From Predicting Demand to Prescribing Action: Operationalizing Prescriptive Analytics in the Enterprise
Combining Machine Learning (ML) and Operations Research and Management Science (OR/MS) allows for using big data to prescribe optimal decisions in daily operations. We explore the suitability of various Statistical Machine Learning techniques to predict outcomes, and a way to compute how effective variables are at informing optimal decision making. To demonstrate the power of this approach in a real-world setting we study an inventory management problem faced by the distribution arm of an international media conglomerate, which ships an average of 1 billion units per year to retail stores. We leverage both internal data and public data scraped from IMDb, Rotten Tomatoes, and Google to prescribe operational decisions that outperform baseline measures.
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