## 2 Literature Review
[5] studied risk management in the logistics sector. The authors classified the risks in logistics processes related to several external and internal factors affecting the logistics system that companies face. The risks faced by the company are expressed as follows: Organizational, information, supplier-partner, customer-related, market, transportation and force majeure. Then, [PERSON] and [PERSON] [43] proposed a sequential dynamic to evaluate risk reduction measures, considering that improving performance and sustainability in logistics processes can be resolved by performing risk analysis. The method comprises the Z-number, Dempster-Shafer evidence theory and Bayesian belief network. The processes in the automotive supply chain are resource and production planning, procurement, loading and unloading, transportation, etc. In addition, the method allows different decision-makers to identify risks in logistics sub-processes and take measures to reduce risks with new technologies. [PERSON] et al. [44] addressed the same problem to identify critical factors affecting food supply chains under uncertainty. First, the criteria were determined using the Delphi method and then weighted using the fuzzy AHP. The criteria were evaluated in four classes: durability-flexibility, communication-collaboration, transparency-traceability and regulation-standardization. Research results indicated that R&D investment, transparency-traceability, level of risk-sharing and collaboration among stakeholders are the critical criteria. Further, [PERSON] et al. [45] examined the sales-related disruptions and risks faced by SMEs and their supply chain partners in COVID-19. The results obtained from the case analysis show that developing different collaborations and using multiple resources as a precaution against disasters are more beneficial to the company. To select a Blockchain Technology-IoT platform for the SC network, [PERSON] et al. [46] applied a hybrid approach. First, the weights were calculated using the interval Weighted Impact Nonlinear Measurement System (WINGS) method. Then, the alternatives were evaluated using the interval VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique. The main criteria are popularity and support, transition complexity, operational features and technical. Next, [PERSON] et al. [47] proposed an approach involving fuzzy DEMATEL and ANP to evaluate resilient and sustainable supply chain performance. The proposed hybrid approach has been applied to an automotive supply chain in Mexico. While the results obtained emphasize that the social dimension is critical, it is stated that the environmental dimension is not essential in the chain. By surveying Chinese manufacturing companies, [PERSON] et al. [48] investigated how the digital supply chain affects the disruption risks of the supply chain. The research results not only express the advantages of the digital supply chain but also state that the dynamic capacity of the chain can reduce the risks of supply, production, demand and circulation interruptions. More recently, [PERSON] et al. [49] studied the well-known, most discussed green supplier-selection problem with a several risk criteria extension. The risks are price changes, production failures, financial and capacity instability, delays, product defect rate and material shortage. To select suppliers, the authors employed a hybrid method involving fuzzy AHP-Combined Compromise Solution and Fuzzy Stepwise Weight Evaluation Ratio Analysis-Failure Mode and Effects Analysis, which are frequently used techniques in the literature.