Utilizing Bayesian Regression Analysis and Optimization Approaches to Identify Main Drivers in Inventory Management

Inventory management is a crucial research area focusing on stock optimization, cost reduction, and supply chain efficiency, with particular attention given to optimizing practices and identifying key factors. If inventory management fails to maintain a sustainable level, it directly hampers the company. To address this problem, we propose a new methodology that combines Bayesian regression analysis with optimization techniques. The historical sales data used in this study was obtained from an online database platform called “Kaggle”. A Bayesian logistic regression approach was utilized to incorporate prior knowledge from this historical data into the Bayesian design framework. The Bayesian optimal experimental design was then obtained by maximizing the expected utility function, with the Kullback–Leibler divergence selected as the utility criterion to support precise estimation of model parameters. The resulting optimal experiment set represents the selection of an optimal covariate set aimed at improving the prediction of the dependent variable. To determine the optimal design, the Approximate Coordinate Exchange (ACE) optimization algorithm was employed, starting with three randomly generated initial designs. This study demonstrates the efficacy of Bayesian regression analysis and optimization techniques in identifying and prioritizing key drivers impacting inventory management. By incorporating prior knowledge and accounting for uncertainty, businesses can achieve more accurate demand forecasting, improved inventory turnover, and reduced holding costs, ultimately driving superior supply chain efficiency and organizational performance.