The prevalence of the COVID-19 epidemic has shaken things up in all businesses around the world, particularly in the global freight sector, causing severe economic consequences. The pandemic has exposed the vulnerabilities of many companies, especially those that depend on global supply chains and are too dependent on production centers and large markets. The threat of the expanding COVID-19 outbreak has raised concerns worldwide about the damage and recovery of global supply chains [1,2]. To avert a large-scale infection, travel restrictions, temporary closures, and medical supplies such as gloves, face masks, ventilators, and other personal protective equipment have all been enforced by governments as preventive measures [3]. These constraints lead to shortages of labor and raw materials. As a result, the global supply chain has faced delays and inventory shortages. Supply chains and goods were disrupted across most sectors [4]. COVID-19 had a notably negative impact on all car manufacturers and the majority of industrial product makers, according to them. In light of this, most automakers are shutting down production at some of their plants. Global output for the automotive industry is expected to decline by 13% due to travel restrictions and spare parts shortages [5]. The COVID-19 problem has underlined the necessity of robust and sustainable supply chains. To elevate global competitiveness, any business must incorporate sustainability objectives into their underlying supply chain networks, particularly response tactics in the COVID-19 pandemic; such measures can be considered a crucial aspect of the pandemic’s influence on supply chains [6,7,8].

Southeast Asia has emerged as an important player in global supply chains over the past few decades, where Vietnam is among the countries becoming major manufacturing hubs. The region is now an important producer of automobiles, computers, electronics and apparel, among other products, for the world. However, the massive production disruption caused by the current Covid pandemic threatens to cause a shift in value chains [9]. In particular, the automotive industry was hardest hit by supply chain disruptions during the pandemic. The crisis spurred a wave of production cuts at auto suppliers, resulting in assembly plant closures extending outside the area, posing a slew of issues for automakers. Procurement is crucial in the automotive industry since many components are assembled, and a company cannot make all those components. There are also numerous sellers for each component, with fierce rivalry. The frequent introduction of new models and shorter product lifecycles, along with quick order fulfillment, demand a high level of agility and flexibility on suppliers, compounding supply chain complexity even more. With the increasing complexity of the supply chain, selecting a sustainable supplier becomes an arduous task yet vital strategic decision [10,11,12,13,14].

In recent years, there has been a growing awareness of sustainability trends in emerging economies such as Vietnam, one of the Southeast Asian countries most distinguished by inefficient technologies and unsustainable production of goods and services, which are revealed in high pollution rates, human and environmental hazards [15]. This has increased the demand for manufacturing enterprises to incorporate sustainability measures to meet stakeholders’ needs while minimizing negative environmental consequences. Vietnam is increasingly aware of a sustainable supply chain’s role and importance in recovering more effectively after the pandemic. However, studies on the COVID-19 pandemic’s impact on supply chain sustainability decisions such as the SSS problem are still meager [1,4,7,8,16,17,18,19,20,21], at least in the context of the automotive industry in Vietnam. Therefore, our study focused on the influence of the COVID-19 pandemic on sustainable supplier selection (SSS) in the automotive industry in Vietnam, examining the relative importance of green strategies and pandemic response methods in SSS. The present study is believed to give related businesses significant insights into achieving supply chain sustainability in the post-pandemic era and prevent perceptual reactions to any unprecedented crisis.

In order to achieve the objectives mentioned earlier, this research is focused on evaluating suppliers’ performance on the basis of sustainability triple bottom line attributes (economic, environmental, and social) and the attributes of COVID-19 pandemic response strategies into their supply chain activities. Thus, it can be concluded that the selection of a potential sustainable supplier is a complex multi-criteria decision making (MCDM) problem, in which MCDM techniques are necessary for reducing the preliminary set of suppliers to the final choices [22]. Furthermore, in real-world applications and many real-world circumstances, uncertainty is an inescapable aspect due to the fuzziness of human judgements and the imprecise nature of information. Imprecise sources include unquantifiable, incomplete, and inaccessible data, as well as partial ignorance, and experts may be unwilling or unable to give precise numerical values to comparison judgments [23]. Fuzzy sets theory [24] and grey systems theory [25] are two main approaches for introducing uncertainty and ambiguity into the assessment process in this way. When dealing with imprecision or ambiguity, crisp or conventional procedures are less effective, but fuzzy sets theory and grey systems theory provide an appropriate paradigm for assessing systems with imprecise data and successfully managing uncertainty. As a result, in this study, we used an integrated MCDM framework that combines spherical fuzzy Analytical Hierarchical Process (SF-AHP) with grey Complex Proportional Assessment (G-COPRAS) to rank and select the best potential supplier. The assessment criteria system is initially identified by a literature research and expert recommendations. The criteria weights are then determined using the SF-AHP. Finally, the G-COPRAS approach is used to choose the best suppliers.

There have been astronomically increasing discussions in supplier selection studies related to the enhancement of supplier capabilities towards green and sustainable practices. A range of studies on SSS suggested by various researchers is reviewed in this section, in which numerous MCDM techniques are presented. Using an integrated MCDM approach combining AHP and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods, Luthra et al. [26] created a scientific model that provides comprehensive information on supplier selection for sustainability based on the essential variables, including product quality, pricing, environmental costs, occupational health and safety systems, and environmental skills. Awasthi et al. [27] employed the fuzzy AHP-VIKOR framework for the extended global sustainable supplier selection towards sustainability risks under five sustainability criteria (economic, quality, environment, social, and global risk). Proposing a hybrid MCDM model of AHP and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods, Jain et al. [28] solved a supplier selection problem in a case study in the Indian automotive industry, considering eight criteria: quality, price/cost, environmental performance relationship, warranty, delivery time, manufacturing capability and brand name. Gupta et al. [29] also considered a case study in the automobile sector supplier selection based on green parameters such as resource consumption, environmental training for employees, quality, service level, eco-design, green image, environmental management system, price/cost, and pollution control. Along with AHP and TOPSIS, the authors used some new MCDM methods, namely MABAC (Multi-Attributive Border Approximation Area Comparison), WASPAS (Weighted Aggregated Sum-Product Assessment). Memari et al. [30] presented an intuitionistic fuzzy TOPSIS method for SSS that concerns nine criteria (cost, quality, service, green image, green competencies, safety and health, environmental efficiency, pollution reduction, and employment practices), as well as thirty sub-criteria for an automobile spare parts manufacturing. Hendiani et al. [11] used the fuzzy best-worst method (BWM) to prioritize the supplier based on their performance of sustainable development under 20 criteria of social, economic, and environment.

Since the COVID-19 crisis, some SSS studies have included pandemic response strategies in their research. For example, Orji and Ojadi [3] examined the COVID-19 pandemic’s impact on SSS in the Nigerian manufacturing sector based on fuzzy set theory, AHP and MULTIMOORA (Multi-Objective Optimization based on Ratio Analysis with full multiplicative form). According to the authors, the most important factors in SSS implementation in the post-pandemic era were quality, affordability, personal protective equipment usage, and information technology use for consumer demand forecast. Wang and Chen [31] proposed a bi-objective AHP–mixed integer nonlinear programming (MINLP)–genetic algorithm (GA) approach for supplier selection amid the COVID-19 pandemic according to five following criteria: level of buyer–supplier cooperation, delivery speed, company reputation, pandemic severity, and pandemic containment performance. Petrudi et al. [32] evaluated suppliers considering social sustainability innovation factors during the COVID-19 disaster with the BWM method and grey relational analysis (GRA). During COVID-19, the authors recommended paying attention to safety and health practices, distant working circumstances, and localization while selecting suppliers. In addition to the above-mentioned MCDM techniques used in supplier selection problem, there are many other effective and novel methods that have been widely applied in multiple industries, such as SWOT analysis [33], Analytic Network Process (ANP) [34], Evaluation Based on Distance from Average Solution (EDAS) [35], Decision Making Trial and Evaluation Laboratory (DEMATEL) [36], COmbined COmpromise SOlution (CoCoSo) [37,38], Multi-Attribute Utility Theory (MAUT) [39], Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) [40], Simple Weighted Sum Product (WISP) [41], Simultaneous Evaluation of Criteria and Alternatives (SECA) [42], to name a few.