From predictive to prescriptive analytics: A data-driven multi-item newsvendor model

摘要

This paper considers a multi-item newsvendor problem with a capacity constraint (Z). The problem has already been addressed in the literature using the classical newsvendor problem. However, provided solutions made assumptions for demand distributions, which are often incorrect and led to errors in the inventory optimization. This research proposes a distribution-free and completely data-driven solution approach to Z. The proposed approach uses sample demand data as input, and machine (and deep) learning methods with empirical risk minimization principle to find order quantities. A heuristic is developed using hierarchies of the retail products to perform multi-item inventory optimization when a capacity constraint is active. The proposed approach is tested on a real-world dataset of retail products. The results from the proposed method are compared with data-driven max-min and empirical inventory optimization methods, and it outperformed all of them. The machine (and deep) learning-based demand forecasting methods (part of the proposed approach) providing better results than neural networks, multiple regression, arima, arimax, etc. Finally, a comparison of total inventory cost from the proposed, max-min, and empirical inventory optimization methods are carried out, and it is observed that the proposed data-driven approach leads to a significant reduction in inventory cost.