Research Papers: Design Automation

Generating Technology Evolution Prediction Intervals Using a Bootstrap Method

[+] Author and Article Information
Guanglu Zhang

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

Douglas Allaire

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.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.


Otto, K. N. , and Wood, K. L. , 2001, Product Design: Techniques in Reverse Engineering and New Product Development, Prentice Hall, Upper Saddle River, NJ.
Betz, F. , 1993, Strategic Technology Management, McGraw-Hill, New York.
Chatfield, C. , 1993, “ Calculating Interval Forecasts,” J. Bus. Econ. Stat., 11(2), pp. 121–135.
Schaller, R. R. , 1997, “ Moore's Law: Past, Present and Future,” IEEE Spectrum, 34(6), pp. 52–59. [CrossRef]
Modis, T. , 2014, An S-Shaped Adventure: Predictions—20 Years Later, Growth Dynamics, Lugano, Switzerland.
Zhang, G. , McAdams, D. A. , Shankar, V. , and Mohammadi Darani, M. , 2018, “ Technology Evolution Prediction Using Lotka–Volterra Equations,” ASME J. Mech. Des., 140(6), p. 061101. [CrossRef]
Efron, B. , 1979, “ Bootstrap Methods: Another Look at the Jackknife,” Ann. Stat., 7(1), pp. 1–26. [CrossRef]
Mooney, C. , and Duval, R. , 1993, Bootstrapping: A Nonparametric Approach to Statistical Inference, SAGE Publications, Newbury Park, CA.
Efron, B. , and Tibshirani, R. , 1986, “ Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy,” Stat. Sci., 1(1), pp. 54–75. [CrossRef]
Chatfield, C. , 1996, “ Model Uncertainty and Forecast Accuracy,” J. Forecasting, 15(7), pp. 495–508. [CrossRef]
Chatfield, C. , 1988, “ What is the ‘Best’ Method of Forecasting?,” J. Appl. Stat., 15(1), pp. 19–38. [CrossRef]
Hyndman, R. J. , and Athanasopoulos, G. , 2014, Forecasting: Principles and Practice, OTexts, Columbia, SC.
Montgomery, D. C. , Peck, E. A. , and Vining, G. G. , 2006, Introduction to Linear Regression Analysis, Wiley-Interscience, Hoboken, NJ.
Shumway, R. H. , and Stoffer, D. S. , 2011, Time Series Analysis and Its Applications, Springer, New York.
Kiureghian, A. D. , and Ditlevsen, O. , 2009, “ Aleatory or Epistemic? Does It Matter?,” Struct. Saf., 31(2), pp. 105–112. [CrossRef]
Chatfield, C. , 2004, The Analysis of Time Series, Chapman & Hall/CRC, Boca Raton, FL.
de Gooijer, J. G. , Abraham, B. , Gould, A. , and Robinson, L. , 1985, “ Methods for Determining the Order of an Autoregressive-Moving Average Process: A Survey,” Int. Stat. Rev./Revue Internationale de Statistique, 53(3), pp. 301–329.
Harvey, A. C. , 1990, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge, UK.
Saltelli, A. , Chan, K. , and Scott, E. M. , 2009, Sensitivity Analysis, Wiley, New York.
Congdon, P. , 2014, Applied Bayesian Modelling, Wiley, Chichester, UK.
Williams, W. H. , and Goodman, M. L. , 1971, “ A Simple Method for the Construction of Empirical Confidence Limits for Economic Forecasts,” J. Am. Stat. Assoc., 66(336), pp. 752–754. [CrossRef]
Makridakis, S. , Hibon, M. , Lusk, E. , and Belhadjali, M. , 1987, “Confidence Intervals: An Empirical Investigation of the Series in the M-Competition,” Int. J. Forecasting, 3(3–4), pp. 489–508. [CrossRef]
Thombs, L. A. , and Schucany, W. R. , 1990, “ Bootstrap Prediction Intervals for Autoregression,” J. Am. Stat. Assoc., 85(410), pp. 486–492. [CrossRef]
Ghysels, E. , and Jasiak, J. , 1997, GARCH for Irregularly Spaced Financial Data: The ACD-GARCH Model, CIRANO, Montreal, QC, Canada.
Anderson, P. , and Tushman, M. L. , 1990, “ Technological Discontinuities and Dominant Designs: A Cyclical Model of Technological Change,” Adm. Sci. Q., (4), pp. 604–633.
Sahal, D. , 1985, “ Technological Guideposts and Innovation Avenues,” Res. Policy, 14(2), pp. 61–82. [CrossRef]
Christensen, C. , 2013, The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail, Harvard Business Review Press, Cambridge, MA.
Ghasemi, A. , and Zahediasl, S. , 2012, “ Normality Tests for Statistical Analysis: A Guide for Non-Statisticians,” Int. J. Endocrinol. Metabolism, 10(2), pp. 486–489. [CrossRef]
Meade, N. , and Islam, T. , 1998, “ Technological Forecasting—Model Selection, Model Stability, and Combining Models,” Manage. Sci., 44(8), pp. 1115–1130. [CrossRef]
Farmer, J. D. , and Lafond, F. , 2016, “ How Predictable is Technological Progress?,” Res. Policy, 45(3), pp. 647–665. [CrossRef]
Arendt, J. L. , McAdams, D. A. , and Malak, R. J. , 2012, “ Uncertain Technology Evolution and Decision Making in Design,” ASME J. Mech. Des., 134(10), p. 100904. [CrossRef]
Naim, A. M. , and Lewis, K. , 2017, “ Modeling the Dynamics of Innovation in Engineered Systems,” ASME Paper No. DETC2017-68180.
Nagy, B. , Farmer, J. D. , Bui, Q. M. , and Trancik, J. E. , 2013, “ Statistical Basis for Predicting Technological Progress,” PLoS ONE, 8(2), p. e52669. [CrossRef] [PubMed]
Young, P. , 1993, “ Technological Growth Curves: A Competition of Forecasting Models,” Technol. Forecasting Soc. Change, 44(4), pp. 375–389. [CrossRef]
Draper, D. , 1995, “ Assessment and Propagation of Model Uncertainty,” J. R. Stat. Soc. Ser. B (Methodol.), 57(1), pp. 45–97. https://www.jstor.org/stable/2346087
Freedman, D. A. , 1981, “ Bootstrapping Regression Models,” Ann. Stat., 9(6), pp. 1218–1228. [CrossRef]
Meade, N. , and Islam, T. , 1995, “ Prediction Intervals for Growth Curve Forecasts,” J. Forecasting, 14(5), pp. 413–430. [CrossRef]
Rohatgi, V. K. , 1984, Statistical Inference, Wiley, New York.
Zhang, G. , McAdams, D. A. , Shankar, V. , and Darani, M. M. , 2017, “ Modeling the Evolution of System Technology Performance When Component and System Technology Performances Interact: Commensalism and Amensalism,” Technol. Forecasting Soc. Change, 125, pp. 116–124. [CrossRef]
Iatrou, K. , 2014, 100 Years of Commercial Aviation, HERMES Air Transport Club, Montreal, QC, Canada.
Daly, M. , and Gunston, B. , 2008, Jane's Aero-Engines, Jane's Information Group Limited, Surry, UK.
Wilkinson, P. H. , 1970, Aircraft Engines of the World 1970, Paul H. Wilkinson, Washington, DC.
Moré, J. , and Sorensen, D. , 1983, “ Computing a Trust Region Step,” SIAM J. Sci. Stat. Comput., 4(3), pp. 553–572. [CrossRef]
Dormand, J. R. , and Prince, P. J. , 1980, “ A Family of Embedded Runge-Kutta Formulae,” J. Comput. Appl. Math., 6(1), pp. 19–26. [CrossRef]
Lapidus, L. , and Seinfeld, J. H. , 1971, Numerical Solution of Ordinary Differential Equations, Academic Press, New York.


Grahic Jump Location
Fig. 1

CPU transistor count evolution prediction intervals from 2014 to 2018

Grahic Jump Location
Fig. 2

Passenger airplane performance evolution prediction from 2004 to 2008

Grahic Jump Location
Fig. 3

CPU transistor count data during 1970–2018

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
Fig. 5

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



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In