Abstract
Inventory management is essential for economic success of companies since it represents a significant part of their financial balance. Stockouts represent one of the major issues that inventory management has to deal with. In case that enough stock is not available to meet demand, sales are typically a downward biased measurement of demand. Censored modelling is then necessary to forecast true demand, while the only information available are sales data. This paper develops an Exponential Smoothing forecasting model in a state space framework for censored data, so usual in supply chain contexts. The examples show how relevant this issue is and how the same inventory policy produces an important reduction in lost sales when an appropriate model including censorship is taken into account.
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Acknowledgements
This work was supported by the European Regional Development Fund and Junta de Comunidades de Castilla-La Mancha (JCCM/FEDER, UE) under the project with reference SBPLY/19/180501/000151.
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Pedregal, D.J., Trapero, J.R., Holgado, E. (2024). Censored Exponential Smoothing for Supply Chain Forecasting. In: Bautista-Valhondo, J., Mateo-Doll, M., Lusa, A., Pastor-Moreno, R. (eds) Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023). CIO 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-031-57996-7_35
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DOI: https://doi.org/10.1007/978-3-031-57996-7_35
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