Derived Feature Engineering and ABC–XYZ Segmentation for Machine Learning-Based Forecasting of Intermittent Spare Parts Demand

Abstract

Forecasting spare parts demand is challenging due to its intermittent, sparse, and highly irregular nature. Traditional inventory strategies, based on stable demand patterns, often lead to inefficiencies, including excess inventory and poor service performance. This study examines the impact of feature engineering combined with ABC–XYZ inventory segmentation on forecasting accuracy in a real-world industrial context. A biweekly forecasting framework was developed using six years (2019–2024) of transactional data from ERP and Field Service Management (FSM) systems of a forklift service company. Fifteen derived features capturing demand dynamics, intermittency, service behavior, and statistical structure were constructed and evaluated using Random Forest, XGBoost, and Support Vector Regression (SVR) models. The results show that restricting modeling to AY/BY inventory categories substantially improves predictive accuracy, reducing RMSE from >22 to <3 compared to full-SKU modeling. A reduced seven-feature set further lowers XGBoost’s RMSE to 2.51 (MAE = 2.14), achieving the best performance across all tested configurations on the 2024 hold-out period. The best-performing configuration achieves a Predicted-Demand Turnover Index (PDTI) of 44.13, compared with a baseline actual stock turnover of 2.78 (€65,944 actual demand/€23,721 historical average stock). PDTI is a theoretical scenario index; operationalizing it requires inventory-policy simulation under realistic constraints. These findings highlight that forecasting performance in intermittent-demand environments depends more on data representation and segmentation than on model selection alone. The study provides a reproducible, interpretable framework for integrating feature engineering and inventory segmentation into data-driven inventory management.