Predicting the Impacts of the COVID-19 Pandemic on Food Supply Chains and Their Sustainability: A Simulation Study

The COVID-19 pandemic has continued to result in severe disruptions to food supply chains. In this research, we present a simulation study on the impact of the COVID-19 pandemic on food supply chains and their sustainability using the lobster industry in Nova Scotia, Canada, as an example. The main contributions of this paper are twofold. First, it analyzes how the pandemic has negatively disrupted lobster supply chains and their sustainability. Second, it demonstrates how a simulation-based methodology based on the software AnyLogistix can be applied to examine the effects of a pandemic on food supply chains. We show the impacts of the COVID-19 pandemic from four perspectives: production-inventory dynamics, customer performance, financial performance, and lead-time performance. Our findings include the following. First, the pandemic has created a backlog problem for the live lobster industry. Second, it has significantly increased the lead time of the lobster supply chain. Overall, this research can help the government and trade organizations to devise appropriate policies to reduce the negative impacts of the pandemic on food supply chains and their sustainability.

Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains

Entering the COVID-19 pandemic wreaked havoc on supply chains. Reacting to the pandemic and adaptation in the “new normal” have been challenging tasks. Exiting the pandemic can lead to some after-shock effects such as “disruption tails.” While the research community has undertaken considerable efforts to predict the pandemic’s impacts and examine supply chain adaptive behaviors during the pandemic, little is known about supply chain management in the course of pandemic elimination and post-disruption recovery. If capacity and inventory management are unaware of the after-shock risks, this can result in highly destabilized production–inventory dynamics and decreased performance in the post-disruption period causing product deficits in the markets and high inventory costs in the supply chains. In this paper, we use a discrete-event simulation model to investigate some exit strategies for a supply chain in the context of the COVID-19 pandemic. Our model can inform managers about the existence and risk of disruption tails in their supply chains and guide the selection of post-pandemic recovery strategies. Our results show that supply chains with postponed demand and shutdown capacity during the COVID-19 pandemic are particularly prone to disruption tails. We then developed and examined two strategies to avoid these disruption tails. First, we observed a conjunction of recovery and supply chain coordination which mitigates the impact of disruption tails by demand smoothing over time in the post-disruption period. Second, we found a gradual capacity ramp-up prior to expected peaks of postponed demand to be an effective strategy for disruption tail control.

Demand Shaping Through Bundling and Product Configuration: A Dynamic Multiproduct Inventory-Pricing Model

In today’s digital age with advanced information technology and analytic tools, such as the internet and data mining, many companies use dynamic bundling to influence demand to match up with the operational status, especially in industries with short product life cycles. In “Demand Shaping through Bundling and Product Configuration: A Dynamic Multiproduct Inventory-Pricing Model,” Jing-Sheng Song and Zhengliang Xue present a novel dynamic model to analyze how exactly the inventory dynamics impact the bundling strategy and, in turn, how the bundling strategy affects the firm's inventory decisions. They also characterize how the optimal bundling decision depends on item complementarity, cost structure, inventory levels, demand uncertainty, and supply responsiveness.

Reverse bullwhip effect: duality of a dynamic model of Supply Chain

This study aims to investigate control strategies for the bullwhip effect based on a dynamic model of the linear supply chain, proposed by Helbing and Lammer (2005), which describes the inventory dynamics and production rates of productive units. We simulated the model for instability and stability conditions defined by mathematical analysis. Through these results, we verified both classical and reverse bullwhip effects associated with instability and stability conditions, respectively. The model revealed a duality once the control strategy proposed by Helbing and Lamer (2005) for the classical bullwhip effect ends up causing a reverse effect, which is equally troubling. In the reverse bullwhip effect, we observed amplification of the production rates in the network chains from the supplier to the customer in a way that the upstream chain was not able to meet the needs of the downstream chain. To withhold both effects, we suggest the dynamic control of the parameters that describe the network based on Helbing and Lammer (2005) model.