The accuracy of demand forecasting in the automotive spare parts industry is critical to operational efficiency and financial performance. However, the irregular nature of spare parts demand makes forecasting processes quite complex. As traditional forecasting methods are unable to model this complex demand structure, researchers have developed more advanced and adaptive forecasting approaches. This study presents a comprehensive analysis to forecast the demand for products with intermittent and lumpy demand structure, which play a crucial role in the company’s sales process and operations, using real data from a company in the automotive spare parts industry. In addition, the results of Croston-based methods, ML, and DL models are compared, and an innovative forecasting approach is presented that integrates the outputs of these models with the stacking ensemble learning method. Empirical results and statistical tests confirm that the stacking method outperforms other models in forecasting intermittent and lumpy demand, highlighting the value of ensemble learning and advanced models.
Ünal et al. (Thu,) studied this question.