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Choosing the system configuration for high-volume manufacturing

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  • Yoram Koren
  • Xi Gu
  • Weihong Guo

Abstract

When designing a new large manufacturing system with high throughput, the corporation should weigh several factors: the capital investment cost, the system’s responsiveness to future varying market demand, the production losses due to disruptive events, and the product quality. These performance metrics depend heavily on the system configuration. In this paper, typical configurations of large-volume manufacturing systems for mechanical products are compared from cost, responsiveness and product quality perspectives. In addition to traditional serial lines and pure parallel systems, we also discuss two practical configurations – parallel serial lines, and reconfigurable manufacturing systems. The results offer managerial insights for selecting the system configuration that creates the maximum economic value over the lifetime of the system, and fits the corporation needs and culture.

Suggested Citation

  • Yoram Koren & Xi Gu & Weihong Guo, 2018. "Choosing the system configuration for high-volume manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 476-490, January.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:1-2:p:476-490
    DOI: 10.1080/00207543.2017.1387678
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    Cited by:

    1. Dolgui, Alexandre & Hashemi-Petroodi, S. Ehsan & Kovalev, Sergey & Kovalyov, Mikhail Y., 2021. "Profitability of a multi-model manufacturing line versus multiple dedicated lines," International Journal of Production Economics, Elsevier, vol. 236(C).
    2. Carlos Alberto Barrera-Diaz & Amir Nourmohammadi & Henrik Smedberg & Tehseen Aslam & Amos H. C. Ng, 2023. "An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
    3. Delorme, Xavier & Cerqueus, Audrey & Gianessi, Paolo & Lamy, Damien, 2023. "RMS balancing and planning under uncertain demand and energy cost considerations," International Journal of Production Economics, Elsevier, vol. 261(C).
    4. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.
    5. Yuming Guo, 2023. "Towards the efficient generation of variant design in product development networks: network nodes importance based product configuration evaluation approach," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 615-631, February.

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