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A quantitative approach to resilience in manufacturing systems

Author

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  • Kosmas Alexopoulos
  • Ioannis Anagiannis
  • Nikolaos Nikolakis
  • George Chryssolouris

Abstract

Resilience is one of the key characteristics that manufacturing systems should have as it offers the ability to withstand difficult situations and be able to accommodate disruptions without the incurrence of significant additional costs. The main contribution of this study is the presentation of a method for quantifying resilience in manufacturing systems based on calculating the penalty of possible changes. The method is applied to an industrially-relevant scenario to estimate the resilience of two production systems when COVID-19 disrupts their production. The first system uses additive manufacturing (3D printing), and the second uses injection moulding. Several scenarios, related to the systems’ operational environment, are presented on the basis of pandemic-related possible events. The validation of the proposed resilience measure demonstrates the method’s suitability and reliability to be considered in industrial practice, in support of decision-making. The resilience measure can be used by managers to assess, compare and improve their production systems, and decide on strategic investment costs to improve systems’ resilience. It can be applied for several disruption scenarios or variations of the same disruption scenario with different disruption characteristics, such as duration, recovery time and impact on the production system.

Suggested Citation

  • Kosmas Alexopoulos & Ioannis Anagiannis & Nikolaos Nikolakis & George Chryssolouris, 2022. "A quantitative approach to resilience in manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7178-7193, December.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:24:p:7178-7193
    DOI: 10.1080/00207543.2021.2018519
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    File URL: http://hdl.handle.net/10.1080/00207543.2021.2018519
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    Citations

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    Cited by:

    1. Rohaninejad, Mohammad & Hanzálek, Zdeněk, 2023. "Multi-level lot-sizing and job shop scheduling with lot-streaming: Reformulation and solution approaches," International Journal of Production Economics, Elsevier, vol. 263(C).
    2. Baishakhi Ganguly & Bikash Koli Dey & Sarla Pareek & Biswajit Sarkar, 2023. "Cost-Effective Imperfect Production-Inventory System under Variable Production Rate and Remanufacturing," Mathematics, MDPI, vol. 11(15), pages 1-24, August.
    3. Basim S. O. Alsaedi, 2024. "A Sustainable Supply Chain Model with Variable Production Rate and Remanufacturing for Imperfect Production Inventory System under Learning in Fuzzy Environment," Mathematics, MDPI, vol. 12(18), pages 1-49, September.
    4. Caputo, A.C. & Donati, L. & Salini, P., 2023. "Estimating resilience of manufacturing plants to physical disruptions: Model and application," International Journal of Production Economics, Elsevier, vol. 266(C).
    5. Yu, Yubing & Xu, Jiawei & Zhang, Justin Z. & Liu, Yulong (David) & Kamal, Muhammad Mustafa & Cao, Yanhong, 2024. "Unleashing the power of AI in manufacturing: Enhancing resilience and performance through cognitive insights, process automation, and cognitive engagement," International Journal of Production Economics, Elsevier, vol. 270(C).

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