Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping

Syntetos, Aris A.; Babaï, Mohamed Zied; Gardner, Everette S.

doi:10.1016/j.jbusres.2015.03.034

Cited by 97 publications

(57 citation statements)

References 42 publications

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“…It is evident that all three graphs on the right column of figure 2 have similar characteristics with what is traditional perceived and classified as intermittent demand (figure 1) and thus intermitted demand forecasting approaches could be perfectly employable: from the original inception of Croston (1972) up to more advanced and recent developments (Willemain et al, 2004, Nikolopoulos et al, 2011Petropoulos and Kourentzes, 2014). For an extensive review for methods specifically designed for and empirically proved to perform well on intermitted data the reader may follow either for insights on simulated data or Syntetos et al (2015) for empirical evaluation on real data. 6 2(a).…”

Section: The Decomposition Lensmentioning

confidence: 87%

We need to talk about intermittent demand forecasting

Νικολόπουλος

2021

European Journal of Operational Research

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In this research essay we propose a methodological innovation through: a) advocating for the broader use of OR forecasting tools and in specific intermittent demand estimators for forecasting black and grey swans, as a simpler, faster and quite robust alternative to econometric probabilistic methods like EVT; b) demonstrating the use in such a context of a rather popular forecasting paradigm: the Naive method (forecasting short horizons) and the SBA method (foresight long horizons) through the ADIDA non-overlapping temporal aggregation method-improving framework, and c) arguing for a new way for deciding the strategic planning horizon for phenomena prone to the appearance of black and grey swans.

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Section: The Decomposition Lensmentioning

confidence: 87%

We need to talk about intermittent demand forecasting

Νικολόπουλος

2021

European Journal of Operational Research

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“…Interestingly, SES has been shown in many studies to perform very well in an intermittent context, despite the fact that it has been developed for fast, rather than intermittent, demand items (Syntetos et al, ). In periodic applications, such as the one considered here, SES is unbiased (Croston, ) and associated with a very robust performance (Syntetos, Babai, & Gardner Jr, ). As such, and in order to retain the relevance with the forecast method the case organization is using, demand forecasts are generated using SES.…”

Section: Simulationmentioning

confidence: 99%

Forecasting for remanufacturing: The effects of serialization

Goltsos

Syntetos

Laan

2019

J of Ops Management

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Remanufacturing operations rely upon accurate forecasts of demand and returned items. Return timing and quantity forecasts help estimate net demand (demand minus returns) requirements. Based on a unique data set of serialized transactional issues and returns from the Excelitas Group and one of their defense contractors, Qioptiq, we assess the empirical performance of some key methods in the area of returns forecasting. We extend their application (for net demand forecasting), by considering that demand is also subject to uncertainty and thus needs to be forecast. Information on remanufacturing costs allows for an evaluation of the inventory implications of such forecasts under various settings. A foray into the literature on information technologies enables a discussion on the interface between information availability and forecast accuracy and utility. We find that serialization accounts for considerable forecast accuracy benefits, and that the accuracy of demand forecasts is as important as that of returns. Further, we show how the combined returns and demand forecast uncertainty affects the inventory performance. Finally, we identify opportunities for further improvements for the operations of Qioptiq, and for remanufacturing operations in general.

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“…In any case, there is no sufficient empirical evidence that these methods are more accurate from the simpler ones (Syntetos et al 2015).…”

Section: Forecasting Supply Chain Sporadic Demandmentioning

confidence: 99%

“…A handful of more advanced but at the same more complex non-parametric methods has also been proposed over the years but these do not necessarily outperform the former (Syntetos et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Forecasting supply chain sporadic demand with nearest neighbor approaches

Νικολόπουλος

Babaï

Bozos

2016

International Journal of Production Economics

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One of the biggest challenges in Supply Chain Management (SCM) is to forecast sporadic demand. Our forecasting methods arsenal includes Croston s method, SBA and TSB as well as some more recent non-parametric advances, but none of these can identify and extrapolate patterns existing in data; this is essential as these patterns do appear quite often, driven by infrequent but nevertheless repetitive managerial practices. One could claim such patterns could be picked up by Artificial Intelligence approaches, however these do need large training datasets, unfortunately non-existent in industrial time series. Nearest Neighbors (NN) can however operate in these latter contexts, and pick up patterns even in short series. In this research we propose applying NN for supply chain data and we investigate the conditions under which these perform adequately through an extensive simulation. Furthermore, via an empirical investigation in automotive data we provide evidence that practitioners could benefit from employing supervised NN approaches. The contribution of this research is not in the development of a new theory, but in the proposition of a new conceptual framework that brings existing theory (i.e. NN) from Computer Science and Statistics and applies it successfully in an SCM setting.

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Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping

Cited by 97 publications

References 42 publications

We need to talk about intermittent demand forecasting

We need to talk about intermittent demand forecasting

Forecasting for remanufacturing: The effects of serialization

Forecasting supply chain sporadic demand with nearest neighbor approaches

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