Combined Forecasts of Intermittent Demand for Stock-keeping Units (SKUs)
Main Article Content
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.
JEL classification: C44, C53, M11
Keywords: Statistical forecasting, Judgmental forecasting, Combining forecasts, Intermittent demand, Stock-keeping Units
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Kapucugil İkiz, A. and Halil Utma, G. (2023) “Combined Forecasts of Intermittent Demand for Stock-keeping Units (SKUs)”, World Journal of Applied Economics, 9(1), pp. 1-31. doi: 10.22440/wjae.9.1.1.
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References
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Franses, P. H., & Legerstee, R. (2010). Do Experts' Adjustments on Model-Based SKU-Level Forecasts Improve Forecast Quality? Journal of Forecasting, 29, 331-340. doi:10.1002/for.1129
Ghobbar, A. A., & Friend, C. H. (2002). Sources of Intermittent Demand for Aircraft Spare Parts within Airline Operations. Journal of Air Transport Management, 8, 221-231. doi:10.1016/S0969-6997(01)00054-0
Ghobbar, A. A., & Friend, C. H. (2003). Evaluation of Forecasting Methods for Intermittent Parts Demand in the Field of Aviation: A Predictive Model. Computers & Operations Research, 30, 2097-2114. doi:10.1016/S0305-0548(02)00125-9
Green, K. C., & Armstrong, J. S. (2007). Structured Analogies for Forecasting. International Journal of Forecasting, 23, 365-376. doi:10.1016/j.ijforecast.2007.05.005
Green, K. C., & Armstrong, J. S. (2015). Simple Versus Complex Forecasting: The Evidence. Journal of Business Research, 68, 1678-1685. doi:10.1016/j.jbusres.2015.03.026
Gutierrez, R. S., Solis, A. O., & Mukhopadhyay, S. (2008). Lumpy Demand Forecasting using Neural Networks. International Journal Production Economics, 111, 409-420. doi:10.1016/j.ijpe.2007.01.007
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Hasni, M., Babai, M. Z., Aguir, M. S., & Jemai, Z. (2019). An Investigation on Bootstrapping Forecasting Methods for Intermittent Demands. International Journal Production Economics, 209, 20-29. doi:10.1016/j.ijpe.2018.03.001
Hsu, C. C., & Sandford, B. A. (2007). The Delphi Technique: Making Sense of Consensus. Practical Assessment, Research, and Evaluation, 12, 1-8. doi:10.7275/pdz9-th90
Hua, Z. S., Zhang, B., Yang, J., & Tan, D. S. (2007). A New Approach of Forecasting Intermittent Demand for Spare Parts Inventories in the Process Industries. Journal of the Operational Research Society, 58, 52-61. doi:10.1057/palgrave.jors.2602119
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (Second ed.). Otexts, Melbourne.
Hyndman, R. J., & Koehler, A. B. (2006). Another Look at Measures of Forecast Accuracy. International Journal of Forecasting, 22, 679-688. doi:10.1016/j.ijforecast.2006.03.001
Johnston, F. R., & Boylan, J. E. (1996). Forecasting for Items with Intermittent Demand. The Journal of the Operational Research Society, 47, 113-121. doi:10.2307/2584256
Johnston, F. R., Boylan, J. E., & Shale, E. A. (2003). An Examination of the Size of Orders from Customers, their Characterisation and the Implications for Inventory Control for Slow-moving Items. The Journal of the Operational Research Society, 54, 833-837. doi:10.1057/palgrave.jors.2601586
Kostenko, A. V., & Hyndman, R. J. (2006). A Note on the Categorization of Demand Patterns. The Journal of the Operational Research Society, 57, 1256-1257. doi:10.1057/palgrave.jors.2602211
Kourentzes, N. (2013). Intermittent Demand Forecasts with Neural Networks. International Journal of Production Economics, 143, 198-206. doi:10.1016/j.ijpe.2013.01.009
Kourentzes, N. (2014). On Intermittent Demand Model Optimisation and Selection. International Journal of Production Economics, 156, 180-190. doi:10.1016/j.ijpe.2014.06.007
Kourentzes, N., & Petropoulos, F. (2016). R: Package 'tsintermittent'. R package manual.
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Lawrence, M., Goodwin, P., O'Connor, M., & Onkal, D. (2006). Judgmental Forecasting: A Review of Progress over the Last 25 Years. International Journal of Forecasting, 22, 493-518. doi:10.1016/j.ijforecast.2006.03.007
Leven, E., & Segerstedt, A. (2004). Inventory Control with a Modified Croston Procedure and Erlang Distribution. International Journal of Production Economics, 90, 361-367. doi:10.1016/S0925-5273(03)00053-7
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