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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Censored Exponential Smoothing for Supply Chain Forecasting
verfasst von : Diego J. Pedregal, Juan Ramón Trapero, Enrique Holgado
Verlag: Springer Nature Switzerland
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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.