Adulyasak Yossiri, Benomar Omar, Chaouachi Ahmed, Cohen Maxime C, Khern-Am-Nuai Warut
HEC Montreal, 3000, Chemin de La Cote-Sainte-Catherine, Montreal, QC H3T 2A7 Canada.
IVADO Labs, 6795 Rue Marconi #200, Montreal, QC H2S 3J9 Canada.
AI Soc. 2023 Apr 15:1-30. doi: 10.1007/s00146-023-01654-9.
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%.
新冠疫情在全球引发了抢购行为。结果，许多必需品在常见销售点持续缺货。尽管大多数零售商意识到了这个问题，但他们措手不及，仍缺乏解决这一问题的技术能力。本文的主要目标是开发一个框架，通过利用人工智能模型和技术来系统地缓解这一问题。我们利用内部和外部数据源，并表明使用外部数据可提高模型的可预测性和可解释性。我们的数据驱动框架可帮助零售商在需求异常出现时进行检测，使其能够做出战略反应。我们与一家大型零售商合作，将我们的模型应用于三类产品，使用了一个包含超过1500万条观测数据的数据集。我们首先表明，我们提出的异常检测模型能够成功检测与抢购相关的异常情况。然后，我们展示了一个规范性分析模拟工具，该工具可帮助零售商在不确定时期改善必需品的配送。利用2020年3月抢购潮的数据，我们表明我们的规范性工具可帮助零售商将必需品的可获取性提高56.74%。
