Intermittent demand forecasts with neural networks

Kourentzes, Nikolaos (2013) Intermittent demand forecasts with neural networks. International Journal of Production Economics, 143 (1). pp. 198-206. ISSN 0925-5273

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Intermittent demand appears when demand events occur only sporadically. Typically such time series have few observations making intermittent demand forecasting challenging. Forecast errors can be costly in terms of unmet demand or obsolescent stock. Intermittent demand forecasting has been addressed using established forecasting methods, including simple moving averages, exponential smoothing and Croston’s method with its variants. This study proposes a neural network (NN) methodology to forecast intermittent time series. These NNs are used to provide dynamic demand rate forecasts, which do not assume constant demand rate in the future and can capture interactions between the non-zero demand and the inter-arrival rate of demand events, overcoming the limitations of Croston’s method. In order to mitigate the issue of limited fitting sample, which is common in intermittent demand, the proposed models use regularised training and median ensembles over multiple training initialisations to produce robust forecasts. The NNs are evaluated against established benchmarks using both forecasting accuracy and inventory metrics. The findings of forecasting and inventory metrics are conflicting. While NNs achieved poor forecasting accuracy and bias, all NN variants achieved higher service levels than the best performing Croston’s method variant, without requiring analogous increases in stock holding volume. Therefore, NNs are found to be effective for intermittent demand applications. This study provides further arguments and evidence against the use of conventional forecasting accuracy metrics to evaluate forecasting methods for intermittent demand, concluding that attention to inventory metrics is desirable.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Production Economics
Additional Information:
The final, definitive version of this article will be published in the Journal, International Journal of Production Economics 143 (1), 2013, © ELSEVIER.
Uncontrolled Keywords:
?? intermittent demandneural networkscroston's methodforecastingslow moving itemsbusiness, management and accounting(all)economics and econometricsmanagement science and operations researchindustrial and manufacturing engineeringdiscipline-based research ??
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Deposited On:
10 Jan 2013 13:28
Last Modified:
02 Dec 2023 01:14