Product Research: The Art and Science Behind Successful Product Launches by N.R.

Description: Product Research by N.R. Srinivasa Raghavan, John A. Cafeo 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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. Back Cover New product development is a highly creative exercise, often involving an interdisciplinary decision making and execution process. This book is a compendium of research articles on various issues concerning product research, written by research scholars both from academia and industry. The papers have been divided into four main parts. The first part is on innovation and information sharing in product design. The second part deals with decision making in engineering design. The third part treats customer driven product definition, and the last part brings together articles on quantitative methods for product planning. Table of Contents Innovation and information sharing in product design.- Improving Intuition in Product Development Decisions.- Design Creativity Research.- User Experience-Driven Wireless Services Development.- Integrating Distributed Design Information in Decision-Based Design.- Decision making in engineering design.- The Mathematics of Prediction.- An Exploratory Study of Simulated Decision-Making in Preliminary Vehicle Design.- Dempster-Shafer Theory in the Analysis and Design of Uncertain Engineering Systems.- Role of Robust Engineering in Product Development.- Distributed Collaborative Designs: Challenges and Opportunities.- Customer driven product definition.- Challenges in Integrating Voice of the Customer in Advanced Vehicle Development Process – A Practitioners Perspective.- A Statistical Framework for Obtaining Weights in Multiple Criteria Evaluation of Voices of Customer.- Text Mining of Internet Content: The Bridge Connecting Product Research with Customers in the Digital Era.- Quantitative methods for product planning.- A Combined QFD and Fuzzy Integer Programming Framework to Determine Attribute Levels for Conjoint Study.- Project Risk Modelling and Assessment in New Product Development.- Towards Prediction of Nonlinear and Nonstationary Evolution of Customer Preferences Using Local Markov Models.- Two Period Product Choice Models for Commercial Vehicles. Long Description 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 classied 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. Feature 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 Details ISBN9400791046 ISBN-10 9400791046 ISBN-13 9789400791046 Format Paperback Publisher Springer Year 2014 Imprint Springer Subtitle The Art and Science Behind Successful Product Launches Place of Publication Dordrecht Country of Publication Netherlands Publication Date 2014-11-29 Edited by N. R. Srinivasa Raghavan Short Title PRODUCT RESEARCH 2009/E Language English Media Book Edition 2009th DEWEY 658.5038 Illustrations XII, 305 p. Pages 305 Author John A. Cafeo Edition Description 2009 ed. Alternative 9789048128594 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. 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