The Big Data Newsvendor: Practical Insights from Machine Learning

Abstract

In Ban and Rudin’s (2018) “The Big Data Newsvendor: Practical Insights from Machine Learning,” the authors take an innovative machine-learning approach to a classic problem solved by almost every company, every day, for inventory management. By allowing companies to use large amounts of data to predict the correct answers to decisions directly, they avoid intermediate questions, such as “how many customers will we get tomorrow?” and instead can tell the company how much inventory to stock for these customers. This has implications for almost all other decision-making problems considered in operations research, which has traditionally considered data estimation separately from the decision optimization. Their proposed methods are shown to work both analytically and empirically with the latter explored in a hospital nurse staffing example in which the best one-step, feature-based newsvendor algorithm (the kernel-weights optimization method) is shown to beat the best-practice benchmark by 24% in the out-of-sample cost at a fraction of the speed.