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

Table of contents

   

Table  of  NA

1.  Framework and context of the work  1

   

Objective  and structure of the work  1

   

Keywords:   5

   

On  the necessity of an analysis and classification matrix context of the  NA

   

   

Limitations  of this work  7

   

Problem,  complexity and innovation elaboration on central notions  9

   

Problems,  problem solving and Problem Solving Methods  9

   

Complex  systems complexity and systems thinking  11

   

Innovation  and invention  13

2.  Problem Solving Methods PSM  15

   

Analysed  and compared methods  15

   

Axiomatic  Design  16

   

Quality  function deployment QFD  19

   

Robust  Design Taguchi Method  23

   

Systematic  Approach of Pahl and Beitz SAPB or  27

   

TRIZ  Theory of Inventive Problem Solving  30

   

Value  Engineering  39

   

Short  suggestion of some other interesting methods  41

   

Positioning  of the methods in the problem solving process or  NA

process  making use of the model  42

3.  Development of criteria for classification analysis and evaluation of  NA

Solving  Methods  46

   

Performance  and characteristic of Problem Solving Methods  46

   

Criteria  and characteristic to describe the problem  50

4.  Development of the analysis and comparison matrix  53

   

Example  problems and their solution concepts  53

   

   

Table of contents

   

   

Convey  glass disc problem  55

   

Disperse  varnish problem  66

   

Glass  polishing problem  76

   

Powder  Injection Moulding PIM problem  86

   

Tea  ball problem  96

   

Supplemental  problem  106

   

Evaluation  of the performance of Problem Solving Methods  113

   

Validation  of Axiomatic Design  114

   

Validation  of QFD  118

   

Validation  of Robust Design  124

   

Validation  of the Systematic Approach of Pahl and Beitz  129

   

Validation  of TRIZ  133

   

Validation  of Value Engineering  139

   

Overall  Analysis and Classification Matrix  145

   

Composition  of the overall analysis and classification matrix  145

   

Depiction  of the AC matrix  146

   

Proceeding  to read the matrix statement  147

5.  Validation of the analysis and comparison matrix concept  153

   

Outline  for the solution of final example problem  153

   

Validation  example problem  153

   

Description  of the problem Development of car tires for all year  NA

   

   

Statement  of the matrix  156

   

Solution  concepts  158

   

Conclusions   164

   

Validation  of the concept with the help of the Validation Square  165

   

The  Validation Square  166

   

Application  of the Validation Square concept on the ACM  167

6.  Conclusion outlook and summary  169

   

   

Table of contents

   

   

Review  of critical points and difficulties  169

   

Outlook   171

   

Summary   172

List  of literature  175

Appendix   183

Recommendations  for the fast  NA

For  an overview on the thesis the fast reader is recommended to glance over  NA

1  and and to read chapters and  NA

   

   

List of abbreveations

   

List  of  NA

   

   

   

   

   

   

   

Analysis  and Classification  NA

   

   

   

   

   

Analysis  and Classification  NA

   

   

   

   

   

Computer  Aided  NA

   

   

   

   

   

Computer  Aided  NA

   

   

   

   

   

entirety  of computer aided  NA

   

   

   

   

   

Concurrent  Engineering Concurrent  NA

   

   

   

   

   

Concurrent  Simultaneous  NA

   

   

   

   

   

Design  for  NA

   

   

   

   

   

Design  Parameter Design  NA

   

   

   

   

   

Ecole  Catholique Arts et  NA

   

   

   

   

   

Functional  Requirement Functional  NA

   

   

   

   

   

House  of  NA

   

   

   

   

   

Innovation  Principle Innovation  NA

   

   

   

   

   

Information  Technology Information  NA

   

   

   

   

   

Massachusetts  Institute of  NA

   

   

   

   

   

Naturorientierte  Innovationsstrategie Naturorientierte  NA

Nature  Oriented Innovation  NA

   

   

   

   

   

Systematic  Approach of Pahl and  NA

   

   

   

   

   

Program  Evaluation and Review  NA

   

   

   

   

   

Powder  Injection  NA

   

   

   

   

   

Problem  Solving  NA

   

   

   

   

   

Process  Variable Process  NA

   

   

   

   

   

Quality  Function  NA

   

   

   

   

   

Signal-noise  ratio Signal noise  NA

   

   

   

   

   

Systematic  Approach of Pahl and  NA

   

   

   

   

   

Simultaneous  Engineering Simultaneous  NA

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

Total  Quality  NA

   

   

   

   

   

Theory  of Inventive Problem  NA

   

   

   

   

   

Value  Analysis Value  NA

   

   

   

   

   

Verein  Deutscher  NA

   

   

   

   

   

Value  Engineering Value  NA

   

   

   

   

   

Value  Management Value  NA

   

   

   

   

   

List of figures

   

List  of  NA

Figure  Consultancy wisdom  6

Figure  Current research directions concerning improvement of  NA

Solving  Methods  7

Figure  Domain model of Axiomatic Design  16

Figure  Zigzagging in Axiomatic Design  17

Figure  Illustration for the Information Axiom  19

Figure  House of Quality  22

Figure  Evolution of quality control  24

Figure  Quality Loss Function  25

Figure  Stages in the design cycle  25

Figure  diagram  26

Figure  Design process according to SAPB simplified version  28

Figure  Solution process of TRIZ  33

Figure  simple field composed of field and two substances  37

Figure  Thinking in time and space boxes draft  38

Figure  model classifying different stages in innovation process  43

Figure  Solution level of example problems conceptual level  55

Figure  model of the convey glass disc problem  59

Figure  Possible conveyor for hot material  62

Figure  Black Box depicting of convey glass disc problem  63

Figure  Principles and  65

Figure  Sketch of dispersion bowl  66

Figure  model of the disperse varnish problem  70

Figure  Black Box depicting of disperse varnish problem  73

Figure  Su field model of varnish dispersion  74

Figure  model of the glass polishing problem  78

Figure  Functional diagram of polishing glass lenses  80

   

   

List of figures

   

Figure  Black Box depicting of polish glass lens problem  82

Figure  PIM process  87

Figure  model of the PIM problem  90

Figure  Screenshot of web based function catalogue  91

Figure  Patent research on espacenet  92

Figure  Black box depicting of PIM problem  93

Figure  model of tea brewing with tea ball  100

Figure  Patented tea ball US patent  102

Figure  Dressman ironing apparatus  107

Figure  Different design solutions for irons  111

Figure  Composition of the AC matrix part part and part  145

Figure  Presentation of the overall AC matrix  146

Figure  Steps to read the AC matrix statement  147

Figure  Validation Square  165

Figure  Possible method combination in overall innovation process  172

   

   

List of tables

   

List  of  NA

Table  Examples for complex systems  11

Table  General Axiomatic Design matrix  18

Table  Possibilities of coupling in Axiomatic Design  18

Table  Technical Parameters of TRIZ  35

Table  Inventive Principles of TRIZ  36

Table  Extract of the Altshuller Matrix  36

Table  Funtional Analysis Table  38

Table  Development of VE  40

Table  Positioning of PSMs in innovation process according to model  45

Table  Characteristics of example problems used for method evaluation  54

Table  Characterisation of convey glass disc problem  56

Table  Design matrix of the convey glass disc problem  59

Table  Planning array for experiments on conveying glass disc  60

Table  Problem analysis in time and space convey glass disc  63

Table  Characterization of disperse varnish problem  67

Table  Design matrix of the disperse varnish problem  70

Table  Planning array for experiments on dispersing varnish  71

Table  Problem analysis in time and space disperse varnish  74

Table  Characterization of glass polishing problem  77

Table  Planning array for experiments on polishing glass lenses  79

Table  Requirement list for glass polishing  81

Table  Problem analysis in time and space glass polishing  82

Table  Functional analysis of glass polishing  83

Table  Component function cost table for glass polishing  85

Table  Results of the brainstorming session on glass polishing  86

Table  Characterisation PIM problem  88

Table  Planning array for experiments on PIM  90

   

   

List of tables

   

Table  Problem analysis in time and space PIM  94

Table  Characterisation of tea ball problem  96

Table  House of Quality of tea ball problem  98

Table  Requirements for an improved tea ball  101

Table  Br inwriting results for an improved tea ball design  102

Table  Problem analysis in time and space tea ball  103

Table  Component function cost table for tea ball  105

Table  Characterisation of iron shirt problem  107

Table  Voice of Customer Table Iron shirt problem  109

Table  Design matrix of the ironing shirt problem  109

Table  Component function cost table for ironing shirts  110

Table  Methods used to solve example problems marked in grey  113

Table  General performance of Axiomatic Design  114

Table  Problem specific performance of Axiomatic Design  117

Table  General performance of QFD  119

Table  Problem specific performance of QFD  122

Table  General performance of Robust Design  124

Table  Problem specific performance of Robust Design  127

Table  General performance of SAPB  129

Table  Problem specific performance of SAPB  132

Table  General performance of TRIZ  134

Table  Problem specific performance of TRIZ  137

Table  General performance of Value Engineering  140

Table  Problem specific performance of Value Engineering  143

Table  Attribution of rating credits depending on the degree of  150

Table  Example for the calculation of rating sum  151

Table  Tire requirements  154

Table  Characterisation of validation example problem  155

   

   

List of tables

   

Table  Congruency check between method performance and  NA

character   157

Table  Voice of the customer table for all year tires  159

Table  Component function cost table for all year tires  164

   

   

1. Framework and context of the work

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. 1

1. Framework and context of the work

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). 2

1. Framework and context of the work

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. 3

1. Framework and context of the work

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. 4

1. Framework and context of the work 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.

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. i

According to /102/ from the 1930s to the 1980s more than 300 innovation methods have appeared. 5

1. Framework and context of the work

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 i : 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 ii - 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. i Definition of «method» according to www.webster.com: a way, technique, or process of or for doing something ii

“super” is meant in the sense of “hyper”, without any intent to judge the method [German: Übermethode] 6

1. Framework and context of the work

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.

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” i , 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, i

[German: Methodenbaukasten] 7

1. Framework and context of the work

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 8

1. Framework and context of the work

“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 9

1. Framework and context of the work

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. 10

1. Framework and context of the work

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.

/72/

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/

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 11

1. Framework and context of the work

“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. 12

1. Framework and context of the 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. 13

1. Framework and context of the work

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/. 14

2. Problem Solving Methods

2. Problem Solving Methods (PSM)

Méthodes de résolution des problèmes

Problemlösungsmethoden

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. 15

2. Problem Solving Methods

2.1.1. Axiomatic Design

2.1.1.1. 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/

Figure 3: Domain model of Axiomatic Design /34/

2.1.1.2. 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/.

2.1.1.3. 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. 16

2. Problem Solving Methods

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.

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 DP i affect FR j ?” 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.) 17

2. Problem Solving Methods

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.

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 18

2. Problem Solving Methods

insufficient number of DPs”, “Decoupling of Coupled Design” or “Need for new design” /62/.

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.

Figure 5: Illustration for the Information Axiom /54/

2.1.1.4. 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)

2.1.2.1. 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/.

2.1.2.2. 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, 19

2. Problem Solving Methods

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.

2.1.2.3. 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 i 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. ii

“Gemba” is Japanese an means “the place where the real action takes place” or “the place where value is created” 20

2. Problem Solving Methods

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. 21

2. Problem Solving Methods

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: 22

2. Problem Solving Methods 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.

2.1.2.4. 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

2.1.3.1. 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. 23

2. Problem Solving Methods

2.1.3.2. 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.

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.

2.1.3.3. 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. 24

2. Problem Solving Methods

Figure 8: Quality Loss Function /61/

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. 25

2. Problem Solving Methods

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). 26

2. Problem Solving Methods 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.

2.1.3.4. 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)

2.1.4.1. 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 i

methods among which those of ROTH (1982), KOLLER (1985, 1986, 1989), RODENACKER (1976) and HANSEN (1965). i

[German: Methodisches Konstruieren] 27

2. Problem Solving Methods

2.1.4.2. 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.

2.1.4.3. 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

Figure 11: Design process according to SAPB, simplified version /36/ 28

2. Problem Solving Methods

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 i . 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 i

[German: Wirkstruktur] 29

2. Problem Solving Methods

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.

2.1.4.4. 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

2.1.5.1. 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.

2.1.5.2. 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 30