A SUSTAINABLE SUPPLIER SELECTION IN GREEN SUPPLY CHAIN FRAMEWORK USING FUZZY TOPSIS METHOD

Vietnam’s garment industry is facing many challenges, including domestic competition and the global market. The free trade agreement, which Vietnam signed, includes environmental barriers, sustainable development, and green development. The agreement further requires businesses to make efforts to improve not only product quality but also the production process. In cases when enterprises cause environmental pollution in the production process and do not apply solutions to reduce waste, save energy, and natural resources, there is a risk of no longer receiving orders or orders being rejected, especially orders from the world’s major branded garment companies. In this research, the authors propose a multicriteria decision-making model (MCDM) for optimizing the supplier evaluation and selection process for the garment industry using sustainability considerations. In the first stage of this research, all criteria affecting supplier selection are determined by a triple bottom line (TBL) model (economic, environmental, and social aspects) and literature reviews; in addition, the fuzzy analytic hierarchy process (FAHP) method was utilized to identify the weight of all criteria in the second stage. The technique for order preference by similarity to an ideal solution (TOPSIS) is a multicriteria decision analysis method, which is used for ranking potential suppliers in the final stage. As a result, decision-making unit 10 (DMU/10) is found to be the best supplier for the garment industry. The contribution of this research includes modeling the supplier selection decision problem based on the TBL concept. The proposed model also addresses different complex problems in supplier selection, is a flexible design model for considering the evaluation criteria, and is applicable to supplier selection in other industries.

Purpose – This article aims to propose criteria for the selection of green suppliers in the foundry sector and select the best suppliers for the organization where researched, by applying the AHP method.Design/methodology/approach – An applied survey was carried out where specialists from the foundry sector were consulted and after the survey had been completed, criteria were defined to select green suppliers.Findings – Adopting the defined criteria and applying the Analytical Hierarchy Process (AHP) decision-making method, five suppliers were classified, with Environment, Health and Safety criteria being considered the most important according to the opinion of the experts consulted in this survey.Originality/Value – The study allows organizations to see the importance of the supplier selection process in the foundry sector, where worldwide concerns for the environment are increasingly present and environmental standards and regulations are strict.Keywords – Green supply chain. Supplier selection. AHP method.

Sustainable supplier selection is a key strategic problem in supply chain management. The aim of this research is to provide a new hybrid multicriteria model for evaluating technology suppliers and validate it with a case study in the banking sector. This approach allows companies to perform qualification, selection, ranking and sorting of suppliers on a sustainable basis. Integration of several techniques is necessary to address this complex decision problem with conflicting economic, environmental and social criteria. Analytic hierarchy process (AHP) is useful for problem structuring and weighting criteria collaboratively. Multi-attribute utility theory (MAUT) is applied to obtain indicators for product quality and supplier risks, whose utility functions are derived by data-driven models that favour evaluation objectivity and transparency. Preference ranking organisation method for enrichment evaluation (PROMETHEE) is suitable for supplier selection due to its discriminant power among alternatives. Finally, FlowSort is proposed to classify suppliers into ordered groups and the outcomes are compared with results from MAUT. Results show its applicability by increasing process transparency and reducing operational risks in practice.

Today, business organizations are facing increasing pressure from a variety of sources to operate using sustainable processes. Thus, most companies need to focus on their supply chains to enhance sustainability to meet customer demands and comply with environmental legislation. To achieve these goals, companies must focus on criteria that include CO2 (carbon footprint) and toxic emissions, energy use and efficiency, wastage generations, and worker health and safety. As in other industries, the food processing industry requires large inputs of resources, which results in several negative environmental effects; thus, decision-makers have to evaluate qualitative and quantitative factors. This work identifies the best supplier for edible oil production in the small and medium enterprise (SME) food processing industry in Vietnam. This study also processes a hybrid multicriteria decision-making (MCDM) model using a fuzzy analytical hierarchy process (FAHP) and green data envelopment analysis (GDEA) model to identify the weight of all criteria of a supplier’s selection process based on opinions from company procurement experts. Subsequently, GDEA is applied to rank all potential supplier lists. The primary objective of this work is to present a novel approach which integrates FAHP and DEA for supplier selection and also consider the green issue in edible oil production in uncertain environments. The aim of this research is also to provide a useful guideline for supplier selection based on qualitative and quantitative factors to improve the efficiency of supplier selection in the food industry and other industries. The results reveal that Decision-Making Unit 1 (DMU 1), DMU 3, DMU 7, and DMU 9 are identified as extremely efficient for five DEA models, which are the optimal suppliers for edible oil production. The contributions of this research include a proposed MCDM model using a hybrid FAHP and GDEA model for supplier selection in the SME food processing industry under a fuzzy environment conditions in Vietnam. This research also is part of an evolution of a new hybrid model that is flexible and practical for decision-makers. In addition, the research also provides a useful guideline in supplier selection in the food processing industry and a guideline for supplier selection in other industries.

Sustainability plays a significant role in promoting competence and collaboration in supply chain management due to increased environmental awareness, tightened regulations, and government policies. The evaluation of sustainable suppliers and selecting the best one is indispensable for companies to promote sustainability. Due to multi-criteria nature of the supplier selection process, it has been considered as a multi-criteria decision making (MCDM) problem in many studies. In this study, a state-of-the-art MCDM method for sustainable supplier selection is developed by integrating AHP and TOPSIS techniques within the Pythagorean Fuzzy Sets (PFSs) linguistic setting. A Group Decision Making (GDM) environment is utilized due to superiority of group consensus over individual decisions. Finally, an apparel industry example is used to illustrate the effectiveness and feasibility of the proposed sustainable supplier selection method. A comparison with existing techniques and sensitivity analysis are done to verify and validate the given outcome.

This research was conducted in a case study of a rubber processing factory involving several parts, namely gardens, processing plants, and final processing. In the last 2 years, the company has started with the implementation of green supply chain management program included the waste treatment system and recycle of solid and liquid waste. Therefore it was necessary to evaluate and measure the performance of the green supply chain to find out how well the performance of the company's supply chain. The purpose of this study was to measure the performance of the green supply chain in the processing of Ribbed Smoke Sheet (RSS) by using the application of Green Supply Chain Operation Reference (SCOR) method. This study employed the model Green Supply Chain Operations Reference (Green SCOR) and Analytical Hierarchy Process (AHP) method. Data collection was reached through interview and questionnaire techniques to the experts. The Green SCOR model was implemented to design business processes and measure performance using the Key Performance Indicator (KPI). The AHP method was required to reach weight and rank to each indicators used in this study. This study employed 24 KPIs to measure the performance of the company's supply chain. Performance value of green supply chain in the company has a value of 72.03% which was classified into good level. Another low score indicators was used as a basic to develop proposed recommendations for company’s green supply chain improvement program

The multi-criteria decision making (MCDM) problem in supplier selection involves a number of qualitative and quantitative attributes which are required to be analysed for evaluating the performance of the suppliers or at the supplier selection stage. This paper presents an integrated methodology for evaluating the supplier selection using analytical hierarchy process (AHP) and linear programming problem (LPP) model. This integrated approach is a two stage process. The first stage identifies the functional weightage scores for the selected suppliers using AHP and the second stage uses LPP, for making an optimal allocation of units to the selected suppliers. The paper shows the application of this methodology for the supplier selection situation, while purchasing the liquefied petroleum gas (LPG) and compressed natural gas (CNG) kits by the automobile manufacturing companies. The selection criteria used in the study includes quantity, on-time delivery product, quality, service, responsiveness, technical capability, production facility and pricing structure.

PurposeWith increasing pressure from the government and private sectors to be more environmentally and socially responsible, sustainable supplier selection has gained enormous currency in recent times. Particularly, in the case of the construction industry, owing to a large amount of industrial wastage generated and extreme workplace conditions, it is even more important to devise strategies to mitigate the harmful consequences. The most crucial step in this regard is the selection of sustainable suppliers that acquire a pivotal position in the supply chain ecosystem. This study aims to identify indicators for three criteria such as economic, environmental and social, and prioritize them according to their level of significance for sustainable supplier selection in the Indian construction industry.Design/methodology/approachIn this study, the best-worst method (BWM) is presented for sustainable supplier selection in Indian construction organizations. Total of 27 indicators was identified for the three criteria of the triple bottom line (TBL) approach namely economic, environmental and social. Using BWM, the most important criterion was found and subsequently, all the indicators under each criterion were ranked in order of importance.FindingsThe analysis revealed that the environmental criterion was the most important criteria in the sustainable supplier selection followed by the economic criteria. The indicators that were the most influential in the effective selection process were “Usage of recyclable raw materials for production”, “Adoption of clean and green technologies”, “Waste management” and “Periodical environmental audits” under the environmental criteria; “Efficiency”, “Cost” and “Flexibility and Scalability” from the economic criteria; and “Safety programs” and “Information disclosure” in the social criteria.Research limitations/implicationsThe study provides a reference framework for the selection of sustainable suppliers in construction organizations. The findings can also be used for the assessment of suppliers' performance in the supply chains.Originality/valueThe novelty of this work lies in its attempt to model the performance of suppliers in the Indian construction supply chains.

Selecting suitable suppliers is a critical decision for organizations, and multi-criteria group decision-making (MCGDM) has been proven as an effective technique in selecting the most appropriate supplier. However, most existing supplier selection methods do not fully explore the trust relationships among decision-makers (DMs), fail to effectively tackle complex and uncertain information, and neglect the interdependencies among criteria. To overcome these obstacles, we develop a social trust network (STN) based hybrid supplier selection approach that adopts the best-worst method (BWM) and the preference ranking organization method for enrichment evaluation (PROMETHEE) under interval-valued Pythagorean fuzzy (IVPF) environment. The main contributions are as follows: Firstly, a novel STN is constructed and extended to the IVPF setting to fully capture both direct and indirect trust relationships among DMs. Secondly, a new trust propagation operator is designed to determine DMs’ weights more objectively. Thirdly, the BWM method, known for its high consistency and efficiency, is adapted to the IVPF environment to derive optimal criterion weights and the preference relation-based PROMETHEE method is also integrated to rank the candidate suppliers. Fourthly, the IVPF weighted Hamy mean (IVPFWHM) operator is employed to aggregate DMs’ preferences and evaluations while explicitly capturing the interrelationships among criteria. The approach is practically applied to a real-world decarbonized and sustainable supplier selection case to validate the efficacy in complex and uncertain group decision-making scenarios while incorporating social trust dynamics. The sensitivity and comparison analyses are also conducted to validate the robustness and benefits of the method.

The journal retracts the article entitled “A Fuzzy Multicriteria Decision-Making (MCDM) Model for Sustainable Supplier Evaluation and Selection Based on Triple Bottom Line Approaches in the Garment Industry” [...]

Supplier selection is a difficult and important issue in sustainable supply chain management. This research proposes a managerial framework based on Industry 4.0, a plan for evaluating and choosing sustainable suppliers to implement circular economy practices. Green supplier selection (GSS), the circular economy, and Industry 4.0 have become hot topics in recent operations management discussions. Three main categories (e.g., economic, environmental, and social) and 16 subcategories related to supplier selection decisions were identified using a hybrid approach combining literature reviews and industry expert opinions. In the fuzzy environment of Pythagorean, this paper proposes comprehensive techniques for the selection of green suppliers based on entropy, stepwise weighted assessment ratio analysis (SWARA), and complex proportional assessment (COPRAS) methods. To calculate the standard weight, this technique first merges the objective weight found by the entropy method and the subjective weight found by the SWARA method. The findings show that access to finance and financial availability for implementing Industry 4.0 within the circular economy (ECO5) and R&D in environmental issues using Industry 4.0 technologies (ENV7), Information technology (IT) facilities (ECO6), and Product cost/price (ECO1) showed highest ranking among sub-criteria. Moreover, Supplier 5 was listed as the best sustainable supplier when they started making such a decision. The results of the proposed method help decision-makers make effective and efficient sustainable supplier selection.

With the growing social and environmental concerns, the integration of sustainability in supplier selection and order allocation is of paramount importance. This research presents a holistic multi-phase decision-support framework to solve the sustainable supplier selection and order allocation problem for the multi-echelon supply chain. The framework comprises a multi-objective mixed-integer nonlinear programming mathematical model augmented with fuzzy multi-criteria decision making techniques and forecast demand. The various economic, environmental and social objectives were optimized for a multi-modal transportation network of a multi-echelon supply chain. The results of the mathematical model highlighted the impact of multi-modal transportation on the total cost and total travel time. The results also demonstrated the relationship between the multi-modal transport network and the environmental impact of the supply chain. The proposed multi-phase holistic decision support framework can be used in the comprehensive sustainability-based analysis of supply chains.

Engineering equipment is essential part in the construction project and usually manufactured with long lead times, large costs and special engineering requirements. Construction manager targets that equipment to be delivered in the site need date with the right quantity, appropriate cost and required quality, and this entails an efficient supplier can satisfy these targets. Selection of engineering equipment supplier is a crucial managerial process .it requires evaluation of multiple suppliers according to multiple criteria. This process is usually performed manually and based on just limited evaluation criteria, so better alternatives may be neglected. Three stages of survey comprised number of public and private companies in Iraqi construction sector were employed to identify main criteria and sub criteria for supplier selection and their priorities.The main criteria identified were quality of product, commercial aspect, delivery, reputation and position, and system quality . An effective technique in multiple criteria decision making (MCDM) as analytical hierarchy process (AHP) have been used to get importance weights of criteria based on experts judgment. Thereafter, a management software system for Evaluation and Selection of Engineering Equipment Suppliers (ESEES) has been developed based on the results obtained from AHP. This model was validated in a case study at municipality of Baghdad involved actual cases of selection pumps suppliers for infrastructure projects .According to experts, this model can improve the current process followed in the supplier selection and aid decision makers to adopt better choices in the domain of selection engineering equipment suppliers.

Supplier selection is crucial decision for buying companies to develop sustainable supply chain strategy. Not only first-tier suppliers but also lower-tier suppliers would influence the buyer’s sustainability performance. Thus, buying companies need to consider extending their sustainable supply chain plan to multitier suppliers, and develop multitier sustainable supply chain management (MT-SSCM). Sustainable supplier selection (SSS), as an important SSCM practice, can effectively enhance the sustainable competitiveness of supply chains. However, there are few theoretical studies on SSS under MT-SSCM, which affects the decision-making efficiency of organisations. In general, there are three multitier supply chain (MTSC) structures: open MTSC, closed MTSC and transitional MTSC structures. Because some traditional decision models cannot deal with a variety of structures at the same time, this study proposes a hybrid model that integrates fuzzy set theory, stepwise weight assessment ratio analysis and a Bayesian network to evaluate the critical SSS criteria in three MTSC structures. Finally, a case of a Chinese buying company is used to verify the flexibility and applicability of the proposed model, and some suggestions are provided for the practitioners to select sustainable suppliers in different MTSC structures.

Sustainable supplier selection has become a critical factor for organizations striving to align their supply chains with environmental, economic, and social sustainability goals. The increasing need for sustainable practices in procurement has prompted the development of frameworks that address these dimensions effectively. This study develops a comprehensive framework for evaluating sustainable suppliers in the textile industry, integrating Triple Bottom Line (TBL) principles with SWARA and TOPSIS methods. Initially, 20 sustainability criteria were identified through a rigorous review of the literature and expert consultations. These criteria were then narrowed down to 15 key criteria based on expert evaluations using a 5-point Likert scale. The study found that the most important economic criteria were chemical quality (0.0929), price (0.0830), and delivery reliability (0.0798), while sustainability concerns were highlighted by environmental management systems (0.0798) and employee health and safety (0.0775). These findings reflect the increasing emphasis on both operational efficiency and environmental and social responsibility in supplier selection. Using TOPSIS, the suppliers were ranked based on their performance across these 15 criteria. The results revealed Supplier A (0.7555) as the most sustainable, followed by Supplier B (0.7337), while Supplier E (0.2573) ranked last due to weaker sustainability performance. These rankings highlight the significance of sustainability practices, especially in environmental and social dimensions, which are increasingly critical in the procurement decision-making process. The study provides valuable insights for procurement managers, particularly in emerging economies like Bangladesh, where SMEs dominate the textile supply chain. By using this SWARA-TOPSIS framework, companies can select suppliers that align with sustainability goals, improving both environmental performance and social responsibility. This research contributes to the field of sustainable supply chain management by offering a structured, transparent, and objective method for supplier evaluation.