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Modeling, analysis, and improvement of integrated productivity and quality system in battery manufacturing

Author

Listed:
  • Feng Ju
  • Jingshan Li
  • Guoxian Xiao
  • Jorge Arinez
  • Weiwen Deng

Abstract

A battery manufacturing system typically includes a serial production line with multiple inspection stations and repair processes. In such systems, productivity and quality are tightly coupled. Variations in battery quality may add up along the line so that the upstream quality may impact the downstream operations. The repair process after each inspection can also affect downstream quality behavior and may further impose an effect on the throughput of conforming batteries. In this article, an analytical model of such an integrated productivity and quality system is introduced. Analytical methods based on an overlapping decomposition approach are developed to estimate the production rate of conforming batteries. The convergence of the method is analytically proved and the accuracy of the estimation is numerically justified. In addition, bottleneck identification methods based on the probabilities of blockage, starvation, and quality statistics are investigated. Indicators are proposed to identify the downtime and quality bottlenecks that remove the need to calculate throughput and quality performance and their sensitivities. These methods provide a quantitative tool for modeling, analysis, and improvement of productivity and quality in battery manufacturing systems and can be applied to other manufacturing systems ameanable to investigation using integrated productivity and quality models.

Suggested Citation

  • Feng Ju & Jingshan Li & Guoxian Xiao & Jorge Arinez & Weiwen Deng, 2015. "Modeling, analysis, and improvement of integrated productivity and quality system in battery manufacturing," IISE Transactions, Taylor & Francis Journals, vol. 47(12), pages 1313-1328, December.
    • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:12:p:1313-1328
    DOI: 10.1080/0740817X.2015.1005777
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    Cited by:

    1. Jun-Qiang Wang & Yun-Lei Song & Peng-Hao Cui & Yang Li, 2023. "A data-driven method for performance analysis and improvement in production systems with quality inspection," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 455-469, February.
    2. Chang-Ho Lee & Dong-Hee Lee & Young-Mok Bae & Seung-Hyun Choi & Ki-Hun Kim & Kwang-Jae Kim, 2022. "Approach to derive golden paths based on machine sequence patterns in multistage manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 167-183, January.

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