A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19

Purpose A recent global pandemic, known as coronavirus disease 2019 (COVID-19), affects the manufacturing supply chains most significantly. This effect becomes more challenging for the manufacturers of high-demand and most essential items, such as toilet paper and hand sanitizer. In a pandemic situation, the demand of the essential products increases expressively; on the other hand, the supply of the raw materials decreases considerably with a constraint of production capacity. These dual disruptions impact the production process suddenly, and the process can collapse without immediate and necessary actions. To minimize the impacts of these dual disruptions, we aim to develop a recovery model for making a decision on the revised production plan. Design/methodology/approach In this paper, the authors use a mathematical modeling approach to develop a production recovery model for a high-demand and essential item during the COVID-19. The authors also analyze the properties of the recovery plan, and optimize the recovery plan to maximize the profit in the recovery window. Findings The authors analyze the results using a numerical example. The result shows that the developed recovery model is capable of revising the production plan in the situations of both demand and supply disruptions, and improves the profit for the manufacturers. The authors also discuss the managerial implications, including the roles of digital technologies in the recovery process. Originality/value This model, which is a novel contribution to the literature, will help decision-makers of high-demand and essential items to make an accurate and prompt decision in designing the revised production plan to recover during a pandemic, like COVID-19.

Automated summary

This paper presents a mathematical modeling approach to develop a production recovery model for high-demand and essential items during COVID-19. The authors analyze the properties of the recovery plan and optimize it to maximize profit in the recovery window. The results show that the model is effective in revising production plans during disruptions and improves profitability for manufacturers.