Bayesian Inference–Based Demand Forecasting for Supply Chain Inventory Optimization in SME Pet Retail: A Case Study

Abstract

Effective inventory management is critical for small- and medium-sized enterprise (SME) pet shops facing fluctuating demand, seasonal trends, and limited storage. This study develops a Bayesian inference–based model to optimize inventory forecasting in SME pet retail supply chains. Using 300 historical sales transactions from a suburban Malaysian pet shop, supplemented by 5,000 synthetic observations generated via Tabular ACTGAN to preserve distributional traits, the model probabilistically predicts stock levels. Inventory is classified into overstock, maximum, minimum, and stockout categories to trigger replenishment signals. Performance was assessed using sensitivity analyses, per-class recall, and F1-scores, yielding a macro-average F1 of 0.42 and a weighted-average F1 of 0.77. The model showed strong predictive capability for overstock conditions (0.88 recall), though minority-stock categories—maximum (0.29) and minimum (0.21)—proved more challenging to forecast. Overall, this probabilistic approach provides viable early-warning signals that reduce overstock and stockout risks for resource-limited retailers. Future work will investigate real-time tracking, hybrid machine learning, and multi-store data integration.