AI Summary & Key Takeaways
AI InsightResults showed that combination is the best method for the majority of SKUs.
📌 3 Key Takeaways
Abstract
Effective inventory management requires accurate forecasts for stock-keeping units (SKUs), especially for the strategic ones for companies’ operations and after-sales services like providing spare parts. Forecasting is a challenging task for such SKUs as they usually have intermittent demand (ID) patterns, consisting of many periods with zero demand and infrequent demand arrivals. Given the highly uncertain nature of ID for SKUs, this study developed a methodological framework for combining statistical and judgmental forecasts and assessed the performance of the proposed framework by using accuracy and bias measures. The forecasting process has several steps, including data preparation, data categorization based on demand patterns, generating statistical and judgmental forecasts, combining statistical and judgmental forecasts, and evaluating the forecast performance. These steps were illustrated on a real-world dataset that contains monthly customer demand data for after-sales spare parts. Results showed that combination is the best method for the majority of SKUs. This paper contributes to the limited literature by addressing the gap between the combined and ID forecasts. The proposed framework gives practitioners and researchers a comprehensive overview to help them make more accurate forecasts while encouraging the use of simple but structured approaches.