State-space ARIMA for supply-chain forecasting

Svetunkov, Ivan and Boylan, John Edward (2020) State-space ARIMA for supply-chain forecasting. International Journal of Production Research, 58 (3). pp. 818-827. ISSN 0020-7543

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Abstract

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Production Research
Additional Information:
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 04/04/2019, available online: https://www.tandfonline.com/doi/full/10.1080/00207543.2019.1600764
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2209
Subjects:
ID Code:
132486
Deposited By:
Deposited On:
09 Apr 2019 12:10
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Sep 2020 04:30