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Value creation through design for scalability of reconfigurable manufacturing systems

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  • Yoram Koren
  • Wencai Wang
  • Xi Gu

Abstract

Rapid and cost-effective scalability of the throughput of manufacturing systems is an invaluable feature for the management of manufacturing enterprises. System design for scalability allows the enterprise to build a manufacturing system to supply the current demand, and upgrade its throughput in the future, in a cost-effective manner, to meet possible higher market demand in a timely manner. To possess this capability, the manufacturing system must be designed at the outset for future expansions in its throughput to enable growths in supply exactly when needed by the market. A mathematical method that maximises the system throughput after reconfiguration is proposed, and an industrial case is presented to validate the method. The paper offers a set of principles for system design for scalability to guide designers of modern manufacturing systems.

Suggested Citation

  • Yoram Koren & Wencai Wang & Xi Gu, 2017. "Value creation through design for scalability of reconfigurable manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1227-1242, March.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:5:p:1227-1242
    DOI: 10.1080/00207543.2016.1145821
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    Cited by:

    1. Weckenborg, Christian & Schumacher, Patrick & Thies, Christian & Spengler, Thomas S., 2024. "Flexibility in manufacturing system design: A review of recent approaches from Operations Research," European Journal of Operational Research, Elsevier, vol. 315(2), pages 413-441.
    2. Huan Shao & Aiping Li & Liyun Xu & Giovanni Moroni, 2019. "Scalability in manufacturing systems: a hybridized GA approach," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1859-1879, April.
    3. Bustinza, Oscar F. & Opazo-Basaez, Marco & Tarba, Shlomo, 2022. "Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance," Technovation, Elsevier, vol. 118(C).
    4. 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.
    5. 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).
    6. Marcello Colledani & Anteneh Yemane & Giovanni Lugaresi & Giovanni Borzi & Daniele Callegaro, 2018. "A software platform for supporting the design and reconfiguration of versatile assembly systems," Post-Print hal-03880599, HAL.
    7. Hosseini, Amir & Otto, Alena & Pesch, Erwin, 2024. "Scheduling in manufacturing with transportation: Classification and solution techniques," European Journal of Operational Research, Elsevier, vol. 315(3), pages 821-843.
    8. Sihan Huang & Yan Yan, 2019. "Part family grouping method for reconfigurable manufacturing system considering process time and capacity demand," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 424-445, June.
    9. Ortega-Jimenez, Cesar H. & Garrido-Vega, Pedro & Cruz Torres, Cristian Andrés, 2020. "Achieving plant responsiveness from reconfigurable technology: Intervening role of SCM," International Journal of Production Economics, Elsevier, vol. 219(C), pages 195-203.
    10. Youssef Lahrichi & Laurent Deroussi & Nathalie Grangeon & Sylvie Norre, 2021. "A balance-first sequence-last algorithm to design RMS: a matheuristic with performance guaranty to balance reconfigurable manufacturing systems," Journal of Heuristics, Springer, vol. 27(1), pages 107-132, April.
    11. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    12. Bashir Salah & Mustufa Haider Abidi & Syed Hammad Mian & Mohammed Krid & Hisham Alkhalefah & Ali Abdo, 2019. "Virtual Reality-Based Engineering Education to Enhance Manufacturing Sustainability in Industry 4.0," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    13. Battaïa, Olga & Dolgui, Alexandre & Guschinsky, Nikolai, 2023. "MIP-based heuristics for combinatorial design of reconfigurable rotary transfer machines for production of multiple parts," International Journal of Production Economics, Elsevier, vol. 262(C).

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