Product Research

1. Front Matter

   Pages i-xii

   PDF

2. Innovation and information sharing in product design

   1. Front Matter

      Pages 1-1

      PDF

   2. Improving Intuition in Product Development Decisions

      • Jeffrey Hartley

      Pages 3-16

   3. Design Creativity Research

      • Amaresh Chakrabarti

      Pages 17-39

   4. User Experience-Driven Wireless Services Development

      • Jee Y. Park, Giridhar D. Mandyam

      Pages 41-65

   5. Integrating Distributed Design Information in Decision-Based Design

      • Justin A. Rockwell, Sundar Krishnamurty, Ian R. Grosse, Jack Wileden

      Pages 67-90

3. Decision making in engineering design

   1. Front Matter

      Pages 92-92

      PDF

   2. The Mathematics of Prediction

      • George A. Hazelrigg

      Pages 93-111

   3. An Exploratory Study of Simulated Decision-Making in Preliminary Vehicle Design

      • Joseph A. Donndelinger, John A. Cafeo, Robert L. Nagel

      Pages 113-133

   4. Dempster-Shafer Theory in the Analysis and Design of Uncertain Engineering Systems

      • S. S. Rao, Kiran K. Annamdas

      Pages 135-160

   5. Role of Robust Engineering in Product Development

      • Rajesh Jugulum

      Pages 161-176

   6. Distributed Collaborative Designs: Challenges and Opportunities

      • Abhijit Deshmukh, Timothy Middelkoop, Chandrasekar Sundaram

      Pages 179-198

4. Customer driven product definition

   1. Front Matter

      Pages 178-178

      PDF

   2. Challenges in Integrating Voice of the Customer in Advanced Vehicle Development Process – A Practitioner’s Perspective

      • Srinivasan Rajagopalan

      Pages 199-209

   3. A Statistical Framework for Obtaining Weights in Multiple Criteria Evaluation of Voices of Customer

      • R. P. Suresh, Anil K. Maddulapalli

      Pages 211-229

   4. Text Mining of Internet Content: The Bridge Connecting Product Research with Customers in the Digital Era

      • S. Shivashankar, B. Ravindran, N. R. Srinivasa Raghavan

      Pages 231-242

5. Quantitative methods for product planning

   1. Front Matter

      Pages 258-258

      PDF

   2. A Combined QFD and Fuzzy Integer Programming Framework to Determine Attribute Levels for Conjoint Study

      • Malay Bhattacharyya, Atanu Chaudhuri

      Pages 243-256

   3. Project Risk Modelling and Assessment in New Product Development

      • Jian-Bo Yang, Dawei Tang, Dong-Ling Xu, Kwai-Sang Chin

      Pages 259-269

   4. Towards Prediction of Nonlinear and Nonstationary Evolution of Customer Preferences Using Local Markov Models

      • Satish T. S. Bukkapatnam, Hui Yang, Foad Madhavi

      Pages 271-287

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.

• Evaluation
• Launch
• design
• engineering design
• modeling
• product design

• General Motors India Pvt. Ltd, Bangalore, India

  N. R. Srinivasa Raghavan

• Manufacturing Systems Research Lab., General Motors R & D Center, Warren, U.S.A.

  John A. Cafeo

Policies and ethics