Titlebook: Service Parts Management; Demand Forecasting a Nezih Altay,Lewis A. Litteral Book 2011 Springer-Verlag London Limited 2011 Forecasting.Inte

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Distributional Assumptions for Parametric Forecasting of Intermittent Demand,imating the moments of such a distribution. For the case of fast demand items the Normality assumption is typically sufficient. However, spare parts typically exhibit intermittent or irregular demand patterns that may not be represented by the Normal distribution. The objective of this work is three

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Decision Trees for Forecasting Trended Demand,e been previously established, we introduce a new protocol, based on serial variation curves, and a modification of a protocol suggested by Gardner and McKenzie. We also introduce two new decision trees, based on the new protocols, to provide simple ways of choosing between exponential smoothing met

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The Impact of Aggregation Level on Lumpy Demand Management,ick changes in the final customer’s preferences and taste are a common cause of demand variability (e.g., in the fashion industry demand for a given color can change dramatically from year to year). Marketing activities may also lead demand to suddenly change e.g., when promotional activities are co

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Forecasting and Inventory Management for Spare Parts: An Installed Base Approach,and forecasting and safety inventory management a major challenge. Mainstream stochastic inventory management approaches for spare parts make use ofdistributional assumptions for demands. In reality, distributions and their parameters are hardly known and need to be estimated. In this paper we pursu

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A Decision Making Framework for Managing Maintenance Spare Parts in Case of Lumpy Demand: Action Re(Taracki et al. .). According to Harland et al. (.), “outsourcing involves the use of specialists to provide competences, technologies and resources”. Three fundamental factors nowadays bring firms to use outsourcing in maintenance: (i) cost reduction, since providers are more experienced, thus they

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