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%. (shrink)

This study uses an artificial intelligence (AI) model to identify arbitrage opportunities in the retail marketplace. Specifically, we develop an AI model to predict the optimal purchasing point based on the price movement of products in the market. Our model is trained on a large dataset collected from an online marketplace in the United States. Our model is enhanced by incorporating user-generated content (UGC), which is empirically proven to be significantly informative. Overall, the AI model attains more than 90% precision (...) rate, while the recall rate is higher than 80% in an out-of-sample test. In addition, we conduct a field experiment to verify the external validity of the AI model in a real-life setting. Our model identifies 293 arbitrage opportunities during a one-year field experiment and generates a profit of $7.06 per arbitrage opportunity. The result demonstrates that AI performs exceptionally well in identifying arbitrage opportunities in retail markets with tangible economic values. Our results also yield important implications regarding the role of AI in the society, both from the consumer and firm perspectives. (shrink)