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Quality control and improvement for multistage systems: A survey

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  • Jianjun Shi
  • Shiyu Zhou

Abstract

A multistage system refers to a system consisting of multiple components, stations or stages required to finish the final product or service. Multistage systems are very common in practice and include a variety of modern manufacturing and service systems. In most cases, the quality of the final product or service produced by a multistage system is determined by complex interactions among multiple stages—the quality characteristics at one stage are not only influenced by local variations at that stage, but also by variations propagated from upstream stages. Multistage systems present significant challenges, yet also opportunities for quality engineering research. The purpose of this paper is to provide a brief survey of emerging methodologies for tackling various issues in quality control and improvement for multistage systems including modeling, analysis, monitoring, diagnosis, control, inspection and design optimization.

Suggested Citation

  • Jianjun Shi & Shiyu Zhou, 2009. "Quality control and improvement for multistage systems: A survey," IISE Transactions, Taylor & Francis Journals, vol. 41(9), pages 744-753.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:9:p:744-753
    DOI: 10.1080/07408170902966344
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    Citations

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    Cited by:

    1. Bera, Sasadhar & Mukherjee, Indrajit, 2016. "A multistage and multiple response optimization approach for serial manufacturing system," European Journal of Operational Research, Elsevier, vol. 248(2), pages 444-452.
    2. Chongyang Liu & Ryan Loxton & Kok Teo, 2014. "Optimal parameter selection for nonlinear multistage systems with time-delays," Computational Optimization and Applications, Springer, vol. 59(1), pages 285-306, October.
    3. Wei, Shuaichong & Nourelfath, Mustapha & Nahas, Nabil, 2023. "Analysis of a production line subject to degradation and preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Lu, Biao & Chen, Zhen & Zhao, Xufeng, 2021. "Data-driven dynamic predictive maintenance for a manufacturing system with quality deterioration and online sensors," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    5. Sangahn Kim & Mehmet Turkoz & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Monitoring of group-structured high-dimensional processes via sparse group LASSO," Annals of Operations Research, Springer, vol. 340(2), pages 891-911, September.
    6. Lin, Zu-Liang & Huang, Yeu-Shiang & Fang, Chih-Chiang, 2015. "Non-periodic preventive maintenance with reliability thresholds for complex repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 145-156.
    7. Liu Yumin & Zhou Haofei, 2014. "MSVM Recognition Model for Dynamic Process Abnormal Pattern Based on Multi-Kernel Functions," Journal of Systems Science and Information, De Gruyter, vol. 2(5), pages 473-480, October.
    8. Xiaoqin Wen & Chenhanzhi Wang, 2022. "Optimal-Quality Choice and Committed Delivery Time in Build-To-Order Supply Chain," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    9. Chen-Fu Chien & Chiao-Wen Liu & Shih-Chung Chuang, 2017. "Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5095-5107, September.
    10. Jinho Kim & Myong K. Jeong & Elsayed A. Elsayed, 2017. "Monitoring multistage processes with autocorrelated observations," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2385-2396, April.
    11. Kenneth J. Klassen & Reena Yoogalingam, 2019. "Appointment scheduling in multi-stage outpatient clinics," Health Care Management Science, Springer, vol. 22(2), pages 229-244, June.
    12. Angus Jeang & Yang-Kuei Lin, 2014. "Product and process parameters determination for quality and cost," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2042-2054, October.

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