Intermittent demand, inventory obsolescence, and temporal aggregation forecasts

References

• Athanasopoulos, G., R. J. Hyndman, N. Kourentzes, and F. Petropoulos. 2017. “Forecasting with Temporal Hierarchies.” European Journal of Operational Research 262 (1): 60–74. doi:10.1016/j.ejor.2017.02.046.
   Web of Science ®Google Scholar
• Azevedo, V. G., and L. M. Campos. 2016. “Combination of Forecasts for the Price of Crude Oil on the Spot Market.” International Journal of Production Research 54 (17): 5219–5235. doi:10.1080/00207543.2016.1162340.
   Web of Science ®Google Scholar
• Babai, M. Z., J. E. Boylan, and B. Rostami-Tabar. 2022. “Demand Forecasting in Supply Chains: A Review of Aggregation and Hierarchical Approaches.” International Journal of Production Research 60 (1): 324–348. doi:10.1080/00207543.2021.2005268.
   Web of Science ®Google Scholar
• Babai, M. Z., Y. Dallery, S. Boubaker, and R. Kalai. 2019. “A New Method to Forecast Intermittent Demand in the Presence of Inventory Obsolescence.” International Journal of Production Economics 209: 30–41. doi:10.1016/j.ijpe.2018.01.026.
   Web of Science ®Google Scholar
• Babai, M. Z., A. A. Syntetos, and R. Teunter. 2014. “Intermittent Demand Forecasting: An Empirical Study on Accuracy and the Risk of Obsolescence.” International Journal of Production Economics 157: 212–219. doi:10.1016/j.ijpe.2014.08.019.
   Web of Science ®Google Scholar
• Babai, M. Z., A. Tsadiras, and C. Papadopoulos. 2020. “On the Empirical Performance of Some new Neural Network Methods for Forecasting Intermittent Demand.” IMA Journal of Management Mathematics 31 (3): 281–305. doi:10.1093/imaman/dpaa003.
   Web of Science ®Google Scholar
• Balugani, E., F. Lolli, R. Gamberini, B. Rimini, and M. Z. Babai. 2019. “A Periodic Inventory System of Intermittent Demand Items with Fixed Lifetimes.” International Journal of Production Research 57 (22): 6993–7005. doi:10.1080/00207543.2019.1572935.
   Web of Science ®Google Scholar
• Borchers, H. W., and M. H. W. Borchers. 2021. “Package ‘pracma’”.
   Google Scholar
• Boylan, J. E., and M. Z. Babai. 2016. “On the Performance of Overlapping and Non-overlapping Temporal Demand Aggregation Approaches.” International Journal of Production Economics 181: 136–144. doi:10.1016/j.ijpe.2016.04.003.
   Web of Science ®Google Scholar
• Clemen, R. T. 1989. “Combining Forecasts: A Review and Annotated Bibliography.” International Journal of Forecasting 5 (4): 559–583. doi:10.1016/0169-2070(89)90012-5.
   Web of Science ®Google Scholar
• Croston, J. D. 1972. “Forecasting and Stock Control for Intermittent Demands.” Journal of the Operational Research Society 23 (3): 289–303. doi:10.1057/jors.1972.50.
   Web of Science ®Google Scholar
• Ducharme, C., B. Agard, and M. Trépanier. 2021. “Forecasting a Customer's Next Time Under Safety Stock.” International Journal of Production Economics 234: 108044. doi:10.1016/j.ijpe.2021.108044.
   Web of Science ®Google Scholar
• Fu, W., and C. F. Chien. 2019. “UNISON Data-Driven Intermittent Demand Forecast Framework to Empower Supply Chain Resilience and an Empirical Study in Electronics Distribution.” Computers & Industrial Engineering 135: 940–949. doi:10.1016/j.cie.2019.07.002.
   Web of Science ®Google Scholar
• Hasni, M., M. S. Aguir, M. Z. Babai, and Z. Jemai. 2019. “Spare Parts Demand Forecasting: A Review on Bootstrapping Methods.” International Journal of Production Research 57 (15–16): 4791–4804. doi:10.1080/00207543.2018.1424375.
   Web of Science ®Google Scholar
• Hollyman, R., F. Petropoulos, and M. E. Tipping. 2021. “Understanding Forecast Reconciliation.” European Journal of Operational Research 294 (1): 149–160. doi:10.1016/j.ejor.2021.01.017.
   Web of Science ®Google Scholar
• Hyndman, R. J., R. A. Ahmed, G. Athanasopoulos, and H. L. Shang. 2011. “Optimal Combination Forecasts for Hierarchical Time Series.” Computational Statistics & Data Analysis 55 (9): 2579–2589. doi:10.1016/j.csda.2011.03.006.
   Web of Science ®Google Scholar
• Hyndman, R. J., and G. Athanasopoulos. 2018. Forecasting: Principles and Practice. Melbourne: OTexts. https://otexts.com/fpp2/.
   Google Scholar
• Hyndman, R. J., and Y. Khandakar. 2008. “Automatic Time Series Forecasting: The Forecast Package for R.” Journal of Statistical Software 27 (1): 1–22. doi:10.18637/jss.v027.i03.
   Google Scholar
• Jiang, P., Y. Huang, and X. Liu. 2020. “Intermittent Demand Forecasting for Spare Parts in the Heavy-Duty Vehicle Industry: A Support Vector Machine Model.” International Journal of Production Research 59 (24): 7423–7744. doi:10.1080/00207543.2020.1842936.
   Web of Science ®Google Scholar
• Judge, G. G., and T. Takayama. 1966. “Inequality Restrictions in Regression Analysis.” Journal of the American Statistical Association 61 (313): 166–181. doi:10.1080/01621459.1966.10502016.
   Web of Science ®Google Scholar
• Kourentzes, N. 2013. “Intermittent Demand Forecasts with Neural Networks.” International Journal of Production Economics 143 (1): 198–206. doi:10.1016/j.ijpe.2013.01.009.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Kourentzes, N., and G. Athanasopoulos. 2021. “Elucidate Structure in Intermittent Demand Series.” European Journal of Operational Research 288 (1): 141–152. doi:10.1016/j.ejor.2020.05.046.
   Web of Science ®Google Scholar
• Kourentzes, N., and F. Petropoulos. 2014. “tsintermittent: Intermittent Time Series Forecasting.” Software https://cran.r-project.org/web/packages/tsintermittent/index.html.
   Google Scholar
• Kourentzes, N., F. Petropoulos, and J. R. Trapero. 2014. “Improving Forecasting by Estimating Time Series Structural Components Across Multiple Frequencies.” International Journal of Forecasting 30 (2): 291–302. doi:10.1016/j.ijforecast.2013.09.006.
   Web of Science ®Google Scholar
• Kourentzes, N., B. Rostami-Tabar, and D. K. Barrow. 2017. “Demand Forecasting by Temporal Gggregation: Using Optimal or Multiple Aggregation Levels.” Journal of Business Research 78: 1–9. doi:10.1016/j.jbusres.2017.04.016.
   Web of Science ®Google Scholar
• Lawson, C. L., and R. J. Hanson. 1974. “Linear Least Squares with Linear Inequality Constraints.” Solving Least Squares Problems, 158–173.
   Google Scholar
• Lei, M., S. Li, and Q. Tan. 2016. “Intermittent Demand Forecasting with Fuzzy Markov Chain and Multi Aggregation Prediction Algorithm.” Journal of Intelligent & Fuzzy Systems 31 (6): 2911–2918. doi:10.3233/JIFS-169174.
   Web of Science ®Google Scholar
• Makridakis, S., S. C. Wheelwright, and R. J. Hyndman. 2008. Forecasting Methods and Applications. New York: John wiley & sons.
   Google Scholar
• Mircetic, D., B. Rostami-Tabar, S. Nikolicic, and M. Maslaric. 2022. “Forecasting Hierarchical Time Series in Supply Chains: An Empirical Investigation.” International Journal of Production Research 60 (8): 2514–2533. doi:10.1080/00207543.2021.1896817.
   Web of Science ®Google Scholar
• Nikolopoulos, K., A. A. Syntetos, J. E. Boylan, F. Petropoulos, and V. Assimakopoulos. 2011. “An Aggregate–Disaggregate Intermittent Demand Approach (ADIDA) to Forecasting: An Empirical Proposition and Analysis.” Journal of the Operational Research Society 62 (3): 544–554. doi:10.1057/jors.2010.32.
   Web of Science ®Google Scholar
• Petropoulos, F., and N. Kourentzes. 2015. “Forecast Combinations for Intermittent Demand.” Journal of the Operational Research Society 66 (6): 914–924. doi:10.1057/jors.2014.62.
   Web of Science ®Google Scholar
• Prestwich, S., R. Rossi, S. Armagan Tarim, and B. Hnich. 2014. “Mean-based Error Measures for Intermittent Demand Forecasting.” International Journal of Production Research 52 (22): 6782–6791. doi:10.1080/00207543.2014.917771.
   Web of Science ®Google Scholar
• Prestwich, S. D., S. A. Tarim, and R. Rossi. 2021. “Intermittency and Obsolescence: A Croston Method with Linear Decay.” International Journal of Forecasting 37 (2): 708–715. doi:10.1016/j.ijforecast.2020.08.010.
   Web of Science ®Google Scholar
• Rostami-Tabar, B., M. Z. Babai, A. A. Syntetos, and Y. Ducq. 2013. “Demand Forecasting by Temporal Aggregation.” Naval Research Logistics 60 (6): 479–498. doi:10.1002/nav.21546.
   Web of Science ®Google Scholar
• Sanguri, K., and K. Mukherjee. 2021. “Forecasting of Intermittent Demands Under the Risk of Inventory Obsolescence.” Journal of Forecasting 40 (6): 1054–1069. doi:10.1002/for.2761.
   Web of Science ®Google Scholar
• Sillanpää, V., and J. Liesiö. 2018. “Forecasting Replenishment Orders in Retail: Value of Modelling low and Intermittent Consumer Demand with Distributions.” International Journal of Production Research 56 (12): 4168–4185. doi:10.1080/00207543.2018.1431413.
   Web of Science ®Google Scholar
• Syntetos, A. A., M. Z. Babai, Y. Dallery, and R. Teunter. 2009. “Periodic Control of Intermittent Demand Items: Theory and Empirical Analysis.” Journal of the Operational Research Society 60 (5): 611–618. doi:10.1057/palgrave.jors.2602593.
   Web of Science ®Google Scholar
• Syntetos, A. A., and J. E. Boylan. 2005. “The Accuracy of Intermittent Demand Estimates.” International Journal of Forecasting 21 (2): 303–314. doi:10.1016/j.ijforecast.2004.10.001.
   Web of Science ®Google Scholar
• Syntetos, A. A., and J. E. Boylan. 2008. “Demand Forecasting Adjustments for Service-Level Achievement.” IMA Journal of Management Mathematics 19 (2): 175–192. doi:10.1093/imaman/dpm034.
   Web of Science ®Google Scholar
• Syntetos, A., D. Lengu, and M. Z. Babai. 2013. “A Note on the Demand Distributions of Spare Parts.” International Journal of Production Research 51 (21): 6356–6358. doi:10.1080/00207543.2013.798050.
   Web of Science ®Google Scholar
• Teunter, R., and B. Sani. 2009. “Calculating Order-up-to Levels for Products with Intermittent Demand.” International Journal of Production Economics 118 (1): 82–86. doi:10.1016/j.ijpe.2008.08.012.
   Web of Science ®Google Scholar
• Teunter, R. H., A. A. Syntetos, and M. Z. Babai. 2011. “Intermittent Demand: Linking Forecasting to Inventory Obsolescence.” European Journal of Operational Research 214 (3): 606–615. doi:10.1016/j.ejor.2011.05.018.
   Web of Science ®Google Scholar
• Wallström, P., and A. Segerstedt. 2010. “Evaluation of Forecasting Error Measurements and Techniques for Intermittent Demand.” International Journal of Production Economics 128 (2): 625–636. doi:10.1016/j.ijpe.2010.07.013.
   Web of Science ®Google Scholar
• Wang, X., and F. Petropoulos. 2016. “To Select or to Combine? The Inventory Performance of Model and Expert Forecasts.” International Journal of Production Research 54 (17): 5271–5282. doi:10.1080/00207543.2016.1167983.
   Web of Science ®Google Scholar
• Wang, X., K. Smith, and R. J. Hyndman. 2006. “Characteristic-based Clustering for Time Series Data.” Data Mining and Knowledge Discovery 13 (3): 335–364. doi:10.1007/s10618-005-0039-x.
   Web of Science ®Google Scholar
• Wickramasuriya, S. L., G. Athanasopoulos, and R. J. Hyndman. 2019. “Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimisation.” Journal of the American Statistical Association 114 (526): 804–819. doi:10.1080/01621459.2018.1448825.
   Web of Science ®Google Scholar
• Wickramasuriya, S. L., B. A. Turlach, and R. J. Hyndman. 2020. “Optimal Non-negative Forecast Reconciliation.” Statistics and Computing 30 (5): 1167–1182. doi:10.1007/s11222-020-09930-0.
   Web of Science ®Google Scholar