The COVID-19 pandemic has an adverse impact on the global trade supply chain. Countries where the economy is driven by global trade, either as exporters or importers and are faced with the problem of declining imports and exports. This is due to the interruption of the main players of the global supply chain (i.e., production, logistics and transportation sector) as well as the slow-down in consumption of overseas customers. This paper presents the development of an efficiency related metric from the Coherent Data Envelopment Analysis (CoDEA) method for assessing the vulnerability (or conversely, the robustness) levels of the supply chain system of six ASEAN countries. The results reveal that Thailand is most vulnerable to international supply chain issues indicated by its lowest efficiency score. This is due to Thailand's severe disruption of logistics and transportation systems compared with its neighboring countries. In contrast, Vietnam is the most robust because of its efficiency in the exports sector. Our research reveals that trading partners with a lower risk and the ability to rapidly recover their import volume reflect their less vulnerable supply chains. This research provides the associated strategies to establish a resilient global supply chain in spite of the COVID-19 pandemic.

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An integrated method comprising Data Envelopment Analysis (DEA) and machine learning (ML) 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 Failure Mode and Effect Analysis (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. Based on a case study with a group of logistics service providers, the results ascertain that the combined DEA and ML approach offers a flexible and reasonable alternative in risk management. The approach allow decision-makers or managers to assess and monitor 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, along with the predicted results. The outcome of our empirical evaluation indicates that the proposed approach contributes towards robustness in sustainable business operations.

This study demonstrates how to profit from up-to-date dynamic economic big data, which contributes to selecting economic attributes that indicate logistics performance as reflected by the Logistics Performance Index (LPI). The analytical technique employs a high degree of productivity in machine learning (ML) for prediction or regression using adequate economic features. The goal of this research is to determine the ideal collection of economic attributes that best characterize a particular anticipated variable for predicting a country’s logistics performance. In addition, several potential ML regression algorithms may be used to optimize prediction accuracy. The feature selection of filter techniques of correlation and principal component analysis (PCA), as well as the embedded technique of LASSO and Elastic-net regression, is utilized. Then, based on the selected features, the ML regression approaches artificial neural network (ANN), multi-layer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and Ridge regression are used to train and validate the data set. The findings demonstrate that the PCA and Elastic-net feature sets give the closest to adequate performance based on the error measurement criteria. A feature union and intersection procedure of an acceptable feature set are used to make a more precise decision. Finally, the union of feature sets yields the best results. The findings suggest that ML algorithms are capable of assisting in the selection of a proper set of economic factors that indicate a country's logistics performance. Furthermore, the ANN was shown to be the best effective prediction model in this investigation.

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