Ensemble learning for demand forecast of After-Market spare parts to empower data-driven value chain and an empirical study

Abstract

Demand forecast for spare parts in supply chains is essential for ensuring customer satisfaction while minimizing appropriate inventory. The after-market orders mainly depend on repair and maintenance that makes the present problem for demand forecast challenging owing to high variability in demand sizes and time intervals. It is critical to address market fluctuation for effective demand forecast to reduce the risks of oversupply and shortage for supply chain resilience. Intelligent data-driven technologies should be developed to promote value integration and value co-creation among supply chain partners for digital transformation. The shortening product life cycle and the reducing lot sizes of diverse products have increased the challenges of demand forecast and supply chain management. This study aims to classify the demand patterns and develop the corresponding models via stacking ensemble approach to improve the overall forecasting performance. This study develops an alarm system to monitor the performance of the proposed approach and a systematic mechanism for retraining the model to maintain the decision quality. An empirical study is conducted in a leading automotive after-market component manufacturer for validation in real settings. The results have shown the forecast errors and the total cost can be effectively reduced by the developed solution.