IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v5y2024i2d10.1007_s43069-024-00333-9.html
   My bibliography  Save this article

Computing Optimal Mitigation Plans for Force-Majeure Scenarios in Dynamic Manufacturing Chains

Author

Listed:
  • Heiner Ackermann

    (Fraunhofer Institute for Industrial Mathematics ITWM)

  • Erik Diessel

    (Fraunhofer Institute for Industrial Mathematics ITWM)

  • Michael Helmling

    (Fraunhofer Institute for Industrial Mathematics ITWM)

  • Neil Jami

    (Fraunhofer Institute for Industrial Mathematics ITWM)

  • Johanna Münch

    (Fraunhofer Institute for Industrial Mathematics ITWM)

Abstract

We consider force-majeure supply disruptions in a dynamic, multi-product manufacturing supply chain with time-dependent parameters. We present a linear programming model that captures a specific force-majeure scenario with respect to several objective functions that can be combined in a multi-objective framework, e.g., minimization of loss, maximization of shortage-free time, or prioritization of mitigation types. Solving this model yields an optimal mitigation plan that describes how to best (re-)allocate supply and production operations. Supported mitigation options include plant-side safety stock, supplier-side inventories, and additional production thus reflecting the manufacturing setting of a large-scale industrial player. We describe a workflow for increasing the resilience of supply chains based on risk profiles generated by our approach.

Suggested Citation

  • Heiner Ackermann & Erik Diessel & Michael Helmling & Neil Jami & Johanna Münch, 2024. "Computing Optimal Mitigation Plans for Force-Majeure Scenarios in Dynamic Manufacturing Chains," SN Operations Research Forum, Springer, vol. 5(2), pages 1-35, June.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00333-9
    DOI: 10.1007/s43069-024-00333-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-024-00333-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-024-00333-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Abhijeet Ghadge & Hendrik Wurtmann & Stefan Seuring, 2020. "Managing climate change risks in global supply chains: a review and research agenda," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 44-64, January.
    2. Tempelmeier, Horst, 2007. "On the stochastic uncapacitated dynamic single-item lotsizing problem with service level constraints," European Journal of Operational Research, Elsevier, vol. 181(1), pages 184-194, August.
    3. Ivanov, Dmitry & Pavlov, Alexander & Dolgui, Alexandre & Pavlov, Dmitry & Sokolov, Boris, 2016. "Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 7-24.
    4. Mari, Sonia Irshad & Lee, Young Hae & Memon, Muhammad Saad & Cho, Su Yeon, 2014. "A Three-level Sustainable and Resilient Supply Chain Network Design under Disruption," MPRA Paper 58228, University Library of Munich, Germany.
    5. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
    6. Balster, Andreas & Friedrich, Hanno, 2019. "Dynamic freight flow modelling for risk evaluation in food supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 4-22.
    7. Suryawanshi, Pravin & Dutta, Pankaj, 2022. "Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    8. Dmitry Ivanov & Alexandre Dolgui & Jennifer V. Blackhurst & Tsan-Ming Choi, 2023. "Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems," International Journal of Production Research, Taylor & Francis Journals, vol. 61(8), pages 2402-2415, April.
    9. Sahitya Elluru & Hardik Gupta & Harpreet Kaur & Surya Prakash Singh, 2019. "Proactive and reactive models for disaster resilient supply chain," Annals of Operations Research, Springer, vol. 283(1), pages 199-224, December.
    10. Heiner Ackermann & Erik Diessel & Michael Helmling & Christoph Hertrich & Neil Jami & Johanna Schneider, 2020. "Decision Support for Material Procurement," Operations Research Proceedings, in: Janis S. Neufeld & Udo Buscher & Rainer Lasch & Dominik Möst & Jörn Schönberger (ed.), Operations Research Proceedings 2019, pages 693-699, Springer.
    11. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    12. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    13. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    14. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    15. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
    16. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.
    17. Parajuli, Anubhuti & Kuzgunkaya, Onur & Vidyarthi, Navneet, 2021. "The impact of congestion on protection decisions in supply networks under disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    18. Torabi, S.A. & Baghersad, M. & Mansouri, S.A., 2015. "Resilient supplier selection and order allocation under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 22-48.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    2. Chen, Li-Ming & Chang, Wei-Lun, 2021. "Supply- and cyber-related disruptions in cloud supply chain firms: Determining the best recovery speeds," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    3. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).
    4. João Pires Ribeiro & Ana Paula F. D. Barbosa-Póvoa, 2023. "A responsiveness metric for the design and planning of resilient supply chains," Annals of Operations Research, Springer, vol. 324(1), pages 1129-1181, May.
    5. Cecil Ash & Uday Venkatadri & Claver Diallo & Peter Vanberkel & Ahmed Saif, 2023. "PPE Supply Optimization Under Risks of Disruption from the COVID-19 Pandemic," SN Operations Research Forum, Springer, vol. 4(2), pages 1-29, June.
    6. Cheramin, Meysam & Saha, Apurba Kumar & Cheng, Jianqiang & Paul, Sanjoy Kumar & Jin, Hongyue, 2021. "Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    7. Fattahi, Mohammad & Govindan, Kannan & Maihami, Reza, 2020. "Stochastic optimization of disruption-driven supply chain network design with a new resilience metric," International Journal of Production Economics, Elsevier, vol. 230(C).
    8. Célestin Elock Son, 2018. "Supply Chain Risk Management: A Review of Thirteen Years of Research," Post-Print hal-03029453, HAL.
    9. Evangelos Gkanatsas & Harold Krikke, 2020. "Towards a Pro-Silience Framework: A Literature Review on Quantitative Modelling of Resilient 3PL Supply Chain Network Designs," Sustainability, MDPI, vol. 12(10), pages 1-25, May.
    10. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    11. Ghanei, Shima & Contreras, Ivan & Cordeau, Jean-François, 2023. "A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    12. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    13. El-Awady Attia & Ali Alarjani & Md. Sharif Uddin & Ahmed Farouk Kineber, 2023. "Determining the Stationary Enablers of Resilient and Sustainable Supply Chains," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    14. Muhammad Umar Farooq & Amjad Hussain & Tariq Masood & Muhammad Salman Habib, 2021. "Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    15. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    16. Shashi & Piera Centobelli & Roberto Cerchione & Myriam Ertz, 2020. "Managing supply chain resilience to pursue business and environmental strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1215-1246, March.
    17. Lida Safari & Seyed Jafar Sadjadi & Farzad Movahedi Sobhani, 2024. "Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 27485-27527, November.
    18. Belhadi, Amine & Kamble, Sachin & Jabbour, Charbel Jose Chiappetta & Gunasekaran, Angappa & Ndubisi, Nelson Oly & Venkatesh, Mani, 2021. "Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    19. Sinha, Priyank & Kumar, Sameer & Prakash, Surya, 2020. "Measuring and mitigating the effects of cost disturbance propagation in multi-echelon apparel supply chains," European Journal of Operational Research, Elsevier, vol. 282(1), pages 148-160.
    20. Giménez-Palacios, Iván & Parreño, Francisco & Álvarez-Valdés, Ramón & Paquay, Célia & Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando, 2022. "First-mile logistics parcel pickup: Vehicle routing with packing constraints under disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00333-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.