Comparing the Predictive Ability of T-method and Cobb-Douglas Production Function for Warranty Data

Abstract

Predictive models are used in an attempt to anticipate future transitions, mitigate losses, and maximize economic gains. In today's market, companies look for high reliability and quality of products due to great market competition. Hence warranty data is of considerable interest to companies. Warranty shows the ability of a system or component to perform its functions within a given customer usage. Many statistical and data mining methods are available to predict the warranty data. This study focuses on analyzing the predictive efficiency of the T-method and Cobb-Douglas production function on warranty data by comparing their prediction capability. The T-method, developed by Genichi Taguchi, is founded upon the fundamentals of the Taguchi System of Quality Engineering which is used to calculate an overall prediction based on signal-to-noise ratio. Using this method, the required parameters are calculated to obtain an overall estimate of the true value of the output for each signal member. The CobbDouglas production function is then applied on the same dataset. In economics, the Cobb-Douglas functional form of production function is widely used to represent the relationship of the output to inputs. The strength of the relationship is then assessed using the R-squared and adjusted R-squared values.

Department(s)

Mathematics and Statistics

Second Department

Engineering Management and Systems Engineering

Keywords and Phrases

adjusted r-squared values; cobb-douglas production function; prediction; r-squared values; t-method; warranty cost

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Nov 2009

Link to Full Text

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