An integrated method comprising DEA and machine learning for risk management is proposed in this paper. Initially, in the process of risk assessment, the DEA cross-efficiency method is used to evaluate a set of risk factors obtained from the FMEA. This FMEA-DEA cross-efficiency method not only overcomes some drawbacks of FMEA, but also eliminates several limitations of DEA to offer a high discrimination capability of decision units. For risk treatment and monitoring processes, an ML mechanism is utilized to predict the degree of remaining risk depending on simulated data corresponding to the risk treatment scenario. Prediction using ML is more accurate since the predictive power of this model is better than that of DEA which potentially contains errors. The motivation for this study is that the combination of the DEA and ML approaches gives a flexible and realistic choice in risk management. Based on a case study of logistics business, the results ascertain that the short-term and urgent solutions in service cost and performance are necessary to sustainable logistics operations under the COVID-19 pandemic. The prediction findings show that the risk of skilled personnel is the next concern once the service cost and performance strategies have been prioritised. This approach allow decision-makers to assess the risk level for handling forthcoming events in unusual conditions. It also serves as a useful knowledge repository such that appropriate risk mitigation strategies can be planned and monitored. The outcome of our empirical evaluation indicates that the proposed approach contributes towards robustness in sustainable business operations.

中文翻译：

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数据包络分析和机器学习方法在风险管理中的应用

本文提出了一种包括 DEA 和机器学习的风险管理集成方法。最初，在风险评估过程中，DEA 交叉效率法用于评估从 FMEA 中获得的一组风险因素。这种 FMEA-DEA 交叉效率方法不仅克服了 FMEA 的一些缺点，而且消除了 DEA 的几个限制，提供了对决策单元的高辨别能力。对于风险处理和监控过程，利用机器学习机制根据风险处理场景对应的模拟数据预测剩余风险的程度。使用 ML 的预测更准确，因为该模型的预测能力优于可能包含错误的 DEA。这项研究的动机是 DEA 和 ML 方法的结合为风险管理提供了灵活和现实的选择。基于物流业务的案例研究，结果确定服务成本和性能方面的短期和紧急解决方案对于 COVID-19 大流行下的可持续物流运营是必要的。预测结果表明，一旦确定了服务成本和绩效策略的优先级，技术人员的风险将成为下一个问题。这种方法允许决策者评估在异常情况下处理即将发生的事件的风险级别。它还可以作为一个有用的知识库，以便可以规划和监控适当的风险缓解策略。