An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management

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