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. 2021 Aug:152:102412.
doi: 10.1016/j.tre.2021.102412. Epub 2021 Jun 30.

Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions

Diana Burgos  1 , Dmitry Ivanov  1
Affiliations

Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions

Diana Burgos et al. Transp Res E Logist Transp Rev. 2021 Aug.

Abstract

In this study, we examine the impact of the COVID-19 pandemic on food retail supply chains (SCs) and their resilience. Based on real-life pandemic scenarios encountered in Germany, we develop and use a discrete-event simulation model to examine SC operations and performance dynamics with the help of anyLogistix digital SC twin. The computational results show that food retail SC resilience at the upheaval times is triangulated by the pandemic intensity and associated lockdown/shutdown governmental measures, inventory-ordering dynamics in the SC, and customer behaviours. We observe that surges in demand and supplier shutdowns have had the highest impact on SC operations and performance, whereas the impact of transportation disruptions was rather low. Transportation costs have spiked because of chaotic inventory-ordering dynamics leading to more frequent and irregular shipments. On bright side, we observe the demand growth and utilization of online sales channels yielding higher revenues. We propose several directions and practical implementation guidelines to improve the food retail SC resilience. We stress the importance of SC digital twins and end-to-end visibility along with resilient demand, inventory, and capacity management. The outcomes of our study can be instructive for enhancing the resilience of food retail SCs in preparation for future pandemics and pandemic-like crises.

Keywords: COVID-19 pandemic; Digital twin; Food supply chain; Simulation; Supply chain resilience.

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Figures

Fig. 1
Fig. 1
Supply chain design (interface from anyLogistix Studio Edition).
Fig. 2
Fig. 2
Digital supply chain design for disruption analysis using anyLogistix (based on Ivanov and Dolgui, 2021b).
Fig. 3
Fig. 3
Simulation model design.
Fig. 4
Fig. 4
Timeline of simulation experiments following the real-life COVID-19 control dynamics Based on the timeline shown in Fig. 4, we developed several simulation scenarios.
Fig. 5
Fig. 5
Experiment description: Bottlenecks in transport.
Fig. 6
Fig. 6
Experiment results: Disruption-free (baseline) scenario.
Fig. 7
Fig. 7
Experiment results: Increase in demand.
Fig. 8
Fig. 8
Experiment results: Suppliers’ factories shutdown.
Fig. 9
Fig. 9
Experiment results: Bottlenecks in transport.
Fig. 10
Fig. 10
Experiment results: COVID-19 scenario.
Fig. 11
Fig. 11
Time-to-recover for service level SLfailure=0.9and SLrecovered=0.98.
Fig. 12
Fig. 12
Time-to-recover for service level SLfailure=0.7and SLrecovered=0.9.
Fig. 13
Fig. 13
Post-disruption framework to increase SC resilience in the food retail industry.
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