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Capability adjustment for gamma processes with mean shift consideration in implementing Six Sigma program

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  • Hsu, Ya-Chen
  • Pearn, W.L.
  • Wu, Pei-Ching

Abstract

In the 1980s, Motorola, Inc. introduced its Six Sigma quality program to the world. Some quality practitioners questioned why the Six Sigma advocates claim it is necessary to add a 1.5[sigma] shift to the process mean when estimating process capability. Bothe [Bothe, D.R., 2002. Statistical reason for the 1.5[sigma] shift. Quality Engineering 14 (3), 479-487] provided a statistical reason for considering such a shift in the process mean for normal processes. In this paper, we consider gamma processes which cover a wide class of applications. For fixed sample size n, the detection power of the control chart can be computed. For small process mean shifts, it is beyond the control chart detection power, which results in overestimating process capability. To resolve the problem, we first examine Bothe's approach and find the detection power is less than 0.5 when data comes from gamma distribution, showing that Bothe's adjustments are inadequate when we have gamma processes. We then calculate adjustments under various sample sizes n and gamma parameter N, with power fixed to 0.5. At the end, we adjust the formula of process capability to accommodate those shifts which can not be detected. Consequently, our adjustments provide much more accurate capability calculation for gamma processes. For illustration purpose, an application example is presented.

Suggested Citation

  • Hsu, Ya-Chen & Pearn, W.L. & Wu, Pei-Ching, 2008. "Capability adjustment for gamma processes with mean shift consideration in implementing Six Sigma program," European Journal of Operational Research, Elsevier, vol. 191(2), pages 517-529, December.
  • Handle: RePEc:eee:ejores:v:191:y:2008:i:2:p:517-529
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    Citations

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    Cited by:

    1. Nourelfath, Mustapha & Hassan, Jawad, 2015. "Six Sigma performance for non-normal processesAuthor-Name: Aldowaisan, Tariq," European Journal of Operational Research, Elsevier, vol. 247(3), pages 968-977.
    2. Wang, Xiaofei & Wang, Bing Xing & Hong, Yili & Jiang, Pei Hua, 2021. "Degradation data analysis based on gamma process with random effects," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1200-1208.
    3. CHEN, Piao & YE, Zhi-Sheng, 2018. "A systematic look at the gamma process capability indices," European Journal of Operational Research, Elsevier, vol. 265(2), pages 589-597.
    4. Jana Fabianová & Jaroslava Janeková & Daniela Onofrejová, 2017. "Cost Analysis of Poor Quality Using a Software Simulation," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 19(44), pages 181-181, February.
    5. Mustapha Nourelfath & Tariq Aldowaisan & Jawad Hassan, 2016. "Evaluating Six Sigma failure rate for inverse Gaussian cycle times," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6092-6101, October.
    6. Pérez-González, Carlos J. & Fernández, Arturo J. & Kohansal, Akram, 2020. "Efficient truncated repetitive lot inspection using Poisson defect counts and prior information," European Journal of Operational Research, Elsevier, vol. 287(3), pages 964-974.
    7. Lepore, A. & Palumbo, B. & Castagliola, P., 2018. "A note on decision making method for product acceptance based on process capability indices Cpk and Cpmk," European Journal of Operational Research, Elsevier, vol. 267(1), pages 393-398.
    8. V�ctor Leiva & Carolina Marchant & Helton Saulo & Muhammad Aslam & Fernando Rojas, 2014. "Capability indices for Birnbaum-Saunders processes applied to electronic and food industries," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 1881-1902, September.

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