Understanding Machine Learning-Based Purchase Order Recommendation in Motorcycle Spare Parts Warehouse

This work presents a conceptual and interpretative analysis of machine learning-based approaches for Purchase Order (PO) recommendation in a motorcycle spare parts warehouse context. The study compares conventional rule-based inventory ordering practices with linear and non-linear machine learning models to examine how sales history and inventory signals are utilized in decision support. Rather than focusing on predictive benchmarking, this work emphasizes model interpretability and practical insights for inventory management. The findings highlight the differences in decision logic between rule-based and machine learning-based methods and discuss their implications for real-world warehouse operations. This manuscript is intended as an independent research contribution and serves as a reference for practitioners and researchers interested in data-driven inventory decision support systems.