Enhancing Intermittent Spare Part Demand Forecasting: A Novel Ensemble Approach with Focal Loss and SMOTE

Saskia Puspa Kenaka (), Andi Cakravastia, Anas Ma’ruf and Rully Tri Cahyono
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Saskia Puspa Kenaka: Industrial Engineering Program, Faculty of Industrial Technology, Bandung Institute of Technology, Bandung 40132, Indonesia
Andi Cakravastia: Industrial Engineering Program, Faculty of Industrial Technology, Bandung Institute of Technology, Bandung 40132, Indonesia
Anas Ma’ruf: Industrial Engineering Program, Faculty of Industrial Technology, Bandung Institute of Technology, Bandung 40132, Indonesia
Rully Tri Cahyono: Industrial Engineering Program, Faculty of Industrial Technology, Bandung Institute of Technology, Bandung 40132, Indonesia

Logistics, 2025, vol. 9, issue 1, 1-25

Abstract: Background : Accurate inventory management of intermittent spare parts requires precise demand forecasting. The sporadic and irregular nature of demand, characterized by long intervals between occurrences, results in a significant data imbalance, where demand events are vastly outnumbered by zero-demand periods. This challenge has been largely overlooked in forecasting research for intermittent spare parts. Methods : The proposed model incorporates the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset and uses focal loss to enhance the sensitivity of deep learning models to rare demand events. The approach was empirically validated by comparing the model’s Mean Squared Error (MSE) performance and Area Under the Curve (AUC). Results : The ensemble model achieved a 47% reduction in MSE and a 32% increase in AUC, demonstrating substantial improvements in forecasting accuracy. Conclusions : The findings highlight the effectiveness of the proposed method in addressing data imbalance and improving the prediction of intermittent spare part demand, providing a valuable tool for inventory management.

Keywords: intermittent demand; spare part; ensemble learning; SMOTE; loss function (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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