Forecasting and Planning for a critical infrastructure sector during a pandemic: empirical evidence from a food supply chain

Aljuneidi, Tariq; Punia, Sushil; Jebali, Aida; Nikolopoulos, Konstantinos

Abstract

The meat supply chain (MSC) – a key constituent of the ‘Food & Agriculture’ CISA critical infrastructure sector, was among the most impacted by the COVID-19 pandemic. The witnessed successive demand and supply shocks uncovered the fragility of the MSC and revealed that more attention should be given by researchers and practitioners to ensure effective planning of such a critical infrastructure sector during periods of turbulence. To that end, in this paper we propose a two-stage approach for the planning of an MSC. In the first stage, we identify the most suitable model for predicting the demand and the supply. In the second stage, a multi-period multi-product mixed integer programming (MIP) model accounting for key MSC features is devised to deal with the planning of the MSC. Furthermore, in order to validate our theoretical proposition, a case study pertaining to a real-life MSC was used during the second and first wave of COVID-19 under different conditions. In particular, the results show that accurate demand and supply forecasting, and the recourse to rolling horizon planning approach, allow for satisfying the demand and maintaining the MSC profit in periods of turbulence, and so can be considered as levers for supply chain resilience.

Citation

Aljuneidi, T., Punia, S., Jebali, A., & Nikolopoulos, K. (2024). Forecasting and Planning for a critical infrastructure sector during a pandemic: empirical evidence from a food supply chain. European Journal of Operational Research, 317(3), 936-952. https://doi.org/10.1016/j.ejor.2024.04.009

• Journal Article Type: Article
• Acceptance Date: Apr 9, 2024
• Online Publication Date: Apr 10, 2024
• Publication Date: Sep 16, 2024
• Deposit Date: Apr 11, 2024
• Publicly Available Date: Apr 11, 2026
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Electronic ISSN: 1872-6860
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 317
• Issue: 3
• Pages: 936-952
• DOI: https://doi.org/10.1016/j.ejor.2024.04.009
• Keywords: Information Systems and Management; Management Science and Operations Research; Modeling and Simulation; General Computer Science; Industrial and Manufacturing Engineering
• Public URL: https://durham-repository.worktribe.com/output/2382024