Federated machine learning for privacy preserving, collective supply chain risk prediction
Federated machine learning for privacy preserving, collective supply chain risk prediction
复制标题
联合机器学习用于隐私保护和集体供应链风险预测
DOI：
10.1080/00207543.2022.2164628
复制
发表时间：
2023-02
影响因子：
9.2
通讯作者：
Ge Zheng;Lingxuan Kong;A. Brintrup
中科院分区：
文献类型：
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作者：
Ge Zheng;Lingxuan Kong;A. Brintrup
使用人工智能（AI）预测供应链风险已变得流行。然而，尽管供应链具有相互关联的性质，但所提出的方法是基于组织在预测风险时单独行动而不是集体行动的前提。这就产生了一个问题：数据集不足的组织无法预测风险。虽然有人提议通过数据共享来评估风险，但实际上，由于隐私问题，这种情况并没有发生。我们提出了一种联邦学习方法来进行集体风险预测，而无需担心数据暴露的风险。我们问：数据集不足的组织能否利用集体知识？这就提出了第二个问题：在什么情况下集体风险预测是有益的？我们提出了一项实证案例研究，其中买家预测 Covid-19 前后共享供应商的订单延迟。结果表明，联邦学习确实可以帮助供应链成员有效预测风险，特别是对于数据集有限的买家而言。训练数据不平衡、中断和算法选择是影响该方法有效性的重要因素。有趣的是，对于订单量过大的买家来说，数据共享或集体风险预测并不总是最佳选择。因此，我们呼吁进一步研究供应链中的本地和集体学习范式。
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.