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

Heiner Ackermann (), Erik Diessel, Michael Helmling, Neil Jami and Johanna Münch
Additional contact information
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

SN Operations Research Forum, 2024, vol. 5, issue 2, 1-35

Abstract: 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.

Keywords: Supply chain disruptions; Force-majeure scenarios; Mitigation; Risk analysis; Business continuity planning; 90B06 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-024-00333-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00333-9

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-024-00333-9

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().