Raghavan / Cafeo Product Research

The Art and Science Behind Successful Product Launches
1. Auflage 2010
ISBN: 978-90-481-2860-0
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 305 Seiten

7. 1. 1 Background Uncertainty can be considered as the lack of adequate information to make a decision. It is important to quantify uncertainties in mathematical models used for design and optimization of nondeterministic engineering systems. In general, - certainty can be broadly classi?ed into three types (Bae et al. 2004; Ha-Rok 2004; Klir and Wierman 1998; Oberkampf and Helton 2002; Sentz 2002). The ?rst one is aleatory uncertainty (also referred to as stochastic uncertainty or inherent - certainty) – it results from the fact that a system can behave in random ways. For example, the failure of an engine can be modeled as an aleatory uncertaintybecause the failure can occur at a random time. One cannot predict exactly when the engine will fail even if a large quantity of failure data is gathered (available). The second one is epistemic uncertainty (also known as subjective uncertainty or reducible - certainty) – it is the uncertainty of the outcome of some random event due to lack of knowledge or information in any phase or activity of the modeling process. By gaining information about the system or environmental factors, one can reduce the epistemic uncertainty. For example, a lack of experimental data to characterize new materials and processes leads to epistemic uncertainty.

Autoren/Hrsg.

Raghavan, N. R. Srinivasa

Cafeo, John A.

Inhaltsverzeichnis

1;Editorial;5
1.1;1 Motivation for this Book;5
1.2;2 Summary of Research Articles;5
1.2.1;2.1 Innovation and Information Sharing in Product Design;6
1.2.2;2.2 Decision Making in Engineering Design;6
1.2.3;2.3 Customer Driven Product Definition;7
1.2.4;2.4 Quantitative Methods for Product Planning;8
2;Acknowledgements;9
3;Contents
;11
4;Part I Innovation and Information Sharing in Product Design;13
4.1;1 Improving Intuition in Product Development Decisions;14
4.1.1;1.1 The Goal of Market Research Is to Create Early and Accurate Intuition That Is Shared Across Functions;17
4.1.1.1;1.1.1 What Is Intuition?;17
4.1.2;1.2 Intuition Is the Abstract Knowledge That Comes Automatically from Guided Experiences – A Trainable Skill, Often ``Beyond Words'';18
4.1.2.1;1.2.1 How Does One Nurture Intuition?;19
4.1.2.2;1.2.2 How Does One Nurture Shared Intuition?;20
4.1.2.2.1;Example 1: Inspirational Research;21
4.1.2.2.2;Example 2: Iterative Design;24
4.1.2.3;1.2.3 How Will the Nurtured Intuition Philosophy Change Company Behavior?;26
4.1.3;References;27
4.2;2 Design Creativity Research;28
4.2.1;2.1 Design, Design Research and Its Methodology;28
4.2.1.1;2.1.1 Research Clarification: Identifying Goals;30
4.2.1.2;2.1.2 Descriptive Study I: Understanding Current Situation;30
4.2.1.3;2.1.3 Prescriptive Study: Developing Support;31
4.2.1.4;2.1.4 Descriptive Study II: Evaluating Support;31
4.2.2;2.2 Objectives of This Paper;31
4.2.3;2.3 Definition and Measures for Creativity;32
4.2.3.1;2.3.1 What Is Meant by Creativity?;32
4.2.3.2;2.3.2 A `Common' Definition;32
4.2.3.3;2.3.3 `Common' Measures;34
4.2.3.3.1;2.3.3.1 Novelty;34
4.2.3.3.2;2.3.3.2 Proposed Novelty Measure and Validation;35
4.2.3.3.3;2.3.3.3 Usefulness;36
4.2.3.3.4;2.3.3.4 Proposed Usefulness Measure and Validation;37
4.2.3.3.5;2.3.3.5 Proposed Creativity Measure and Validation;37
4.2.4;2.4 Major Influences on Creativity;38
4.2.5;2.5 Effect of Search and Exploration on Creativity;40
4.2.6;2.6 How Well Do Designers Currently Explore Design Spaces?;42
4.2.7;2.7 Supporting Creativity;43
4.2.7.1;2.7.1 Idea-Inspire;43
4.2.7.2;2.7.2 Using Idea-Inspire;44
4.2.7.3;2.7.3 Evaluation;46
4.2.8;2.8 Summary and Conclusions;47
4.2.9;References;48
4.3;3 User Experience-Driven Wireless Services Development;51
4.3.1;3.1 Introduction;51
4.3.2;3.2 Persona-Based Mobile Service Design;53
4.3.3;3.3 Stakeholders;54
4.3.4;3.4 End Users;55
4.3.5;3.5 Trade Customers;55
4.3.6;3.6 Operator Users;57
4.3.7;3.7 Mobile Social Community Example;63
4.3.8;3.8 Caveats in the Use of Personas for Mobile Service Design;71
4.3.9;3.9 Conclusions;74
4.3.10;References;75
4.4;4 Integrating Distributed Design Information in Decision-Based Design;76
4.4.1;4.1 Introduction;76
4.4.2;4.2 Integrating Distributed Design Information;78
4.4.2.1;4.2.1 Emerging and Existing Information Technologies;79
4.4.2.1.1;4.2.1.1 Unicode and URI;79
4.4.2.1.2;4.2.1.2 XML;79
4.4.2.1.3;4.2.1.3 RDF;80
4.4.2.1.4;4.2.1.4 Ontology;81
4.4.2.1.5;4.2.1.5 Information Technology Summary;82
4.4.2.2;4.2.2 An Ontological Approach to Integrating Design Information;82
4.4.2.2.1;4.2.2.1 Engineering Design Ontologies;82
4.4.2.2.2;4.2.2.2 Linking Distributed Information;84
4.4.3;4.3 Modeling Decisions in a Distributed Environment;85
4.4.4;4.4 Case Study;89
4.4.4.1;4.4.1 Problem Setup;89
4.4.4.2;4.4.2 Conjoint-HoQ Method;91
4.4.4.3;4.4.3 Design of the Transfer Plate Using DSO Framework;91
4.4.4.4;4.4.4 Case Study Summary;96
4.4.5;4.5 Summary;96
4.4.6;References;97
5;Part II Decision Making in Engineering Design;100
5.1;5 The Mathematics of Prediction;101
5.1.1;5.1 Introduction;101
5.1.2;5.2 Basic Concepts;102
5.1.3;5.3 The Dutch Book;103
5.1.4;5.4 The Use of Evidence in Prediction;108
5.1.5;5.5 Stochastic Modeling;114
5.1.6;5.6 Conclusions;117
5.1.7;References;119
5.2;6 An Exploratory Study of Simulated Decision-Making in Preliminary Vehicle Design;120
5.2.1;6.1 Introduction;120
5.2.2;6.2 Prior Work;121
5.2.2.1;6.2.1 Decision Analysis;121
5.2.2.2;6.2.2 Decision Analysis Cycle;123
5.2.2.3;6.2.3 Human Aspects;124
5.2.2.3.1;6.2.3.1 State of Information;124
5.2.2.3.2;6.2.3.2 Cognition;124
5.2.2.3.3;6.2.3.3 Personality;126
5.2.3;6.3 Methodology;127
5.2.3.1;6.3.1 Method;127
5.2.3.2;6.3.2 Problem Statement;128
5.2.3.3;6.3.3 Description of Decision-Makers;129
5.2.3.3.1;6.3.3.1 Jim;129
5.2.3.3.2;6.3.3.2 Terry;129
5.2.3.3.3;6.3.3.3 Glenn;130
5.2.4;6.4 Results;130
5.2.4.1;6.4.1 Common Elements;130
5.2.4.2;6.4.2 Jim's Decision;132
5.2.4.3;6.4.3 Terry's Decision;133
5.2.4.4;6.4.4 Glenn's Decision;134
5.2.5;6.5 Discussion;135
5.2.5.1;6.5.1 State of Information;135
5.2.5.2;6.5.2 Cognition;136
5.2.5.3;6.5.3 Prior Knowledge;136
5.2.5.4;6.5.4 Personality;137
5.2.5.5;6.5.5 Decision-Analytic Principles;137
5.2.5.6;6.5.6 Evaluation of Decisions;138
5.2.6;6.6 Conclusions;139
5.2.7;References;140
5.3;7 Dempster-Shafer Theory in the Analysis and Design of Uncertain Engineering Systems;141
5.3.1;7.1 Introduction;142
5.3.1.1;7.1.1 Background;142
5.3.1.2;7.1.2 Review of Dempster Shafer Theory;143
5.3.2;7.2 Vertex Method;145
5.3.2.1;7.2.1 Computational Aspects of the Vertex Method;145
5.3.3;7.3 Analysis of a Welded Beam;146
5.3.3.1;7.3.1 Analysis with Two Uncertain Parameters;147
5.3.4;7.4 DST Methodology when Sources of EvidenceHave Different Credibilities;151
5.3.4.1;7.4.1 Solution Procedure with Weighted Dempster ShaferTheory for Interval-Valued Data (WDSTI);152
5.3.4.2;7.4.2 Analysis of a Welded Beam;152
5.3.4.3;7.4.3 Numerical Results;153
5.3.5;7.5 Evidence-Based Fuzzy Approach;154
5.3.5.1;7.5.1 -Cut Representation;154
5.3.5.2;7.5.2 Fuzzy Approach for Combining Evidences(Rao and Annamdas 2008);155
5.3.5.3;7.5.3 Computation of Bounds on the Margin of Failure/Safety;156
5.3.6;7.6 Other Combination Rules;158
5.3.6.1;7.6.1 Dempster's Rule;160
5.3.6.2;7.6.2 Yager's Rule (Yager 1987);160
5.3.6.3;7.6.3 Inagaki's Extreme Rule;161
5.3.6.4;7.6.4 Zhang's Rule;162
5.3.6.5;7.6.5 Murphy's Rule;164
5.3.6.5.1;7.6.5.1 Observations on the Results of the Automobile Safety Problem;164
5.3.7;7.7 Conclusion;165
5.3.8;References;165
5.4;8 Role of Robust Engineering in Product Development;167
5.4.1;8.1 Introduction to Robust Engineering;167
5.4.2;8.2 Concepts of Robust Engineering;169
5.4.2.1;8.2.1 Parameter Diagram (P-Diagram);169
5.4.2.2;8.2.2 Experimental Design;170
5.4.2.2.1;8.2.2.1 Types of Experiments;171
5.4.2.3;8.2.3 Signal to Noise (S/N) Ratios;171
5.4.2.4;8.2.4 Simulation Based Experiments;172
5.4.3;8.3 Case Examples;173
5.4.3.1;8.3.1 Circuit Stability Design;173
5.4.3.1.1;8.3.1.1 Classification of Factors: Control Factors and Noise Factors;173
5.4.3.1.2;8.3.1.2 Parameter Design;176
5.4.3.2;8.3.2 Robust Parameter Design of Brake System;177
5.4.3.2.1;8.3.2.1 Signal Factor and Levels;178
5.4.3.2.2;8.3.2.2 Noise Factors and Noise Strategy;178
5.4.3.2.3;8.3.2.3 Control Factor and Levels;178
5.4.3.2.4;8.3.2.4 Experimental Details;178
5.4.3.2.5;8.3.2.5 Two-Step Optimization;179
5.4.4;References;182
5.5;9 Distributed Collaborative Designs: Challenges and Opportunities;183
5.5.1;9.1 Collaborative Product Development;183
5.5.1.1;9.1.1 Issues in Distributed Collaborative Design;184
5.5.2;9.2 Negotiation Among Designers;185
5.5.2.1;9.2.1 Negotiation Framework;189
5.5.2.2;9.2.2 Analyzing Negotiation-Based Product Development;191
5.5.2.2.1;9.2.2.1 Convergence;193
5.5.2.2.2;9.2.2.2 Solution Quality;194
5.5.2.2.3;9.2.2.3 Communication;197
5.5.3;9.3 Rationality of Collaborative Designs;198
5.5.3.1;9.3.1 Rationality Tester;199
5.5.4;9.4 Summary;202
5.5.5;References;202
6;Part III Customer Driven Product Definition;203
6.1;10 Challenges in Integrating Voice of the Customer in Advanced Vehicle Development Process – A Practitioner's Perspective;204
6.1.1;10.1 Introduction;204
6.1.2;10.2 Voice of the Customer;205
6.1.3;10.3 Understanding and Interpreting the Voice of the Customer;206
6.1.3.1;10.3.1 Conjoint Analysis;206
6.1.3.2;10.3.2 S-Model;207
6.1.3.3;10.3.3 Quantitative vs. Qualitative Market Research;207
6.1.3.4;10.3.4 Kano Model;208
6.1.3.5;10.3.5 Questions;209
6.1.4;10.4 Incorporating the Voice of the Customer;210
6.1.4.1;10.4.1 Questions;211
6.1.5;10.5 Global Voice of the Customer;212
6.1.5.1;10.5.1 Questions;212
6.1.6;10.6 Conclusions;213
6.1.7;References;214
6.2;11 A Statistical Framework for Obtaining Weights in Multiple Criteria Evaluation of Voices of Customer;215
6.2.1;11.1 Introduction;215
6.2.2;11.2 Voice of Customer Prioritization Using ER Algorithm;217
6.2.2.1;11.2.1 Evidential Reasoning Algorithm;219
6.2.2.2;11.2.2 Impact of Weight of Survey;219
6.2.3;11.3 Factors Influencing the Weight of a Survey;221
6.2.3.1;11.3.1 Design for Selecting Respondents;221
6.2.3.2;11.3.2 Source for Identifying the Respondents;223
6.2.3.3;11.3.3 Credibility of Agency Conducting the Survey;223
6.2.3.4;11.3.4 Domain Experience of Respondents;224
6.2.3.5;11.3.5 Weight of a Survey;225
6.2.4;11.4 Demonstrative Example;226
6.2.4.1;11.4.1 Influence of Sampling Design on Survey Weights;226
6.2.4.2;11.4.2 Influence of Source of Respondents on Survey Weights;228
6.2.4.3;11.4.3 Influence of Agency Credibility on Survey Weights;229
6.2.4.4;11.4.4 Influence of Domain Experience on Survey Weights;229
6.2.4.5;11.4.5 Estimating Survey Weights;230
6.2.4.6;11.4.6 Application of ER Algorithm for Voice Prioritization;231
6.2.5;11.5 Summary;232
6.2.6;References;233
6.3;12 Text Mining of Internet Content: The Bridge Connecting Product Research with Customers in the Digital Era;234
6.3.1;12.1 Introduction;234
6.3.2;12.2 Overview of Web Mining Types;236
6.3.2.1;12.2.1 Information Retrieval;236
6.3.2.2;12.2.2 Natural Language Processing;238
6.3.3;12.3 Product Review;238
6.3.3.1;12.3.1 Buzz Analysis;238
6.3.3.1.1;12.3.1.1 Named Entity Recognition;239
6.3.3.1.2;12.3.1.2 Establishing a Baseline;240
6.3.3.1.3;12.3.1.3 Cleaning the Data;240
6.3.3.1.4;12.3.1.4 Weighing the Opinions;240
6.3.3.2;12.3.2 Opinion Mining;241
6.3.4;12.4 Conclusions;244
6.3.5;References;244
7;Part IV Quantitative Methods for Product Planning;246
7.1;13 A Combined QFD and Fuzzy Integer Programming Framework to Determine Attribute Levels for Conjoint Study;247
7.1.1;13.1 Introduction;247
7.1.2;13.2 Solving Fuzzy Integer Linear Programs;249
7.1.3;13.3 Converting a Fuzzy Integer Linear Programming (FILP) Problem to Parametric Integer Linear Programming (PILP) Problem;249
7.1.4;13.4 A Contraction Algorithm for Solving a PILP(Bailey and Gillett 1980);251
7.1.5;13.5 The Model Description;252
7.1.6;13.6 Application;253
7.1.7;13.7 Results;257
7.1.8;13.8 Results with Symmetric Triangular Fuzzy Numbers;258
7.1.9;References;259
7.2;14 Project Risk Modelling and Assessment in New Product Development;261
7.2.1;14.1 Introduction;261
7.2.2;14.2 The Proposed Approach to Generate the Probabilitiesin Bayesian Network;262
7.2.2.1;14.2.1 Generation of Probabilities of the Nodes without Parent;262
7.2.2.2;14.2.2 Generation of Probabilities for Nodeswith a Single Parent;263
7.2.2.3;14.2.3 Generation of Conditional Probabilitiesfor Multi-Parent Nodes;264
7.2.3;14.3 Application of the Method in Risk Evaluation of NPD;265
7.2.3.1;14.3.1 Case Description;265
7.2.3.2;14.3.2 Bayesian Network Construction;266
7.2.3.3;14.3.3 Generation of Conditional Probabilities in BN;267
7.2.3.4;14.3.4 Generation of Prior Probabilities in BN;269
7.2.3.5;14.3.5 Result;270
7.2.4;14.4 Conclusion;270
7.2.5;References;271
7.3;15 Towards Prediction of Nonlinear and Nonstationary Evolution of Customer Preferences Using Local Markov Models;272
7.3.1;15.1 Introduction;272
7.3.2;15.2 Markov Modeling Approach;274
7.3.2.1;15.2.1 Nonlinear Dynamic Characterization;275
7.3.2.2;15.2.2 Pattern Analysis and Segmentation;276
7.3.2.2.1;15.2.2.1 Markov Model Derivation;277
7.3.2.2.2;15.2.2.2 Segmentation by Pattern Analysis of the Markov Transition Matrix;280
7.3.2.3;15.2.3 State and Performance Prediction;281
7.3.3;15.3 Implementation Details and Results;281
7.3.4;15.4 Comparison of the Proposed Model with CommonlyUsed Stationary Models;283
7.3.5;15.5 Conclusions;285
7.3.6;References;286
7.4;16 Two Period Product Choice Models for Commercial Vehicles;289
7.4.1;16.1 Introduction;289
7.4.2;16.2 Literature Review;290
7.4.3;16.3 Formulating Two Period Product ChoiceModels: Application in Commercial Vehicles;291
7.4.3.1;16.3.1 Input to the Models;291
7.4.3.2;16.3.2 Modeling the Customers' Product Choice Decision;292
7.4.4;16.4 Choice of Product Line Model for Commercial Vehicles Over Two Periods-Boom and Recession;293
7.4.4.1;16.4.1 Customer Choice Constraints;294
7.4.5;16.5 Managerial Implication of the Results;296
7.4.6;16.6 Discussion;297
7.4.7;Appendix;298
7.4.8;References;300
8;Index;302