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Analysis of robust design experiments with time-dependent ordinal response characteristics: a quality improvement study from the horticulture industry

Author

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  • N. R. Parsons
  • S. G. Gilmour
  • R. N. Edmondson

Abstract

An approach to the analysis of time-dependent ordinal quality score data from robust design experiments is developed and applied to an experiment from commercial horticultural research, using concepts of product robustness and longevity that are familiar to analysts in engineering research. A two-stage analysis is used to develop models describing the effects of a number of experimental treatments on the rate of post-sales product quality decline. The first stage uses a polynomial function on a transformed scale to approximate the quality decline for an individual experimental unit using derived coefficients and the second stage uses a joint mean and dispersion model to investigate the effects of the experimental treatments on these derived coefficients. The approach, developed specifically for an application in horticulture, is exemplified with data from a trial testing ornamental plants that are subjected to a range of treatments during production and home-life. The results of the analysis show how a number of control and noise factors affect the rate of post-production quality decline. Although the model is used to analyse quality data from a trial on ornamental plants, the approach developed is expected to be more generally applicable to a wide range of other complex production systems.

Suggested Citation

  • N. R. Parsons & S. G. Gilmour & R. N. Edmondson, 2009. "Analysis of robust design experiments with time-dependent ordinal response characteristics: a quality improvement study from the horticulture industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(9), pages 1037-1054.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:1037-1054
    DOI: 10.1080/02664760802566796
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    References listed on IDEAS

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    1. J. A. Nelder & Y. Lee, 1991. "Generalized linear models for the analysis of taguchi‐type experiments," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 7(1), pages 107-120, March.
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