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research-article

Generating Technology Evolution Prediction Intervals Using a Bootstrap Method

[+] Author and Article Information
Guanglu Zhang

ASME Member, Department of Mechanical Engineering, Texas A&M University, College Station, Texas, 77840, USA
glzhang@tamu.edu

Douglas Allaire

ASME Member, Department of Mechanical Engineering, Texas A&M University, College Station, Texas, 77840, USA
dallaire@tamu.edu

Daniel A. McAdams

ASME Fellow, Department of Mechanical Engineering, Texas A&M University, College Station, Texas, 77840, USA
dmcadams@tamu.edu

Venkatesh Shankar

Center for Retailing Studies, Mays Business School, Texas A&M University, College Station, Texas, 77840, USA
vshankar@mays.tamu.edu

1Corresponding author.

ASME doi:10.1115/1.4041860 History: Received April 29, 2018; Revised October 24, 2018

Abstract

Technology evolution prediction is critical for designers, business managers, and entrepreneurs to make important decisions during product development planning such as R&D investment and outsourcing. In practice, designers want to supplement point forecasts with prediction intervals to assess future uncertainty and make contingency plans accordingly. However, prediction intervals generation for technology evolution has received scant attention in the literature. In this paper, we develop a generic method that uses bootstrapping to generate prediction intervals for technology evolution. The method we develop can be applied to any model that describes technology performance incremental change. We consider parameter uncertainty and data uncertainty and establish their empirical probability distributions. We determine an appropriate confidence level to generate prediction intervals through a holdout sample analysis rather than specify that the confidence level equals 0.05 as is typically done in the literature. In addition, our method provides the probability distribution of each parameter in a prediction model. The probability distribution is valuable when parameter values are associated with the impact factors of technology evolution. We validate our method to generate prediction intervals through two case studies of central processing units and passenger airplanes. These case studies show that the prediction intervals generated by our method cover every actual data point in the holdout sample tests. We outline four steps to generate prediction intervals for technology evolution prediction in practice.

Copyright (c) 2018 by ASME
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