Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology

Abstract

Purpose

Accurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.

Design/methodology/approach

The paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.

Findings

A structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.

Research limitations/implications

Results from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.

Originality/value

Earlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

Citation

Leung, K.H., Mo, D.Y., Ho, G.T.S., Wu, C.H. and Huang, G.Q. (2020), "Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology", Industrial Management & Data Systems, Vol. 120 No. 6, pp. 1149-1174. https://doi.org/10.1108/IMDS-12-2019-0646