The apparel and textile industry are known as a key sector in the structure of many economies around the world. In particular, the influence of foreign outsourcing manufacturers on textile supply chains has been recognized for decades. The outsourcing manufacturers are multi-criteria selected and changed by supply chain managers from time to time in search of the most efficient state for the entire supply chain. This is a known concern with the community and there is large interest in studying the apparel and textile outsourcing manufacturer problems. Aiming at reinforcing the selection methods, this study develops a three-layer fuzzy multiple criteria decision-making approach that leverages the strengths from the original methods. In turn through the layers, the hierarchy and weights of criteria and sub-criteria, which includes sustainability factors, are determined by the fuzzy analytic hierarchy process (FAHP) method. Next, the results from the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) process determine the outsourcing manufacturer’s performance via expert linguistics judgments. Then, data envelopment analysis (DEA) models are applied for the purpose of evaluating the outsourcing manufacturer’s overall performance along with other quantitative effectiveness. This approach is applied to the problem of selecting the apparel and textile outsourcing manufacturers in Vietnam, one of the places that makes the necessity of this problem grow. The third position in the world apparel and textile export ranking, as well as the trend of shifting labor-intensive production systems to Southeast Asia make the necessity of Vietnam outsourcing manufacturer selection problem grow. The results of this study also classified manufacturers into groups as a support for selection decisions. Analysis of quantitative uncertainties using simulation tools and forecasting techniques can strengthen the solutions in future related studies.

Geothermal potential is a significant advantage in terms of renewable energy for countries located on the Pacific Ring of Fire. Studies on geothermal energy sources show that Malaysia is one of the countries possessing this inexhaustible and stable energy source. This on-site energy source is a promising solution to the problem of energy security during emergencies where the energy supply chain is disrupted. To exploit this advantage, this study proposes a novel tri-layer framework to prioritize locations for direct-use geothermal energy location (DGL) in Malaysia. First, the proposed locations are screened for feasibility to limit the impact on other natural conservation areas and stable residential areas. In the second layer, locations are evaluated for efficiency using the data envelopment analysis (DEA) model based on quantitative indicators. In the third layer, the spherical fuzzy extended combination of the analytic hierarchy process (SF AHP) and the combined compromise solution (SF CoCoSo) methods are introduced and applied to prioritize high-efficiency locations. According to the findings, costs, social acceptance, and noise impacts are the qualitative criteria of most concern for DGLs. Through the tri-layer framework, the suggested concordant locations for DGLs in Malaysia are Marudi of Sarawak, Tawau of Sabah, Serian of Sarawak, and Jeram of Selangor.

The delays and disruptions during the pandemic have awakened interest in the sustainability and resilience of production systems to emergencies. In that context, the deployment of smart technologies has emerged as an almost mandatory development orientation to ensure the stability of manufacturing. The core value of smart technologies is to reduce the dependence on human labor in production systems. Thereby, the negative impacts caused by emergency situations are mitigated. However, the implementation of smart technologies in a specific production system that already exists requires a high degree of suitability. Motivated by this fact, this study proposes an integrated spherical fuzzy bounded rationality decision-making approach, which is composite of the spherical fuzzy decision-making trial and evaluation laboratory (SF DEMATEL) and the spherical fuzzy regret theory-based combined compromise solution (R-SF CoCoSo) method. The proposed approach reflects both the ambiguities and psychological behaviors of decision-makers in prioritization problems. It was applied to prioritize seven smart technologies for manufacturing in Vietnam. The results show that reliability, costs, and maturity are the most important criteria for choosing smart technology which is suitable for an existing production system in Vietnam. Our findings seem to suggest that the automatic inspection, remote machine operation, and robots are the most suitable smart technologies to stabilize and sustain production in Vietnam for emergency situations.

After the pandemic, global supply chains will be in the process of restructuring. The relocation of production lines among countries is being considered for the purpose of sustainable development. The problem of determining the most suitable destination for manufacturers’ investments will become important, especially in the field of manufacturing high-tech products, which involves many complicated factors such as technological maturity, support policies, political issues, and technology security. In that context, Southeast Asia is seen as one of the regions attracting multinational manufacturers. To address this problem, a novel composited regret-theory-based spherical fuzzy prioritization approach is proposed. On the one hand, the super-efficiency slack-based model (super-SBM) of data envelopment analysis (DEA) is applied to evaluate efficiency, based on indicators. On the other hand, the novel spherical fuzzy regret-theory-based decision-making approach (SfRDMA) is developed and introduced to determine effectiveness, based on criteria. Then, the efficiency and the effectiveness of countries are combined by a composite-score function that is based on a geometric mean and an arithmetic mean. The findings imply that government policy, political stability, and human resources availability are the three most important criteria. Moreover, India, Thailand, Vietnam, Malaysia, and Indonesia are identified as promising destinations for the world’s high-tech production lines.