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One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers' performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion.

Due to significant changes in supply chain environments and the importance of environmental and economic issues, various supply chain paradigms have been developed to address different challenges. As supplier evaluation and selection is one of the critical issues in supply chain management, in this paper a data-driven model is developed for this purpose. Considering the significance of different components in the case study of the home appliance industry, the leagile, resilience, circular economy, and Industry 4.0 paradigms are simultaneously considered for the first time in supplier evaluation. The key evaluation indicators in this study are recycled product, financial ability, waste management and delivery speed. The methodology used in this paper involves the use of data-driven stochastic model. In this regard, a stochastic VIKOR method has been developed based on scenarios, which improves evaluation effectiveness by considering different scenarios. Additionally, a neural network algorithm with a learning rate optimized using a genetic algorithm has been used to evaluate supplier performance. The findings demonstrate that the developed algorithm surpasses other algorithms, achieving an accuracy rate of 98 percent, and is effective for predicting supplier performance.

The supplier selection problem (SSP) is known as one of the major issues in the supply chain management area. In this field, the literature shows that the combination of green and agile indicators has been ignored by researchers. Hence, this research attempts to study the SSP considering green and agile aspects, simultaneously. To do this, an efficient hybrid fuzzy decision-making approach is developed based on the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL), Fuzzy Best-Worst Method (FBWM), Fuzzy Analytic Network Process (FANP), and Fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje (FVIKOR) methods. Then, to show the efficiency and application of the proposed approach, a case study in the medical devices industry is investigated. After determining the main indicators and alternatives, the interrelationships between indicators are identified employing FDEMATEL. Then, the weights of indicators are calculated using integrated FBWM-FANP. Finally, the potential suppliers are ranked applying FVIKOR. Based on the obtained results, price and greenness are the more important aspects and also material costs, environmental performance evaluation, manufacture flexibility, service level, and system reliability are the most important criteria for the green-agile supplier selection problem in the medical devices industry. Since all of the consistency ratios are less than 0.1, the reliability of the results is proved. On the other side, the results of conducting sensitivity analysis show that by changing the defuzzification methods, there is no significant change in the obtained results that demonstrates the validity of the proposed approach. Eventually, based on the obtained results, suppliers #1 and #5 are the best suppliers for the considered company.

The supplier selection problem is one of the most important competitive challenges used by modern enterprises. Due to the fact that companies have to improve their practices in the management of a green supply chain that aims to respect green practices and technologies to increase sustainability, selecting the optimal green supplier is a challenging multi-dimensional problem. While most of the research has focused on the development and improvement of new methods, relatively limited attention has been paid to the identifying sets of criteria and capturing the domain knowledge. This issue is significant because a correctly identified set of criteria plays a vital role in supporting the decision-making process. The approach presented creates a gap between classical assumption of decision making and knowledge-based problem structuring. The current paper presents a methodological and practical background for capturing and handling knowledge about green supplier selection criteria, supported by a formal guideline for their appropriate acquisition. In addressing this research challenge, the aims of this paper are twofold: providing meta-analysis to reveal a collection of key data supported by a formal and trustworthy bibliometric analysis, and capturing knowledge in one place in the form of ontology for enabling selection and evaluation criteria of green suppliers is proposed. The proposed ontology is available for public use.

The rapid development of digital technologies – such as IoT, AI, blockchain, and digital twins – has transformed supply chains into interconnected ecosystems, making digital supplier selection both critical and complex. For the first time, this study proposes a novel multi-criteria decision-making (MCDM) method, Multiple Normalization Rating Analysis (MUNRA), for ranking alternatives. It integrates linear, vector, and non-linear normalization to improve robustness, reduce rank reversal, and enhance decision accuracy. A case study of digital supplier selection in the textile industry is considered for a real-life application of the method. Results highlight technology integration, flexibility, and technological capability as the most influential criteria for selecting digital suppliers. Moreover, the final ranking of the six digital suppliers is as follows: DS5, DS4, DS2, DS6, DS1, and DS3. Validation through comparative MCDM methods, Spearman correlation, and sensitivity analyses confirms the credibility of the method. It is also shown that it is free from the rank reversal phenomenon. The research presents a computationally efficient and rigorous method for evaluating digital suppliers, offering strategic insights for digital supply chain management. The application of MUNRA to a larger decision-making problem further illustrates its scalability and cross-domain applicability.

Today, improvement and development must occur in all parts of the supply chain, and supplier selection as the starting point of the chain must be considered one of the most critical parts of planning. In the current competitive and unstable environment, in selecting the best suppliers, common approaches such as resilience and sustainability can be helpful for organizations in terms of sustainability and competitive advantage. Therefore, designing a sustainable and resilient supply chain model and considering all these parameters can effectively provide products and services. Also, to consider real-world uncertainties, interval type-2 trapezoidal fuzzy sets (IT2TFSs) are used because these sets are more powerful than classical fuzzy sets and better reflect uncertainties. Then, a new version of the MULTIMOORA model called MULTIMAMOORA is extended. In this model, the importance of the criteria is specified using the best–worst method. The importance of experts is defined using a new version of the MABAC approach developed by the average concept. Then, the ranking of suppliers is determined by the developed MULTIMAMOORA method. Notably, the proposed approach is developed in the IT2TF environment to consider the uncertainties of real issues and the ambiguity caused by experts’ opinions in the decision-making process. Eventually, a case study is solved utilizing the proposed approach, and the best supplier is determined under resilient and sustainable paradigms.

The choice of suppliers of material and technical resources is one of the most important decisions in procurement marketing. Effective decisions on the selection of suppliers are the basis for creating a database of sources of supply. The aim of the study is to analyse approaches to the selection of suppliers of material and technical resources in procurement marketing and substantiate the directions of increasing its efficiency under conditions of uncertainty.In order to make a balanced selection of suppliers, the authors propose an algorithm for organising procurement activities and criteria for evaluating potential suppliers, substantiating the use of a multi-criteria approach that allows a balanced approach to the selection of a supplier. The main criteria and scale for evaluating suppliers of material resources for the SE \"Arena Lviv\" are determined, and the results of their expert evaluation are presented.The algorithm for organising procurement activities and the multi-criteria approach to selecting suppliers, developed by the authors, can be recommended for enterprises and organisations engaged in procurement activities.

Resilience is a topic that has recently emerged concerning the basics of the construction project supply chain and we can consider it as a response to disruption in the supply chain of the project. Disruption also is an unavoidable reality in today’s complex and dynamic construction supply chain, the occurrence of which can cause irretrievable damages to the system, such as financial losses. Successful companies seek to minimize disruption and maintain adequate supply chain performance before disruption occurs, rather than looking for costly and challenging post-disruption solutions. This paper covers this gap by proposing a scenario-based mixed integer-programming model aiming to minimize logistics costs and delays, while scheduling projects to address selecting the appropriate supplier at risk of disruption. So far, this quantitative view was not presented in discussions about disruptions in the project supply chain, therefore different scenarios are applied in the process to validate the model. To improve its resilience level, this model benefits from back-up suppliers’ strategy. This study focuses on providing the required materials for the project site in an emergency without incurring additional costs using a back-up supplier. Results reveal the model’s suitability in confronting the unavailability of a supplier due to disruption.

Building on Carter and Jennings' (2002a,b, 2004) seminal works on socially responsible purchasing and logistics, this multinational study investigates the extent to which socially responsible supplier selection (SRSS) is associated with customer firms’ financial performance in three key world economic regions. We collect and utilize a unique dataset consisting of a total of 479 manufacturing, retail, and service provider firms operating in three distinct national cultures: China, the United Arab Emirates, and the United States of America. Based on an exploratory empirical analysis, we observe evidence that, overall, firms that consider social responsibility aspects during the supplier selection process enjoy financial performance advantages versus rivals. However, model comparisons across the studied countries reveal differential outcomes of SRSS by region. Our findings aid supply chain managers by linking SRSS to commonly expected outcomes within these important national settings.

Purpose: The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value measure (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multiobjective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future. Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality. Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.