To read this content please select one of the options below:

Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology

Henry Lau (School of Business, Western Sydney University, Sydney, Australia)
Yung Po Tsang (School of Business, Western Sydney University, Sydney, Australia) (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR)
Dilupa Nakandala (School of Business, Western Sydney University, Sydney, Australia)
Carman K.M. Lee (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 30 April 2021

Issue publication date: 5 July 2021

1087

Abstract

Purpose

In the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.

Design/methodology/approach

A novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.

Findings

Throughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.

Originality/value

A novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

Keywords

Acknowledgements

The authors would like to thank Western Sydney University and the Department of Industrial and Systems Engineering in the Hong Kong Polytechnic University for supporting the research.

Citation

Lau, H., Tsang, Y.P., Nakandala, D. and Lee, C.K.M. (2021), "Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology", Industrial Management & Data Systems, Vol. 121 No. 7, pp. 1684-1703. https://doi.org/10.1108/IMDS-04-2020-0199

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

Related articles