Product Research: The Art and Science Behind Successful Product Launches

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.

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 Practitioner’s 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.