Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions

This study develops machine learning methods for the data-driven demand estimation and assortment planning problem by addressing three subproblems, that is, demand forecasting simultaneously considering cross-selling and substitutions, estimation of the cross-selling and substitution effects, and assortment optimization. These three subproblems are transformed into three sequentially related machine learning problems: collective demand forecasting, demand inference for cross-selling and substitutions, and assortment rule mining. For collective demand forecasting, related product features are introduced to consider both the cross-selling and substitution effects, and a collaborative coordinate descent method with a good convergence property is developed to make distributed demand forecasting and a global update of related product features. Using the results, demand inference adopts transfer and semisupervised learning methods to tackle the challenge of missing data in quantifying the cross-selling and substitution effects. For assortment rule mining, the assortment rules bridge the gap between prediction and optimization, and the developed heuristics obtain the best assortment using the prior knowledge discovered in demand inference. The computational results on a real-world database and a semisynthetic database show that collective demand forecasting obtained far better results than the standard demand forecasting methods and some popular graph learning methods, and the developed heuristics identified much better assortments than those obtained with the baseline methods.