Product Research (eBook)

Editorial 5
1 Motivation for this Book 5
2 Summary of Research Articles 5
2.1 Innovation and Information Sharing in Product Design 6
2.2 Decision Making in Engineering Design 6
2.3 Customer Driven Product Definition 7
2.4 Quantitative Methods for Product Planning 8
Acknowledgements 9
Contents 
11 
Part I Innovation and Information Sharing in Product Design 13
1 Improving Intuition in Product Development Decisions 14
1.1 The Goal of Market Research Is to Create Early and Accurate Intuition That Is Shared Across Functions 17
1.1.1 What Is Intuition? 17
1.2 Intuition Is the Abstract Knowledge That Comes Automatically from Guided Experiences – A Trainable Skill, Often ``Beyond Words'' 18
1.2.1 How Does One Nurture Intuition? 19
1.2.2 How Does One Nurture Shared Intuition? 20
Example 1: Inspirational Research 21
Example 2: Iterative Design 24
1.2.3 How Will the Nurtured Intuition Philosophy Change Company Behavior? 26
References 27
2 Design Creativity Research 28
2.1 Design, Design Research and Its Methodology 28
2.1.1 Research Clarification: Identifying Goals 30
2.1.2 Descriptive Study I: Understanding Current Situation 30
2.1.3 Prescriptive Study: Developing Support 31
2.1.4 Descriptive Study II: Evaluating Support 31
2.2 Objectives of This Paper 31
2.3 Definition and Measures for Creativity 32
2.3.1 What Is Meant by Creativity? 32
2.3.2 A `Common' Definition 32
2.3.3 `Common' Measures 34
2.3.3.1 Novelty 34
2.3.3.2 Proposed Novelty Measure and Validation 35
2.3.3.3 Usefulness 36
2.3.3.4 Proposed Usefulness Measure and Validation 37
2.3.3.5 Proposed Creativity Measure and Validation 37
2.4 Major Influences on Creativity 38
2.5 Effect of Search and Exploration on Creativity 40
2.6 How Well Do Designers Currently Explore Design Spaces? 42
2.7 Supporting Creativity 43
2.7.1 Idea-Inspire 43
2.7.2 Using Idea-Inspire 44
2.7.3 Evaluation 46
2.8 Summary and Conclusions 47
References 48
3 User Experience-Driven Wireless Services Development 51
3.1 Introduction 51
3.2 Persona-Based Mobile Service Design 53
3.3 Stakeholders 54
3.4 End Users 55
3.5 Trade Customers 55
3.6 Operator Users 57
3.7 Mobile Social Community Example 63
3.8 Caveats in the Use of Personas for Mobile Service Design 71
3.9 Conclusions 74
References 75
4 Integrating Distributed Design Information in Decision-Based Design 76
4.1 Introduction 76
4.2 Integrating Distributed Design Information 78
4.2.1 Emerging and Existing Information Technologies 79
4.2.1.1 Unicode and URI 79
4.2.1.2 XML 79
4.2.1.3 RDF 80
4.2.1.4 Ontology 81
4.2.1.5 Information Technology Summary 82
4.2.2 An Ontological Approach to Integrating Design Information 82
4.2.2.1 Engineering Design Ontologies 82
4.2.2.2 Linking Distributed Information 84
4.3 Modeling Decisions in a Distributed Environment 85
4.4 Case Study 89
4.4.1 Problem Setup 89
4.4.2 Conjoint-HoQ Method 91
4.4.3 Design of the Transfer Plate Using DSO Framework 91
4.4.4 Case Study Summary 96
4.5 Summary 96
References 97
Part II Decision Making in Engineering Design 100
5 The Mathematics of Prediction 101
5.1 Introduction 101
5.2 Basic Concepts 102
5.3 The Dutch Book 103
5.4 The Use of Evidence in Prediction 108
5.5 Stochastic Modeling 114
5.6 Conclusions 117
References 119
6 An Exploratory Study of Simulated Decision-Making in Preliminary Vehicle Design 120
6.1 Introduction 120
6.2 Prior Work 121
6.2.1 Decision Analysis 121
6.2.2 Decision Analysis Cycle 123
6.2.3 Human Aspects 124
6.2.3.1 State of Information 124
6.2.3.2 Cognition 124
6.2.3.3 Personality 126
6.3 Methodology 127
6.3.1 Method 127
6.3.2 Problem Statement 128
6.3.3 Description of Decision-Makers 129
6.3.3.1 Jim 129
6.3.3.2 Terry 129
6.3.3.3 Glenn 130
6.4 Results 130
6.4.1 Common Elements 130
6.4.2 Jim's Decision 132
6.4.3 Terry's Decision 133
6.4.4 Glenn's Decision 134
6.5 Discussion 135
6.5.1 State of Information 135
6.5.2 Cognition 136
6.5.3 Prior Knowledge 136
6.5.4 Personality 137
6.5.5 Decision-Analytic Principles 137
6.5.6 Evaluation of Decisions 138
6.6 Conclusions 139
References 140
7 Dempster-Shafer Theory in the Analysis and Design of Uncertain Engineering Systems 141
7.1 Introduction 142
7.1.1 Background 142
7.1.2 Review of Dempster Shafer Theory 143
7.2 Vertex Method 145
7.2.1 Computational Aspects of the Vertex Method 145
7.3 Analysis of a Welded Beam 146
7.3.1 Analysis with Two Uncertain Parameters 147
7.4 DST Methodology when Sources of EvidenceHave Different Credibilities 151
7.4.1 Solution Procedure with Weighted Dempster ShaferTheory for Interval-Valued Data (WDSTI) 152
7.4.2 Analysis of a Welded Beam 152
7.4.3 Numerical Results 153
7.5 Evidence-Based Fuzzy Approach 154
7.5.1 -Cut Representation 154
7.5.2 Fuzzy Approach for Combining Evidences(Rao and Annamdas 2008) 155
7.5.3 Computation of Bounds on the Margin of Failure/Safety 156
7.6 Other Combination Rules 158
7.6.1 Dempster's Rule 160
7.6.2 Yager's Rule (Yager 1987) 160
7.6.3 Inagaki's Extreme Rule 161
7.6.4 Zhang's Rule 162
7.6.5 Murphy's Rule 164
7.6.5.1 Observations on the Results of the Automobile Safety Problem 164
7.7 Conclusion 165
References 165
8 Role of Robust Engineering in Product Development 167
8.1 Introduction to Robust Engineering 167
8.2 Concepts of Robust Engineering 169
8.2.1 Parameter Diagram (P-Diagram) 169
8.2.2 Experimental Design 170
8.2.2.1 Types of Experiments 171
8.2.3 Signal to Noise (S/N) Ratios 171
8.2.4 Simulation Based Experiments 172
8.3 Case Examples 173
8.3.1 Circuit Stability Design 173
8.3.1.1 Classification of Factors: Control Factors and Noise Factors 173
8.3.1.2 Parameter Design 176
8.3.2 Robust Parameter Design of Brake System 177
8.3.2.1 Signal Factor and Levels 178
8.3.2.2 Noise Factors and Noise Strategy 178
8.3.2.3 Control Factor and Levels 178
8.3.2.4 Experimental Details 178
8.3.2.5 Two-Step Optimization 179
References 182
9 Distributed Collaborative Designs: Challenges and Opportunities 183
9.1 Collaborative Product Development 183
9.1.1 Issues in Distributed Collaborative Design 184
9.2 Negotiation Among Designers 185
9.2.1 Negotiation Framework 189
9.2.2 Analyzing Negotiation-Based Product Development 191
9.2.2.1 Convergence 193
9.2.2.2 Solution Quality 194
9.2.2.3 Communication 197
9.3 Rationality of Collaborative Designs 198
9.3.1 Rationality Tester 199
9.4 Summary 202
References 202
Part III Customer Driven Product Definition 203
10 Challenges in Integrating Voice of the Customer in Advanced Vehicle Development Process – A Practitioner's Perspective 204
10.1 Introduction 204
10.2 Voice of the Customer 205
10.3 Understanding and Interpreting the Voice of the Customer 206
10.3.1 Conjoint Analysis 206
10.3.2 S-Model 207
10.3.3 Quantitative vs. Qualitative Market Research 207
10.3.4 Kano Model 208
10.3.5 Questions 209
10.4 Incorporating the Voice of the Customer 210
10.4.1 Questions 211
10.5 Global Voice of the Customer 212
10.5.1 Questions 212
10.6 Conclusions 213
References 214
11 A Statistical Framework for Obtaining Weights in Multiple Criteria Evaluation of Voices of Customer 215
11.1 Introduction 215
11.2 Voice of Customer Prioritization Using ER Algorithm 217
11.2.1 Evidential Reasoning Algorithm 219
11.2.2 Impact of Weight of Survey 219
11.3 Factors Influencing the Weight of a Survey 221
11.3.1 Design for Selecting Respondents 221
11.3.2 Source for Identifying the Respondents 223
11.3.3 Credibility of Agency Conducting the Survey 223
11.3.4 Domain Experience of Respondents 224
11.3.5 Weight of a Survey 225
11.4 Demonstrative Example 226
11.4.1 Influence of Sampling Design on Survey Weights 226
11.4.2 Influence of Source of Respondents on Survey Weights 228
11.4.3 Influence of Agency Credibility on Survey Weights 229
11.4.4 Influence of Domain Experience on Survey Weights 229
11.4.5 Estimating Survey Weights 230
11.4.6 Application of ER Algorithm for Voice Prioritization 231
11.5 Summary 232
References 233
12 Text Mining of Internet Content: The Bridge Connecting Product Research with Customers in the Digital Era 234
12.1 Introduction 234
12.2 Overview of Web Mining Types 236
12.2.1 Information Retrieval 236
12.2.2 Natural Language Processing 238
12.3 Product Review 238
12.3.1 Buzz Analysis 238
12.3.1.1 Named Entity Recognition 239
12.3.1.2 Establishing a Baseline 240
12.3.1.3 Cleaning the Data 240
12.3.1.4 Weighing the Opinions 240
12.3.2 Opinion Mining 241
12.4 Conclusions 244
References 244
Part IV Quantitative Methods for Product Planning 246
13 A Combined QFD and Fuzzy Integer Programming Framework to Determine Attribute Levels for Conjoint Study 247
13.1 Introduction 247
13.2 Solving Fuzzy Integer Linear Programs 249
13.3 Converting a Fuzzy Integer Linear Programming (FILP) Problem to Parametric Integer Linear Programming (PILP) Problem 249
13.4 A Contraction Algorithm for Solving a PILP(Bailey and Gillett 1980) 251
13.5 The Model Description 252
13.6 Application 253
13.7 Results 257
13.8 Results with Symmetric Triangular Fuzzy Numbers 258
References 259
14 Project Risk Modelling and Assessment in New Product Development 261
14.1 Introduction 261
14.2 The Proposed Approach to Generate the Probabilitiesin Bayesian Network 262
14.2.1 Generation of Probabilities of the Nodes without Parent 262
14.2.2 Generation of Probabilities for Nodeswith a Single Parent 263
14.2.3 Generation of Conditional Probabilitiesfor Multi-Parent Nodes 264
14.3 Application of the Method in Risk Evaluation of NPD 265
14.3.1 Case Description 265
14.3.2 Bayesian Network Construction 266
14.3.3 Generation of Conditional Probabilities in BN 267
14.3.4 Generation of Prior Probabilities in BN 269
14.3.5 Result 270
14.4 Conclusion 270
References 271
15 Towards Prediction of Nonlinear and Nonstationary Evolution of Customer Preferences Using Local Markov Models 272
15.1 Introduction 272
15.2 Markov Modeling Approach 274
15.2.1 Nonlinear Dynamic Characterization 275
15.2.2 Pattern Analysis and Segmentation 276
15.2.2.1 Markov Model Derivation 277
15.2.2.2 Segmentation by Pattern Analysis of the Markov Transition Matrix 280
15.2.3 State and Performance Prediction 281
15.3 Implementation Details and Results 281
15.4 Comparison of the Proposed Model with CommonlyUsed Stationary Models 283
15.5 Conclusions 285
References 286
16 Two Period Product Choice Models for Commercial Vehicles 289
16.1 Introduction 289
16.2 Literature Review 290
16.3 Formulating Two Period Product ChoiceModels: Application in Commercial Vehicles 291
16.3.1 Input to the Models 291
16.3.2 Modeling the Customers' Product Choice Decision 292
16.4 Choice of Product Line Model for Commercial Vehicles Over Two Periods-Boom and Recession 293
16.4.1 Customer Choice Constraints 294
16.5 Managerial Implication of the Results 296
16.6 Discussion 297
Appendix 298
References 300
Index 302