Sustainable supplier selection by using spherical fuzzy AHP

Nguyen

et al. 2022

Axioms

Sustainable supplier selection (SSS) is gaining popularity as a practical method to supply chain sustainability among academics and practitioners. However, in addition to balancing economic, social, and environmental factors, the emergence of the COVID-19 pandemic has affected the selection of long-term suppliers to ensure sustainable supply chains, recover better from the pandemic and effectively respond to any future unprecedented crises. The purpose of this study is to assess and choose a possible supplier based on their capability to adapt to the COVID-19 epidemic in a sustainable manner. For this assessment, a framework based on multi-criteria decision making (MCDM) is provided that integrates spherical fuzzy Analytical Hierarchical Process (SF-AHP) and grey Complex Proportional Assessment (G-COPRAS), in which spherical fuzzy sets and grey numbers are used to express the ambiguous linguistic evaluation statements of experts. In the first stage, the evaluation criteria system is identified through a literature review and experts’ opinions. The SF-AHP is then used to determine the criteria weights. Finally, the G-COPRAS method is utilized to select sustainable suppliers. A case study in the automotive industry in Vietnam is presented to demonstrate the proposed approach’s effectiveness. From the SF-AHP findings, “quality”, “use of personal protective equipment”, “cost/price”, “safety and health practices and wellbeing of suppliers”, and “economic recovery programs” have been ranked as the five most important criteria. From G-COPRAS analysis, THACO Parts (Supplier 02) is the best supplier. A sensitivity study was also conducted to verify the robustness of the proposed model, in which the priority rankings of the best suppliers are very similar. For long-term development and increased competitiveness, industrial businesses must stress the integration of response mechanisms during SSS implementation in the COVID-19 epidemic, according to the findings. This will result in significant cost and resource savings, as well as reduced environmental consequences and a long-term supply chain, independent of the crisis.

Section: Literature Review On Proposed Methodologymentioning

confidence: 99%

A Two-Stage Multi-Criteria Supplier Selection Model for Sustainable Automotive Supply Chain under Uncertainty

Nguyen

et al. 2022

Axioms

“…The usefulness of fuzzy AHP approaches has been demonstrated, and researchers and practitioners are becoming more interested. These approaches have been used on many extensions of fuzzy set theory that are dependent on the determination of linguistic assertions, such as traditional fuzzy sets [8,27,42,49,55,56], type-2 fuzzy sets [57][58][59], interval-valued fuzzy sets [60], intuitionistic fuzzy sets [7], neutrosophic sets [61], Pythagorean fuzzy sets (PSF) [62], and spherical fuzzy sets [15][16][17]. The spherical fuzzy set (SFS) is a novel set introduced in 2018 by Kutlu Gündo gdu and Kahraman [10,11,63,64].…”

Section: Research Gapsmentioning

confidence: 99%

“…This is the first attempt to propose an integrated SF-AHP and CoCoSo approach to identify and prioritize the SSS evaluation criteria and to select the most efficient supplier from a set of alternatives, for sustainability in the supply chain. Both methods are novel techniques that have proved to be effective in various studies in recent times, in applications such as renewable energy [10], location selection [11][12][13], technology selection [14], supplier selection [15][16][17][18], and management issues [19][20][21][22]. In the present research, the proposed approach is employed in a real case study, to evaluate the performance of five suppliers in terms of achieving social sustainability goals with the aid of experts' inputs.…”

Section: Introductionmentioning

confidence: 99%

Integrating Triple Bottom Line in Sustainable Chemical Supplier Selection: A Compromise Decision-Making-Based Spherical Fuzzy Approach

Wang

Chou²,

et al. 2022

Processes

As a consequence of increased awareness of environmental preservation and the associated rigorous regulations, the adoption of sustainable practices has become a crucial element for corporate organizations in regard to their supply chains. In the chemical industry, which is characterized by high risks, high pollution, and high efficiency, these characteristics can help businesses analyze their long-term development and sustainability. The goal of this research is to analyze and choose possible suppliers based on their sustainability performance in the chemical sector. A methodology based on multi-criteria decision making (MCDM) is proposed for this evaluation, using spherical fuzzy analytical hierarchy process (SF-AHP) and combined compromise solution (CoCoSo) methods, in which the novel spherical fuzzy sets theory is employed to present the ambiguous linguistic preferences of experts. In the first stage, an evaluation criteria system is identified through literature review and experts’ opinions. The SF-AHP is used to determine the criteria weights, while the CoCoSo method is utilized to select the right sustainable supplier. A case study in the chemical industry in Vietnam is presented to demonstrate the effectiveness of the proposed approach. From the SF-AHP findings, “equipment system and technology capability”, “flexibility and reliability”, “logistics cost”, “green materials and technologies”, and “on-time delivery” were ranked as the five most important criteria. From the CoCoSo analysis, Vietnam National Chemical Group (CHE-05) was found to be the best supplier. A sensitivity study and a comparison analysis of methods were also conducted to verify the robustness of the proposed model, and the priority rankings of the best suppliers were very similar. To the best of our knowledge, this is the first study that has proposed SF-AHP and CoCoSo to prioritize SSS evaluation criteria and determine the best alternatives. The suggested method and findings can be used to make well-informed decisions that help businesses to achieve supply chain sustainability, capture opportunities, and maintain competitiveness through reconfiguring resources. The method could be useful for case studies in other countries and for other sustainability problems.

“…SFS improves decision-making over its predecessors by making it more intelligent (comparable to human judgment), which can lead to a decision-making process that is more accurate when evaluating options. Because of this benefit, SFS has lately been used in a variety of applications, including location selection [43,44], renewable energy evaluation [45], supplier selection [46][47][48][49], systems management [50][51][52][53][54][55][56], technology evaluation [57], and strategies selection [58].…”

Section: Nomentioning

confidence: 99%

Evaluating Global Container Shipping Companies: A Novel Approach to Investigating Both Qualitative and Quantitative Criteria for Sustainable Development

Wang

Nguyen

et al. 2022

Axioms

The COVID-19 pandemic has implications for the container shipping industry and global supply chains. Measuring the efficiency of major international container shipping companies (CSCs) is an important issue that helps them make strategic decisions to improve performance, especially in the context that all businesses and governments are adapting to build back better the post-pandemic world. This paper develops a new integrated approach using both a qualitative assessment tool and a performance assessment tool as a systematic and flexible framework for evaluating the container shipping industry. This new methodology is implemented in two phases to consider both qualitative and quantitative criteria for assessing the performance of CSCs based on efficiency. In the first phase, qualitative performance evaluation is performed using spherical fuzzy analytical hierarchical process (AHP-SF) to find criteria weights and then the grey complex proportional assessment methodology (COPRAS-G) is used to find the ranking of CSCs. Qualitative variables are converted into a quantitative variable for use in the data envelopment analysis (DEA) model as an output called an output variable called expert-based qualitative performance (EQP). Then, DEA is performed to identify efficient and inefficient CSCs with the EQP variable and other quantitative parameters (i.e., capacity, lifting, expenses, revenue, and CO2 emissions). The efficiency of 14 major global CSCs is empirically evaluated, and the scores for CSCs’ efficiency in all dimensions are measured and examined. The results show that the average cargo efficiency of the CSCs is lower than their eco-efficiency performance, revealing the operational disruption caused by the pandemic. Moreover, by identifying efficient and inefficient CSCs, our findings provide practical implications for decision-makers in the maritime field and assist in modifying applicable policies and strategies to achieve sustainable performance.