A forecasting analytics model for assessing forecast error in e-fulfilment performance
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 31 August 2022
Issue publication date: 8 November 2022
Abstract
Purpose
Demand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.
Design/methodology/approach
The paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.
Findings
A structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.
Originality/value
Earlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.
Keywords
Acknowledgements
The authors would like to thank the Research Grants Council of Hong Kong for supporting this research under the Grant UGC/FDS14/E06/19. Also, this project is also supported partially by the Big Data Intelligence Centre in The Hang Seng University of Hong Kong.
Citation
Ho, G.T.S., Choy, S.K., Tong, P.H. and Tang, V. (2022), "A forecasting analytics model for assessing forecast error in e-fulfilment performance", Industrial Management & Data Systems, Vol. 122 No. 11, pp. 2583-2608. https://doi.org/10.1108/IMDS-01-2022-0056
Publisher
:Emerald Publishing Limited
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