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Design of Experiments: A Key to Successful Innovation

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Frontiers in Statistical Quality Control 12

Part of the book series: Frontiers in Statistical Quality Control ((FSQC))

Abstract

An important theme in this chapter is that the use of statistical methodology, such as design of experiments, can aid innovation. Design of experiments is viewed as part of a process for enabling both breakthrough innovation and incremental innovation, without which Western society will fail to be competitive. Quality engineering technology in general is part of a broader approach to innovation and business improvement called statistical engineering. The most powerful statistical technique in statistical engineering is design of experiments. Several important developments in this field are reviewed, the role of designed experiments in innovation examined, and new developments and applications of the methods discussed.

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References

  • Anderson-Cook, C. M., Lu, L., Clark, G., DeHart, S. P., Hoerl, R., Jones, B., et al. (2012a). Statistical engineering—Forming the foundations. Quality Engineering, 24(2), 110–132.

    Article  Google Scholar 

  • Anderson-Cook, C. M., Lu, L., Clark, G., DeHart, S. P., Hoerl, R., Jones, B., et al. (2012b). Statistical engineering—Roles for statisticians and the path forward. Quality Engineering, 24(2), 133–152.

    Article  Google Scholar 

  • Antony, J., Coleman, S., Montgomery, D. C., Anderson, M. J., & Silverstrini, R. T. (2011). Design of experiments for non-manufacturing processes: Benefits, challenges and some examples. Journal of Engineering Manufacture, 225(11), 2088–2095.

    Article  Google Scholar 

  • Bisgaard, S. (2012). The future of quality technology: From a manufacturing to a knowledge economy & from defects to innovations. Quality Engineering, 24(1), 30–36.

    Article  Google Scholar 

  • Box, G. E. P. (1990). Do interactions matter? Center for Quality and Productivity Improvement, University of Wisconsin, Madison.

    Google Scholar 

  • Box, G. E. P., & Woodall, W. H. (2012). Innovation, quality engineering, and statistics. Quality Engineering, 24(1), 20–29.

    Article  Google Scholar 

  • Coleman, D. E., & Montgomery, D. C. (1993). A systematic approach to planning for a designed industrial experiment (with discussion). Technometrics, 35(1), 1–27.

    Article  Google Scholar 

  • Griffin, A., Price, R., & Vojak, B. (2012). Serial innovators: How individuals create and deliver breakthrough innovations in mature firms. Stanford, CA: Stanford University Press.

    Google Scholar 

  • Hockman, K. K., & Jensen, W. A. (2016). Statisticians as innovation leaders. Quality Engineering, 28(2), 165–174.

    Article  Google Scholar 

  • Hoerl, R. W., & Snee, R. D. (2010a). Moving the statistics profession forward to the next level. The American Statistician, 64(1), 10–14.

    Article  MathSciNet  Google Scholar 

  • Hoerl, R. W., & Snee, R. D. (2010b). Closing the gap: Statistical engineering can bridge statistical thinking with methods and tools. Quality Progress, 43(5), 52–53.

    Google Scholar 

  • Johnson, R. T., Montgomery, D. C., & Jones, B. (2011). An empirical study of the prediction performance of space-filling designs. International Journal of Experimental Design and Process Optimization, 2, 1–18.

    Article  Google Scholar 

  • Johnson, R. T., Montgomery, D. C., Jones, B., & Parker, P. A. (2010). Comparing computer experiments for fitting high-order polynomial models. Journal of Quality Technology, 42(1), 86–102.

    Article  Google Scholar 

  • Jones, B., & Johnson, R. T. (2009). Design and analysis for the gaussian process model. Quality and Reliability Engineering International, 25, 515–524.

    Article  Google Scholar 

  • Jones, B., & Montgomery, D. C. (2010). Alternatives to resolution IV screening designs in 16 runs. International Journal of Experimental Design and Process Optimization, 1(4), 285–295.

    Article  Google Scholar 

  • Jones, B., & Nachtsheim, C. J. (2011). A class of three-level designs for definitive screening in the presence of second-order effects. Journal of Quality Technology, 43, 1–15.

    Article  Google Scholar 

  • Jones, B., Shinde, S. M., & Montgomery, D. C. (2015a). Alternatives to resolution III regular fractional factorial designs for 9–14 factors in 16 runs. Applied Stochastic Models in Business and Industry, 31, 50–58.

    Article  MathSciNet  Google Scholar 

  • Jones, B., Silvestrini, R. T., Montgomery, D. C., & Steinberg, D. M. (2015b). Bridge designs for modeling systems with low noise. Technometrics, 57(2), 155–163.

    Article  MathSciNet  Google Scholar 

  • Krishnamoorthy, A., Montgomery, D. C., Jones, B., & Borror, C. M. (2015). Analyzing no-confounding designs using the Dantzig selector. International Journal of Experimental Design and Process Optimization, 4, 183–205.

    Article  Google Scholar 

  • Montgomery, D. C. (1992). The use of statistical process control and design of experiments in product and process development. IIE Transactions, 24(5), 4–17.

    Article  Google Scholar 

  • Montgomery, D. C. (1999). Experimental design for product and process design and development (with commentary). Journal of the Royal Statistical Society Series D (The Statistician), 48, Part 2, 159–177.

    Google Scholar 

  • Montgomery, D. C. (2012). Design and analysis of experiments (8th ed.). Hoboken, NJ: Wiley

    Google Scholar 

  • Montgomery, D. C., & Runger, G. C. (2014). Applied statistics and probability for engineers (6th ed.). New York: John Wiley & Sons

    MATH  Google Scholar 

  • Montgomery, D. C., & Woodall, W. H. (2008). An overview of six sigma. International Statistical Review, 76(3), 329–346.

    Article  Google Scholar 

  • Shinde, S. M., Montgomery, D. C., & Jones, B. (2014). Projection properties of no-confounding designs for six, seven, and eight factors in 16 runs. International Journal of Experimental Design and Process Optimization, 4(1), 1–26.

    Article  Google Scholar 

  • Silvestrini, R. T., Montgomery, D. C., & Jones, B. (2013). Comparing computer experiments for the Gaussian process model using integrated prediction variance. Quality Engineering, 25(2), 164–174.

    Article  Google Scholar 

  • Thomke, S. H. (2003a). Experimentation matters: Unlocking the potential of new technologies for innovation. Boston: Harvard Business Press.

    Google Scholar 

  • Thomke, S. H. (2003b). R&D comes to service. Harvard Business Review, 81(4), 70–79.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

    Douglas C. Montgomery

  2. Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY, USA

    Rachel T. Silvestrini

Authors
  1. Douglas C. Montgomery
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  2. Rachel T. Silvestrini
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Corresponding author

Correspondence to Rachel T. Silvestrini .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, Helmut Schmidt University, Hamburg, Germany

    Sven Knoth

  2. Department of Statistics, European University Viadrina, Frankfurt (Oder), Germany

    Wolfgang Schmid

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Montgomery, D.C., Silvestrini, R.T. (2018). Design of Experiments: A Key to Successful Innovation. In: Knoth, S., Schmid, W. (eds) Frontiers in Statistical Quality Control 12. Frontiers in Statistical Quality Control. Springer, Cham. https://doi.org/10.1007/978-3-319-75295-2_15

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