The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective

Karzan Mahdi Ghafour () and Abdulqadir Rahomee Ahmed Aljanabi ()
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Karzan Mahdi Ghafour: University of Sulaimani
Abdulqadir Rahomee Ahmed Aljanabi: Sulaimani Polytechnic University

Operations Management Research, 2023, vol. 16, issue 2, No 12, 780-793

Abstract: Abstract Strengthen the resilience of supply chains was observed to be critical issue by firms to confront disruptions triggered by unprecedented demand and severe disasters. However, the extraordinarily challenging disruptions of COVID-19 pandemic, unlike any disasters seen in recent times. This study aims to provide a practical solution to supply chain (SC) disruptions by estimating the best forecasting models for demand fluctuations in the context of food and beverages. A method is proposed to predict SC disruptions and enhance SC resilience. Double exponential smoothing (DES) and the ARIMA model are adopted as forecasting approaches to estimate demand and optimum inventory quantities during three different periods of disruption associated with the COVID-19 outbreak. A downstream SC involving 2 distributors and 56 retailers is considered to elaborate inventory measurements (optimal inventory levels and total costs). The results demonstrate that distributors can reduce costs by dispensing with some retailers, particularly those who order low quantities and thus incur unjustified expenses. Furthermore, high accuracy is obtained, with minimal differences between the real data and the model’s forecast. Existing research has largely ignored supply disruptions in the distributor-retailer relationship. This study provides distributors and SC managers important knowledge on SC disruptions and identifies appropriate forecasting methods to increase SC resilience. It also provides distributors and other SC managers unprecedented insights on tackling crises of stability like COVID-19.

Keywords: Forecasting; ARIMA model; COVID-19 pandemic; Supply chain resilience; Time series (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12063-022-00327-y

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