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Product Research

The Art and Science Behind Successful Product Launches

  • Conference proceedings
  • © 2009

Overview

Editors:
  1. N. R. Srinivasa Raghavan

    1. General Motors India Pvt. Ltd, Bangalore, India

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  2. John A. Cafeo

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

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    You can also search for this editor in PubMed   Google Scholar

  • The first book of its kind giving a fair treatment to both quantitative and qualitative methodologies relevant to research in product development
  • Articles are the result of research pursued by top notch researchers and practitioners from around the world

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Table of contents (16 papers)

  1. Front Matter

    Pages i-xii
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  2. Innovation and information sharing in product design

    1. Front Matter

      Pages 1-1
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    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
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    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

  5. Quantitative methods for product planning

    1. Front Matter

      Pages 258-258
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    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
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Keywords

About this book

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.

Editors and Affiliations

  • 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

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