A forecasting analytics model for assessing forecast error in e-fulfilment performance

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.

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