Masters Theses

Abstract

"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.

Advisor(s)

Takai, Shun

Committee Member(s)

Du, Xiaoping
Chandrashekhara, K.

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Mechanical Engineering

Sponsor(s)

Missouri University of Science and Technology. Department of Mechanical Engineering
Missouri University of Science and Technology. Intelligent Systems Center

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2011

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

Pagination

xi, 59 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2011 Swithin Samuel Razu, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Library of Congress Subject Headings

Business forecasting
Consumers' preferences -- Forecasting
New products

Thesis Number

T 9856

Print OCLC #

792892358

Electronic OCLC #

909372123

Link to Catalog Record

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://laurel.lso.missouri.edu/record=b8545174~S5

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