| Marcel ILIE1, Augustin SEMENESCU2
Abstract. Demand forecasting plays a critical role in supply chain management, directly influencing inventory planning, procurement strategies, and service level performance. Traditional forecasting methods such as ARIMA and exponential smoothing remain widely used due to their simplicity and interpretability; however, they are fundamentally limited by their reliance on point estimates and their inability to explicitly quantify predictive uncertainty. In contrast, modern machine learning methods, including recurrent neural networks and LSTM-based architectures, improve predictive accuracy by capturing nonlinear temporal dependencies, but still largely operate in a deterministic framework without rigorous uncertainty representation. This paper proposes a unified Bayesian framework for demand forecasting and inventory control that addresses both predictive accuracy and uncertainty quantification. A hierarchical Bayesian model is developed to capture temporal dynamics and structural variability in demand, while posterior predictive distributions are used to generate probabilistic forecasts. These forecasts are then directly integrated into a stochastic (s, S) inventory policy, enabling dynamic and risk-aware decision-making. The proposed framework allows inventory thresholds to be informed by predictive uncertainty rather than fixed assumptions, improving robustness under volatile and non-stationary demand conditions. The performance of the proposed Bayesian approach is evaluated against classical ARIMA-based methods and LSTM-inspired forecasting models using a simulated non-stationary demand environment with seasonal effects and structural shocks. The results demonstrate that the Bayesian model achieves superior forecasting accuracy, particularly during periods of high volatility, while also providing meaningful uncertainty estimates through credible intervals. Furthermore, the integration of probabilistic forecasts into inventory control leads to more stable inventory trajectories and improved responsiveness to demand fluctuations. Overall, the findings highlight the advantages of combining Bayesian hierarchical forecasting with operational decision-making frameworks. The proposed approach bridges the gap between predictive analytics and prescriptive optimization in supply chains, offering a scalable and interpretable solution for managing demand uncertainty in complex and dynamic environments. Keywords: Bayesian forecasting; supply chain management; demand forecasting; hierarchical models; inventory control; (s, S) policy; uncertainty quantification; probabilistic forecasting; deep learning; ARIMA comparison DOI 10.56082/annalsarscieng.2026.1.86 Read full article 1Marcel ILIE [1], Associate. Prof. Ph.D. Georgia Southern University, 1332 Southern Dr. Statesboro GA 30458, USA, *Corresponding author:, milie@georgiasouthern.edu 2Augustin SEMENESCU2, Prof. National Science and Technology University Politehnica Bucharest, Spl.Independentei 313, Bucharest, Romania, augustin.semenescu@upb.ro |
PUBLISHED in Annals Academy of Romanian Scientists Series on Engineering Sciences
ISSN PRINT 2066 – 6950 ISSN ONLINE 2066 – 8570 |
