| Marcel ILIE1, Augustin SEMENESCU2
Abstract. Effective inventory management under uncertainty remains a central challenge in supply chain systems, particularly when demand is stochastic, non-stationary, or partially observed. Classical inventory control methods rely on fixed probabilistic assumptions that often fail to capture evolving demand dynamics. This paper develops a Bayesian Inventory Control framework that integrates probabilistic demand learning with sequential decision-making. The proposed approach updates demand distributions in real time using Bayesian inference, enabling adaptive ordering policies that respond to new information. To evaluate performance, the Bayesian model is compared with traditional forecasting-driven inventory policies, including ARIMA-based statistical forecasting and LSTM-based deep learning demand prediction. Simulation results demonstrate that the Bayesian approach consistently reduces total inventory cost, improves service levels, and shows superior robustness under high demand uncertainty and low data availability scenarios. The findings highlight the advantages of combining probabilistic inference with inventory optimization in modern supply chain systems. Keywords: Bayesian inventory control; stochastic inventory management; (s,S) policy; demand forecasting; uncertainty quantification; machine learning forecasting models DOI 10.56082/annalsarscieng.2026.1.36 Read full article 1Associate. Prof. Ph.D. Georgia Southern University, 1332 Southern Dr. Statesboro GA 30458, USA, *Corresponding author:, milie@georgiasouthern.edu 2Prof. National Science and Technology University Politehnica Bucharest, Spl.Independentei 313, Bucharest, Romania, augustin.semenescu@upb.ro
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PUBLISHED in Annals Academy of Romanian Scientists Series on Engineering Sciences
ISSN PRINT 2066 – 6950 ISSN ONLINE 2066 – 8570 |
