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Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm

Author

Listed:
  • Daniela F. Dianda

    (Universidad Nacional de Rosario-CONICET)

  • Marta B. Quaglino

    (Universidad Nacional de Rosario)

  • José A. Pagura

    (Universidad Nacional de Rosario)

Abstract

Current industrial processes are sophisticated enough to be tied to only one quality variable to describe the process result. Instead, many process variables need to be analyze together to assess the process performance. In particular, multivariate process capability analysis (MPCIs) has been the focus of study during the last few decades, during which many authors proposed alternatives to build the indices. These measures are extremely attractive to people in charge of industrial processes, because they provide a single measure that summarizes the whole process performance regarding its specifications. In most practical applications, these indices are estimated from sampling information collected by measuring the variables of interest on the process outcome. This activity introduces an additional source of variation to data, that needs to be considered, regarding its effect on the properties of the indices. Unfortunately, this problem has received scarce attention, at least in the multivariate domain. In this paper, we study how the presence of measurement errors affects the properties of one of the MPCIs recommended in previous researches. The results indicate that even little measurement errors can induce distortions on the index value, leading to wrong conclusions about the process performance.

Suggested Citation

  • Daniela F. Dianda & Marta B. Quaglino & José A. Pagura, 2018. "Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 117-143, January.
    • Handle: RePEc:spr:alstar:v:102:y:2018:i:1:d:10.1007_s10182-017-0295-2
    DOI: 10.1007/s10182-017-0295-2
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    References listed on IDEAS

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    1. Wang, F. K. & Du, T. C. T., 2000. "Using principal component analysis in process performance for multivariate data," Omega, Elsevier, vol. 28(2), pages 185-194, April.
    2. Michele Scagliarini, 2011. "Multivariate process capability using principal component analysis in the presence of measurement errors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 113-128, June.
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