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Research Papers: Design Automation

Generating Technology Evolution Prediction Intervals Using a Bootstrap Method

[+] Author and Article Information
Guanglu Zhang

Mem. ASME
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77840
e-mail: glzhang@tamu.edu

Douglas Allaire

Mem. ASME
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77840
e-mail: dallaire@tamu.edu

Daniel A. McAdams

Fellow ASME
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77840
e-mail: dmcadams@tamu.edu

Venkatesh Shankar

Center for Retailing Studies,
Mays Business School,
Texas A&M University,
College Station, TX 77840
e-mail: vshankar@mays.tamu.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 29, 2018; final manuscript received October 24, 2018; published online January 31, 2019. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 141(6), 061401 (Jan 31, 2019) (9 pages) Paper No: MD-18-1341; doi: 10.1115/1.4041860 History: Received April 29, 2018; Revised October 24, 2018

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 (CPU) 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.

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Figures

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Fig. 1

CPU transistor count evolution prediction intervals from 2014 to 2018

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Fig. 2

Passenger airplane performance evolution prediction from 2004 to 2008

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Fig. 3

CPU transistor count data during 1970–2018

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Fig. 4

Histogram of C01/a0 distribution in Eq. (20)

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Fig. 5

Histogram of C10/a1 distribution in Eq. (21)

Tables

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