Using AI to detect panic buying and improve products distribution amid pandemic

Abstract

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%.

Review on Panic Buying Behavior during Pandemics: Influencing Factors, Stockpiling, and Intervention Strategies

“…The quantitative models were seldom verified except in Govindan et al [ 70 ] and Shoukat et al [ 71 ], who used statistical analysis to evaluate the demand. It has been demonstrated that data-driven frameworks and AI can detect demand anomalies raised by panic [ 74 , 75 ]; however, these models do not predict future demand by influencing factors or propose intervention plans.…”