Data Analytics to Detect Panic Buying and Improve Products Distribution Amid Pandemic

Abstract

The COVID-19 pandemic has triggered a 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 data-driven framework that can systematically alleviate this issue by leveraging data-driven models and machine learning 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 products distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help the retailer increase access to essential products by 56.74%.

Keywords: Anomaly Detection, Retail Operations, Machine Learning, COVID-19