The Big Data Newsvendor: Practical Insights from Machine Learning

By Gah-Yi Ban, Cynthia Rudin. Operational Research, 2013

527 citations recorded by the source corpus.

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.

Subfields: Management Information Systems, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/e8d92bfb6d71e7863a03a011252f553902c009c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Information Systems

1. Susan Athey
2. Stefan Wager
3. Hamed Rahimian
4. Sanjay Mehrotra

In Management Science and Operations Research

1. Stefan Wager
2. Susan Athey
3. Hamed Rahimian
4. Sanjay Mehrotra
5. David A. Hirshberg
6. Guido W. Imbens
7. David McAdams
8. Qingyuan Zhao

In Strategy and Management

1. Susan Athey
2. Stefan Wager
3. Hamed Rahimian
4. Sanjay Mehrotra
5. Yuliy Sannikov
6. Sylvain Chassang

Citations in the corpus

• The Operational Value of Social Media Information (2018, unknown): “…have used machine learning tools to analyze such problems as demand estimation and price optimization in online retail Ye et al. (2014), predicting emergency department waiting times (Ang et al. 2015), and developing a feature-based algorithm to solve nursing staff problems (Rudin and Vahn 2015).”
• Distributionally Robust Optimization: A Review (2019, methodology): “Tulabandhula and Rudin [303] incorporate machine learning for the decision making.”
• Policy Learning With Observational Data (2017, result): “From this perspective, our result is related to the work of Ban and Rudin (2019) and Bertsimas and Kallus (2020), who study data-driven rules for optimal inventory management and related problems.”