures to each rating characteristic
4. Assign numerical rating values to the individual characteristic
5. Obtain an overall rating
6. Compare and contrast alternatives
7. Consider uncertainties

- Embodiment Design

Using Conceptual Design as starting point, layout and form of the product are developed. This phase is supported by rules, principles and guidelines. Rules state conditions that have to be fulfilled to achieve good, i.e. clear, simple and safe design. Principles present abstract engineering knowledge (Principle of the division of tasks, Principle of self-help, Principles of force and energy transmission, etc.), whereas guidelines are more specific, e.g. giving advice concerning moulding, casting or assembly.

- Detail Design

In this last phase detail drawings and production documents are generated before the solution can be realized. Literature

Pahl, Gerhard; Beitz, Wolfgang:

Engineering Design: A systematic approach.

3rd edition,

Berlin: Springer, 1995.

/11/, /36/, /42/, /51/

2.1.5. TRIZ – Theory of Inventive Problem Solving What is TRIZ?

TRIZ is the Russian acronym for Theory of Inventive Problem Solving. TRIZ was developed by Genrich Altshuller starting in the 1940s and is based on a huge patent research. The findings of this research, generic solution approaches used in thousands of patents, are made accessible through the methodology of TRIZ. A systematic solution process and many tools lead to qualified and innovative solutions. Development and state of the art of TRIZ

„Technical evolution has its own characteristics and laws. This is why different inventors in different countries, working on the same technical problems independently, come up with the same answer. This means that certain regularities exist. If we can find these regularities, then we can use them to solve technical problems – by rule, with formulae, without wasting time on sorting out variants.” Altshuller in /5/

TRIZ was developed mainly by Genrich Altshuller, a soviet patent engineer, starting in the 1940s. His revolutionary ideas and the idealistic way of promoting it led to his imprisonment in a Soviet Gulag. So Altshuller was not able to publish the basics on his theory but in 1961. Altshuller’s theory and his lectures had a large response. All over the Soviet Union people started to learn and apply TRIZ. In the period from 1974 to 1986 Altshuller was banned from publishing. However he continued developing his theory. Due to those restrictions and the Cold War, Altshuller’s theory did not reach the US but in the early 1990s. From there it dropped to Europe. /105/

Today TRIZ is applied in most big companies worldwide. Apart from classical TRIZ according to Altshuller, some methods adapting TRIZ (e.g. “Problemzentrierte Invention” (PI) according to Moehrle) or methods integrating TRIZ (e.g. “Widerspruchsorientierte Innovationsstrategie” (WOIS) according to Linde) are used. Some simplified methods based on TRIZ like Ford Motor Company’s “Structured Inventive Thinking” (SIT) exist as well /53/.

However TRIZ is still relatively unknown among most people, compared to concepts like Total Quality Management or Lean Manufacturing. Tools and procedures of TRIZ

Essential elements of TRIZ are systematic problem description and problem solution based on principles extracted from thousands of patents. TRIZ comprises numerous tools which can be selected depending on the structure of the problem. Due to this breadth, a short presentation of the method is difficult. In the following part the most basic ideas, procedures and tools shall be presented.

The foundation of TRIZ

- Levels of innovation

The analysis of patents revealed that values of patents differ. Based on that finding Altshuller suggested five levels of innovation, indicating their value:

Level 1: Existing products or processes are modified and improved to a small extend. The knowledge available within a trade is required.

Level 2: A minor technical contradiction is solved. Knowledge from different fields related to the problem is required.

Level 3: A significant physical contradiction is solved. Knowledge of related industries is required.

Level 4: A long-standing contradiction is solved by a new and breakthrough technology. Interdisciplinary knowledge is required.

Level 5: A new principle or phenomena is discovered what allows a higher technological solution level.

Most of the patents solve simple problems of the level 1 and 2 (60-80%). Breakthrough innovations of level 5 are absolutely rare (less than 1%). TRIZ aims at assisting inventors to elevate their solutions to levels 3 and 4.

- Contradictions

As allready mentioned contradictions form the reason for problems. A contradiction occurs, when solving one parameter relevant to a product or process a second one deteriorates, hindering the achievement of an ideal result. Those problems solved, formerly containing a contradiction, tend to bring up innovative solutions. Therefore TRIZ primarily aims at solving contradictions. In usual problem solving, contradictions are handled by tradeoffs.

Altshuller distinguishes between technical, physical and administrative contradictions.

For an administrative contradiction (AC) it is obvious and well-known that the contradiction has to be tackled. The contradiction is allready analysed but still the final goal and the mean to get there have to be decided on.

The technical contradiction (TC) refers to the inconsistency of the optimization of two parameters. To give an example:

When augmenting the maximum velocity of an airplane by installing a stronger engine, this might require bigger wings what could reduce velocity due to more drag.

To describe technical contradictions Altshuller proposed 39 technical parameters, see below. To solve TCs based on a consistent description Altshuller created the contradiction matrix, the most well-known tool of TRIZ.

Finally there are physical contradictions (PC): They demand two contradictory properties from one single element. To give an example as well:

An airplane should on the one hand have wheels to allow debarking and on the other hand it should not have wheels for they increase drag.

To solve PCs Altshuller formulated separation principles which are going to be explained later.

- Idealty

TRIZ is not interested in compromises. The aim is to advance to idealty.

„The technical object is ideal if it does not exist, but its function is performed. The ideal object is perfect: it costs nothing, is unbreakable, it causes no side effects, it wants no maintenance, etc.“ (ALTSHULLER quoted from /106/)

The description of an Ideal Final Result (IFR), though not possible to reach, is meant to give some guidance on the way to develop a solution. The degree of idealty can be determined by the equation

Degree of idealty = useful functions /(harmful functions + costs)

The Law of Idealty testifies that any technical system tends to become more ideal during lifetime, i.e. more economic, performant, simple, etc.

- Evolution of technical systems

All systems tend to more idealty during lifetime. Apart from that law ALTSHULLER described eight patterns of technical systems evolution. One is life cycle using S-curves to position a product and predict its future. A second is dynamisation, suggesting a transition from a rigid to a dynamic and flexible system.

The proceeding in using TRIZ

- TRIZ process

The solution process of TRIZ can be visualized with Figure 12.

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Figure 12 : Solution process of TRIZ

Starting with a concrete problem, TRIZ first analysis the problem in detail. The Main Useful Function (MUF) is identified, positive and negative relationships of the system described, contradictions discovered, ressources summed up and parameters described. With this abstract formulation of the problem, acess to solution creating tools like the contradiction matrix is given. The gained abstract solution still has to be concretised to be able to realize the solution.


For complex problems Altshuller developed ARIZ, what is the Russian acronym for Algorithm for Inventive Problem Solving. ARIZ gives a step-by-step instruction to find a solution. The first ARIZ was developed in 1959, many consequent versions precised the algorithm. The most recent ARIZ has nearly 100 steps, assigning TRIZ’s tools and thereby granting a systematic solution process. To give an idea of the proceeding, ARIZ-61, a still manageable version shall be quoted /6/:

“Part One: Analytical stage

1. State the problem
2. Imagine the Ideal Final Result
3. Find the contradiction
4. Find the reason for the contradiction
5. Find conditions during which the contradiction is removed

Part Two: Operative stage

1. Explore the possibility of making changes in the object itself
2. Explore the possibility of dividing an object into independent parts
3. Explore the possibility of altering the outside environment
4. Explore the possibility of making changes in neighbouring objects
5. Study prototypes from other industries
6. Return to the original problem (in case the above steps are not applicable) and widen that problem’s conditions – make the transition to a more general problem statement

Part Three: Synthetic stage

1. Change the shape of a given object
2. Change other objects that interact with the one under consideration
3. Introduce changes into the means of an object’s functionality
4. Explore the implementation of the new-found principle in solving other technical problems”

The main tools of TRIZ

- Parameters, principles and the contradiction matrix

The contradiction matrix to solve technical contradictions described by technical paramenters is the central tool of TRIZ. Its componenents and usage shall be described in the following paragraph.

Altshuller collected 39 technical parameters, see Table 4, that satisfy describing most common technical problems. By the means of these, the problem has to be described. There might be one contradiction (i.e. between two parameters) or even more contradictions (that can be described by more parameters).

Table 4 : 39 Technical Parameters of TRIZ

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In his patent research Altshuller found out that most of the patents contain recurring solution principles for perfoming a certain action and solve certain contradictions. He discovered and described 40 Inventive Principles, for an enumeration see Table 5.

To give access to those solution principles Altshuller combined Technical Parameters and Inventive Principles to a 39 by 39 matrix. An extract of this matrix is presented below, in Table 6.

Table 5 : 40 Inventive Principles of TRIZ

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Table 6 : Extract of the Altshuller Matrix

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[i] According to /102/ from the 1930s to the 1980s more than 300 innovation methods have appeared.

[ii] Definition of «method» according to a way, technique, or process of or for doing something

[iii] “super” is meant in the sense of “hyper”, without any intent to judge the method [German: Übermethode]

[iv] [German: Methodenbaukasten]

[v] [v] “Gemba” is Japanese an means “the place where the real action takes place” or “the place where value is created”

[vi] [German: Methodisches Konstruieren]

[vii] [German: Wirkstruktur]

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Contribution to the design of a matrix to analyse and classify problem solving methods according to performance criteria
University of Stuttgart  (Institut für Arbeitswissenschaften und Technologiemanagement)
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TRIZ, Bionic, Problemlösung, Methoden, Bionik
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Martin Fritz (Author), 2006, Contribution to the design of a matrix to analyse and classify problem solving methods according to performance criteria, Munich, GRIN Verlag,


  • Martin Fritz on 7/26/2010

    Alternativer deutscher Titel: Methodenmatrix zur technischen Problemlösung

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