"Analysis of customer preferences is one of the most important tasks in new product development. How customers come to appreciate and decide to purchase a new product impacts market share and, therefore, the success of the new product. Unfortunately, when designers select a product concept early in the product development process, the "true" customer preferences and therefore, market share of the new product is unknown. Conjoint analysis is a statistical methodology that has been used to forecast the market share of a product concept from customer preference survey data. Although conjoint analysis has been increasingly incorporated in design research as a tool to forecast market share of a new product design, market share uncertainty modeling using customer preference survey data has not been fully explored. The first paper compares two approaches for market share uncertainty modeling that use conjoint analysis data: bootstrap and binomial inference. Demonstration and comparison of the two approaches are presented using an illustrative example. The second paper demonstrates and compares two approaches that use conjoint analysis data to model demand uncertainty: bootstrap of respondent choice data and Monte Carlo simulation of utility estimation errors. Reliability of demand distribution and accuracy of demand estimation are compared for the two approaches in an illustrative example"--Abstract, page iv.
Mechanical and Aerospace Engineering
M.S. in Mechanical Engineering
Missouri University of Science and Technology. Department of Mechanical Engineering
Missouri University of Science and Technology. Intelligent Systems Center
Intelligent Systems Center
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Comparison of customer-preference uncertainty modeling for product concept selection
- Reliability and accuracy of bootstrap and Monte Carlo methods for demand distribution modeling
xi, 59 pages
© 2011 Swithin Samuel Razu, All rights reserved.
Thesis - Restricted Access
Consumers' preferences -- Forecasting
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Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu/record=b8545174~S5
Razu, Swithin Samuel, "A comparison of bootstrap, binomial inference, and Monte Carlo methods for demand distribution modeling" (2011). Masters Theses. 4455.
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