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

33 Pages Posted: 7 Dec 2020

See all articles by Yossiri Adulyasak

Yossiri Adulyasak

HEC Montréal

Omar Benomar

IVADO Labs

Ahmed Chaouachi

IVADO Labs

Maxime C. Cohen

Desautels Faculty of Management, McGill University

Warut Khern-am-nuai

McGill University - Desautels Faculty of Management

Date Written: December 3, 2020

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. Unfortunately, such a hoarding behavior disproportionately puts vulnerable groups of people at risk as they cannot "compete" with the demand surge, hence creating a critical societal issue. 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 research is to develop a data-driven framework that can systematically alleviate this issue by leveraging statistical models and machine learning techniques. We leverage both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our proposed 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

Suggested Citation

Adulyasak, Yossiri and Benomar, Omar and Chaouachi, Ahmed and Cohen, Maxime C. and Khern-am-nuai, Warut, Data Analytics to Detect Panic Buying and Improve Products Distribution Amid Pandemic (December 3, 2020). Available at SSRN: https://ssrn.com/abstract=3742121 or http://dx.doi.org/10.2139/ssrn.3742121

Yossiri Adulyasak

HEC Montréal ( email )

3000, Chemin de la Côte-Sainte-Catherine
Montreal, Quebec H2X 2L3
Canada

HOME PAGE: http://yossiri.info/

Omar Benomar

IVADO Labs

5100 4e avenue
Montreal, H1Y 2V3
Canada

Ahmed Chaouachi

IVADO Labs ( email )

6795 Rue Marconi #200
Montreal, QC H2S 3J9
Canada

HOME PAGE: http://www.ivadolabs.com

Maxime C. Cohen (Contact Author)

Desautels Faculty of Management, McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Warut Khern-am-nuai

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

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