Federated machine learning for privacy preserving, collective supply chain risk prediction

The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem...

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Bibliographic Details
Published in:International journal of production research Vol. 61; no. 23; pp. 8115 - 8132
Main Authors: Zheng, Ge, Kong, Lingxuan, Brintrup, Alexandra
Format: Journal Article
Language:English
Published: London Taylor & Francis 02.12.2023
Taylor & Francis LLC
Subjects:
Algorithms
Artificial intelligence
Datasets
federated learning
Machine learning
Predictions
Privacy
privacy preserving
Risk
Supply chain
Supply chains
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary:The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has been proposed to evaluate risk, in practice this does not happen due to privacy concerns. We propose a federated learning approach for collective risk prediction without the risk of data exposure. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This raises a second question: Under what circumstances would collective risk prediction be beneficial? We present an empirical case study where buyers predict order delays from their shared suppliers before and after Covid-19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers with limited datasets. Training data-imbalance, disruptions, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers with disproportionately larger order-books. We thus call for further research on on local and collective learning paradigms in supply chains.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2022.2164628