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Intermittent demand forecasts with neural networks

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Intermittent demand forecasts with neural networks. / Kourentzes, Nikolaos.
In: International Journal of Production Economics, Vol. 143, No. 1, 05.2013, p. 198-206.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Kourentzes, N 2013, 'Intermittent demand forecasts with neural networks', International Journal of Production Economics, vol. 143, no. 1, pp. 198-206. https://doi.org/10.1016/j.ijpe.2013.01.009

APA

Kourentzes, N. (2013). Intermittent demand forecasts with neural networks. International Journal of Production Economics, 143(1), 198-206. https://doi.org/10.1016/j.ijpe.2013.01.009

Vancouver

Kourentzes N. Intermittent demand forecasts with neural networks. International Journal of Production Economics. 2013 May;143(1):198-206. Epub 2013 Jan 19. doi: 10.1016/j.ijpe.2013.01.009

Author

Kourentzes, Nikolaos. / Intermittent demand forecasts with neural networks. In: International Journal of Production Economics. 2013 ; Vol. 143, No. 1. pp. 198-206.

Bibtex

@article{f92a91a5db184515a565ef85b3c56112,
title = "Intermittent demand forecasts with neural networks",
abstract = "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 beenaddressed using established forecasting methods, including simple moving averages, exponential smoothing and Croston{\textquoteright}s method with its variants. Thisstudy proposes a neural network (NN) methodology to forecast intermittenttime series. These NNs are used to provide dynamic demand rate forecasts,which do not assume constant demand rate in the future and can captureinteractions between the non-zero demand and the inter-arrival rate of demand events, overcoming the limitations of Croston{\textquoteright}s method. In order tomitigate the issue of limited fitting sample, which is common in intermittentdemand, the proposed models use regularised training and median ensemblesover multiple training initialisations to produce robust forecasts. The NNsare evaluated against established benchmarks using both forecasting accuracy and inventory metrics. The findings of forecasting and inventory metricsare conflicting. While NNs achieved poor forecasting accuracy and bias, allNN variants achieved higher service levels than the best performing Croston{\textquoteright}s method variant, without requiring analogous increases in stock holdingvolume. Therefore, NNs are found to be effective for intermittent demandapplications. This study provides further arguments and evidence againstthe use of conventional forecasting accuracy metrics to evaluate forecastingmethods for intermittent demand, concluding that attention to inventorymetrics is desirable.",
keywords = "Intermittent demand, Neural Networks, Croston's Method, Forecasting, Slow moving items",
author = "Nikolaos Kourentzes",
note = "The final, definitive version of this article will be published in the Journal, International Journal of Production Economics 143 (1), 2013, {\textcopyright} ELSEVIER.",
year = "2013",
month = may,
doi = "10.1016/j.ijpe.2013.01.009",
language = "English",
volume = "143",
pages = "198--206",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Intermittent demand forecasts with neural networks

AU - Kourentzes, Nikolaos

N1 - The final, definitive version of this article will be published in the Journal, International Journal of Production Economics 143 (1), 2013, © ELSEVIER.

PY - 2013/5

Y1 - 2013/5

N2 - 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 beenaddressed using established forecasting methods, including simple moving averages, exponential smoothing and Croston’s method with its variants. Thisstudy proposes a neural network (NN) methodology to forecast intermittenttime series. These NNs are used to provide dynamic demand rate forecasts,which do not assume constant demand rate in the future and can captureinteractions between the non-zero demand and the inter-arrival rate of demand events, overcoming the limitations of Croston’s method. In order tomitigate the issue of limited fitting sample, which is common in intermittentdemand, the proposed models use regularised training and median ensemblesover multiple training initialisations to produce robust forecasts. The NNsare evaluated against established benchmarks using both forecasting accuracy and inventory metrics. The findings of forecasting and inventory metricsare conflicting. While NNs achieved poor forecasting accuracy and bias, allNN variants achieved higher service levels than the best performing Croston’s method variant, without requiring analogous increases in stock holdingvolume. Therefore, NNs are found to be effective for intermittent demandapplications. This study provides further arguments and evidence againstthe use of conventional forecasting accuracy metrics to evaluate forecastingmethods for intermittent demand, concluding that attention to inventorymetrics is desirable.

AB - 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 beenaddressed using established forecasting methods, including simple moving averages, exponential smoothing and Croston’s method with its variants. Thisstudy proposes a neural network (NN) methodology to forecast intermittenttime series. These NNs are used to provide dynamic demand rate forecasts,which do not assume constant demand rate in the future and can captureinteractions between the non-zero demand and the inter-arrival rate of demand events, overcoming the limitations of Croston’s method. In order tomitigate the issue of limited fitting sample, which is common in intermittentdemand, the proposed models use regularised training and median ensemblesover multiple training initialisations to produce robust forecasts. The NNsare evaluated against established benchmarks using both forecasting accuracy and inventory metrics. The findings of forecasting and inventory metricsare conflicting. While NNs achieved poor forecasting accuracy and bias, allNN variants achieved higher service levels than the best performing Croston’s method variant, without requiring analogous increases in stock holdingvolume. Therefore, NNs are found to be effective for intermittent demandapplications. This study provides further arguments and evidence againstthe use of conventional forecasting accuracy metrics to evaluate forecastingmethods for intermittent demand, concluding that attention to inventorymetrics is desirable.

KW - Intermittent demand

KW - Neural Networks

KW - Croston's Method

KW - Forecasting

KW - Slow moving items

U2 - 10.1016/j.ijpe.2013.01.009

DO - 10.1016/j.ijpe.2013.01.009

M3 - Journal article

VL - 143

SP - 198

EP - 206

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - 1

ER -