A Machine Learning Approach for Modeling Safety Stock Optimization Equation in the Cosmetics and Beauty Industry

Abstract

Safety Stock is generally accepted as an appropriate inventory management strategy to deal with the uncertainty of demand and supply, as well as for limiting the risk of service loss and overproduction [6]. In particular, companies from the cosmetics and beauty industry face additional inventory management challenges derived from the strict regulatory standards applicable in different jurisdictions, in addition to the constantly changing trends, which highlight the importance of defining an accurate safety stock. In this paper, on the basis of the Linear Regression, Decision Trees, Support Vector Machine (“SVM”) and Neural Network machine learning techniques, we modeled a general Safety Stock equation and one per product category for a multinational enterprise operating in the cosmetics and beauty industry. The results of our analysis indicate that the Linear Regression is the most accurate model to generate a reasonable and effective prediction of the company’s Safety Stock.