Intermittent demand: Linking forecasting to inventory obsolescence
Intermittent demand: Linking forecasting to inventory obsolescence
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DOI：
10.1016/j.ejor.2011.05.018
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发表时间：
2011-11-01
影响因子：
6.4
通讯作者：
Babai, M. Zied
中科院分区：
文献类型：
--
作者：
Teunter, Ruud H.;Syntetos, Aris A.;Babai, M. Zied
预测间歇性需求的标准方法是Croston的方法。这种方法可用于SAP和专门预测软件包（例如Forecast Pro）等ERP类解决方案，并经常在实践中应用。它使用指数平滑分别更新估计的需求规模和需求间隔时，一个积极的需求发生，和他们的比率提供了每个时期的需求预测。Croston方法有两个重要的缺点。首先也是最重要的是，在（许多）零需求时期后不进行更新会使该方法不适合处理过时问题。第二，该方法是正偏的，并且这对于所有时间点（即考虑在任意时间段做出的预测）和仅发布点（即仅考虑在正需求发生之后的预测）都是真实的。第二个问题已在文献中提出的估计（Syntetos-Boylan近似，SBA）是近似无偏的。在本文中，我们提出了一种新的方法，克服了这两个缺点，同时不增加复杂性。与Croston方法不同的是，新方法是无偏的（对于所有时间点），它更新的需求概率，而不是需求间隔，这样做在每个周期。新的估计的比较优点进行评估，通过广泛的模拟实验。结果表明，其上级性能，并使洞察需求预测和过时之间的联系。(C)2011 Elsevier B. V.保留所有权利。
The standard method to forecast intermittent demand is that by Croston. This method is available in ERP-type solutions such as SAP and specialised forecasting software packages (e.g. Forecast Pro), and often applied in practice. It uses exponential smoothing to separately update the estimated demand size and demand interval whenever a positive demand occurs, and their ratio provides the forecast of demand per period. The Croston method has two important disadvantages. First and foremost, not updating after (many) periods with zero demand renders the method unsuitable for dealing with obsolescence issues. Second, the method is positively biased and this is true for all points in time (i.e. considering the forecasts made at an arbitrary time period) and issue points only (i.e. considering the forecasts following a positive demand occurrence only). The second issue has been addressed in the literature by the proposal of an estimator (Syntetos-Boylan Approximation, SBA) that is approximately unbiased. In this paper, we propose a new method that overcomes both these shortcomings while not adding complexity. Different from the Croston method, the new method is unbiased (for all points in time) and it updates the demand probability instead of the demand interval, doing so in every period. The comparative merits of the new estimator are assessed by means of an extensive simulation experiment. The results indicate its superior performance and enable insights to be gained into the linkage between demand forecasting and obsolescence. (C) 2011 Elsevier B.V. All rights reserved.