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

Suriyan Jomthanachai,Wai-Peng Wong,Chee-Peng Lim

doi:10.1109/access.2021.3087623

Abstract

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.

Highlights

The significant transformation of the business world due to globalization leads to rapid changes in operations and customer demands
Data Envelopment Analysis (DEA) APPROACH FOR Failure Mode and Effect Analysis (FMEA) AND THE EXTENSION OF THE DEA CROSS-EFFICIENCY METHOD In the traditional DEA method, a set of decision-making units (DMUs) is formed, utilizing the inputs X ∈ x to deliver the outputs Y ∈ y, where m and s are the numbers of the inputs and outputs, respectively
The multiplier formulation of an inputoriented structure to indicate the constant returns of scale (CRS) situation [46] is shown in model (2): max θ = ∑ μ y s. t. μ y − ν x ≤ 0 νx =1 μ, ν ≥ 0(ε) where subscript or characterizes the DMU under evaluation, ε is non-Archimedean infinitesimal

Summary

Introduction

The significant transformation of the business world due to globalization leads to rapid changes in operations and customer demands. Business organizations nowadays face intense competition and arduous challenges. In today’s dynamic environments, organizations need to tackle various uncertainties and handle them effectively. In this respect, risk management is a robust approach to becoming prepared to face risks and their consequences [1]. Identification and assessment of risks are a crucial aspect of the risk management process [2]. Risk assessment includes the analysis and evaluation processes. It requires a sensible technique to ascertain the qualitative and/or quantitative risk levels and examine the prospective outcomes of possible failures with respect to an organization’s resources [3]

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