Data-Driven Forecasting Method for Intermittent Demand
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Parts demand forecasting is a crucial technique for determining production quantities, pricing strategies, and inventory management. Unlike the demand patterns for construction equipment and automobiles, the demand for spare parts exhibits an intermittent pattern. The intervals between demand occurrences are irregular, and the quantity of demand varies significantly. These characteristics pose significant challenges for traditional time series models in capturing the demand patterns for parts. In this study, we propose a forecasting model designed to address the time series problems characterized by intermittent patterns. We demonstrate the effectiveness of the proposed model by applying it to real parts sales data for demand forecasting.