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The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed. [ABSTRACT FROM AUTHOR]

Objective: Today, there is increasing competition among various organizations, and one of the competitive tools is supply chain management. Existing pressures and disruptions for companies have led to working under uncertain conditions. Therefore, the supply chain must be made resilient. Such a chain is of great importance in the oil industry, which is one of the country's most essential activities. Accordingly, this study seeks to identify and prioritize the factors affecting a resilient supply chain in the oil industry. Methodology: This research was conducted in one of the active companies in the oil industry. Initially, semi-structured interviews were conducted with 10 experts from the company, who were purposefully selected, and analyzed using the thematic analysis technique. Findings: This analysis identified 13 factors. In the next step, using the Best- Worst Method (BWM), these factors were ranked, with financial factors, flexibility, the use of experienced managers, the use of backup suppliers, and foresight being ranked first to fifth, respectively. Conclusion: Overall, it is recommended to focus on these underlying factors as the foundation of supply chain resilience. [ABSTRACT FROM AUTHOR]

With consideration for the extensive resources consumption and environmental degradation being on the rise today, implementing green development strategy to pursue both socioeconomic growth and the coordinated of environment sustainability, has become an increasingly important issue in modern enterprise supply chain operations management. Hence, the appropriate green supplier selection (GSS), viewed as a core issue in green supply chain management (GSCM), requires continuous research in this field to obtain a complete perception on GSS practices. It can be regarded as a multi-attribute group decision-making (MAGDM) problem that involves many conflict and unmeasurable evaluation criteria. In view of the superiority of multi-granularity extended probabilistic linguistic term sets (MGEPLTSs) in modeling such issues on potential ambiguity, complexity and uncertainty in actual GSS practices, we propose a novel integrated MAGDM methodology for GSS problems, by integrating the BWM (best–worst method) with the VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) technique under the MGEPLTSs environment. First, by introducing the multi-granularity and probabilistic linguistic term sets, the MGEPLTSs are proposed to represent and quantify the decision information of GSCM practitioners. Then, the BWM is introduced to the MGEPLTSs environment to compute the weights of decision-making panels and evaluation attributes in GSS problems, by building the fuzzy mathematical programming model, respectively. Finally, we extend a multi-granularity extended probabilistic linguistic VIKOR method to calculate the compromise measure of alternatives considering the group utility maximization and the individual regret minimization, thereby achieving the full ranking of alternatives. A GSS case is conducted to illustrate the feasibility of the proposed approach, and the sensitivity analysis and comparative analysis with other similar approaches are presented to demonstrate its effectiveness and advantages. [ABSTRACT FROM AUTHOR]

During COVID-19 pandemic, the logistics sector has witnessed supply chain disruptions all across the globe. Digitalisation and sustainable solutions have become the priority for the logistics companies to deal with this crisis situation. Managing logistic operations for sustainability has become a major challenge. Therefore, the main objective of the study is to identify the critical success factors (CSFs) for achieving circular economy goals through emerging technologies during this pandemic. The most important factors, which will impact sustainability goals through the use of technologies are extracted from the literature review and then finalised after discussion with supply chain professionals. The importance rating of critical success factors for achieving CE goals is found using Best-Worst Method (BWM). The novelty of this study lies in exploring applications of emerging technologies for achieving circular economy goals in logistics sectors during COVID 19 pandemic. These factors are further prioritised from strategy perspectives. The prioritisation of CSFs will help logistics companies in developing strategies for digitalisation and achieving sustainable goals during the uncertain business environment. It will also contribute to formulating effective policies for long-term business continuity through technological and sustainable achievement. [ABSTRACT FROM AUTHOR]

This study establishes a valid set of circular supply chain management enablers in Vietnam under uncertainties. Circular supply chain management optimises resource utilisation and minimises waste in achieving competitive advantage; however, prior studies failed to build enablers for industrial improvement under qualitative information. This study adopts the fuzzy Delphi method to arrive at a valid group of attributes. Fuzzy set theory deals with the uncertainties from qualitative data, and fuzzy interpretive structural modelling builds the hierarchical structure under uncertainties. The best-worst method confirms the consistencies. This study stipulates a four-level hierarchical structure in the seafood processing industry. The results show that organisational capability and agility, technological advancement and economic benefit are dominant aspects. Commitment of leadership and managerial support, suppliers' collaboration, firm culture, readiness for organisational change and sustainability concept integration are top criteria for industrial improvement. [ABSTRACT FROM AUTHOR]

Supplier selection process plays a vital role in supply chain management and is the most important variable in its success. With increasing environmental considerations, organizations must consider sustainability considerations and economic goals to protect the environment. Furthermore, the destructive effects of disruptions on the supply chain performance of companies have prompted organizational experts to pay special attention to the concept of resilience. This study developed an integrated approach based on the extended version of Multi-Criteria Decision-Making (MCDM) methods in a spherical fuzzy (SFS) environment to address sustainable and resilient IoT supplier selection. In the proposed approach, the main criteria (i.e., resilience, and sustainability) have been used in the supplier selection process. Then, these criteria are weighted using the developed SFS-Best-Worst Method (BWM), which reduces uncertainty in pairwise comparisons. In the next step, the 14 selected IoT suppliers are evaluated and prioritized by applying SFS-mulTi-noRmalization mUlti-Distance aSsessmenT (TRUST) that considers a multi-normalization algorithm to reduce subjectivity in normalized data. The results of this study shows that the pollution control and risk-taking sub-criteria are placed in the first and second priorities, respectively. The comparison of the results of the SFS-TRUST with other MCDM methods and sensitivity analysis demonstrates the performance of the proposed approach and its ranking stability in various scenarios. [ABSTRACT FROM AUTHOR]

This paper pioneers the investigation of the significant factors that influence corporate decisions on the use of social media for supply chain social sustainability, and it highlights a crucial research area that is currently understudied in supply chain management literature. A theoretical framework was developed in this study based upon the Technology–Organization–Environment (TOE) and Human–Organization–Technology (HOT) theories to obtain the significant critical success factors (CSFs) which influence the use of social media for supply chain social sustainability in freight logistics firms in Nigeria. The Best-Worst Method was applied to analyse and rank the CSFs using their determined relative importance level. The research findings indicate that customer satisfaction, sufficient security and privacy, affordability and competitive pressure are the highest ranked CSFs to achieve supply chain social sustainability using of social media. This research has important implications for policy makers and practitioners to gain perspectives on how to foster the use of social media in the freight logistics sector for supply chain social sustainability. [ABSTRACT FROM AUTHOR]

Robust and resilient agri-food supply chain management (AFSCM) is paramount to agribusinesses, given the many challenges and risks that this increased demand will bring in the coming decades. Interruptions caused by various risks to this crucial supply chain network, particularly in emerging economies, can put the lives of millions in danger, not to mention creating devastating impacts on the economy and the environment. Even so, there are only a limited number of quantitative risk management studies in the AFSCM literature. In this study, an integrated modified risk mitigation matrix (M-RMM) is developed to analyze the mitigation strategies for dealing with various risks in the context of the agri-food supply chain. The M-RMM is integrated with the grey multi-objective binary linear programming (GMOBLP) model to obtain the optimal risk mitigation strategies related to the three objective functions of risk, cost, and time minimization. The proposed model is a useful tool for formulating sustainable business policies and reducing food waste, and acquiring a context-specific (i.e., a developing economy), sector-specific (i.e., the agri-food processing sector), and multi-product (i.e., fresh and non-perishable) approach. The findings reveal that continuous training and development and vulnerability analysis of IT systems are the most effective risk mitigation strategies to lessen the impacts of lack of skilled personnel, sub-standard leadership, failure in IT systems, insufficient capacity to produce quality products, and poor customer relationships. The findings assist practitioners in managing risks in supply chains.

Sustainability is hinged on innovation. The importance of sustainable innovation management in sustainable supply chain management (SSCM) cannot be underestimated. Studies on SSCM have emphasised the need for sustainable innovation in achieving sustainability but none provide deep insights into sustainable innovation management in SSCM implementation. This lack of research depth stimulates this study to identify and investigate criteria for sustainable supply chain management innovation advancement. This paper proposes a sustainable innovation criteria framework for investigating sustainable supply chains in manufacturing companies. To exemplify the applicability and efficiency of the proposed framework, a sample of five Indian manufacturing companies are used to evaluate and prioritise the sustainable innovation management criteria, using the 'best–worst' multi-criteria decision-making (BW-MCDM) model. The criteria weights for all companies from BWM are aggregated, averaged and used for ranking. The respondent managers viewed 'financial availability for innovation' as the most important sustainable innovation sub-criteria. The results of the study will inform industrial managers, practitioners and decision-makers on which criteria to focus on during the implementation stage, to increase sustainability in manufacturing supply chains, and further advance corporate and supply chain sustainable development. The framework may also serve as a theoretical construct for a future empirical study on sustainable supply chain innovation in the manufacturing sector. This paper sets the stage for further research in sustainable innovation practices in the manufacturing sector and its supply chains. [ABSTRACT FROM AUTHOR]

In view of increasing supply chain disruption events, for example the China–United States trade war, the COVID-19 pandemic, and the Russia–Ukraine war, the complexity and dynamicity of global freight management keeps increasing. To build a resilient and sustainable supply chain, industrial practitioners are eager to systematically revamp the freight management decision process related to the selection of carriers, shipping lanes, and third-party logistics service providers. Therefore, this study aims at strengthening decision-making capabilities for global freight management, in which an industrial blockchain-based global freight decision framework (IB-GFDF) is proposed to incorporate consortium blockchain technology with the Bayesian best-worst method. Through the blockchain technology, pairwise comparisons can be conducted over the international freight network in a decentralized and immutable manner, and thus, a secure and commonly agreed-on pairwise comparison dataset is acquired. Subsequently, the pairwise comparison dataset with multi-stakeholder opinions is analyzed using the Bayesian best-worst method in order to prioritize the selection decision criteria related to carriers, shipping lanes, and 3PL service providers for global freight management. To verify the methodological feasibility, a case study of an Australian agricultural supply chain firm was conducted to support the development end-to-end (E2E) supply chain solutions originated from Australia. It was found that port infrastructure, ports of call and communication effectiveness were the major criteria for the selection decision, which can be emphasized in future global freight collaboration. In addition, an immutable and append-only record of pairwise comparisons can be established to support the visibility of time-varying stakeholders' preferences. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has severely impacted the global social sustainability of the supply chains, pushing them towards a more flexible management approach. However, there is a paucity of literature that focuses on social sustainability issues for emerging economies. In the post-COVID-19 period, firms around the world will face several critical challenges to social sustainability, which will hinder achieving sustainable development goals (SDGs). Against this backdrop, this study identifies the pressing challenges to social sustainability in the post-COVID-19 context by a literature review and opinions from an expert panel, focusing on the footwear supply chain. In this paper, the best–worst method is applied to compute the criticality of social sustainability challenges towards the flexibility of the supply chains. The study findings reveal that among the nine identified critical challenges, "high level of lay off", "health protocol development", "complexity in ensuring workplace safety", "facing trouble in mental health", and "lack of government enforcement and regulations for social issues" are reported as the top five challenges, respectively. Furthermore, this study suggests several flexible managerial guidelines, which will help practitioners and policymakers to achieve SDGs considering the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background: This research aims to prioritize the blood supply hub for coping barriers of implementation blockchain (BC) in supply chain management (SCM). Nowadays, blood supply is a crucial matter that plays an essential role in people’s lives. Hence, tracing the supply of blood is very substantial. One of the ways for the SCM of blood supply is a blockchain system. It shows how all traces of the SCM stream can flow from raw material to receiving blood into end users. However, there are many barriers to the implementation of blockchain. None of the companies can design improvement projects to resolve these barriers because of a lack of rare resources, such as human resources (HR), budget, information, etc. Methods: Barriers are first extracted from previous studies and interviews with experts in this study. Then, these barriers are customized for this case study by the Delphi method. Then, these blood supply hubs are ranked by measurement alternatives and ranking according to the compromise solution (MARCOS) method. Since this method needs primary weight, the best–worst method (BWM) is applied to obtain this weight. Result: Results have pointed out that business owners’ unwillingness was the highest priority among the nine barriers. Conclusions: Additionally, implementing blockchain for SCM of blood supply requires paying more attention to business owners’ unwillingness barriers and resolving them. Furthermore, hub 4 is faced with many problems in tackling barriers to implementing SCM blockchain.

Adopting green practices in supply chains contributes towards protecting the environment. For the successful adoption of environmentally sustainable supply chain management (ESSCM), the dimensions related to technology, strategy, policy and so forth, termed as hard dimensions in the scholarly literature, play a significant role. However, the independent influence of these dimensions on ESSCM has not been previously studied. Therefore, the present study aims to fill this gap and to evaluate the effectiveness of these dimensions. To do this, the most significant dimensions are identified through a thorough literature review and experts' inputs. To determine their priority and cause–effect relationship, a hybrid approach of best–worst method (BWM) and decision‐making trial and evaluation laboratory (DEMATEL) is used. The analysis indicates that 'total quality management' and 'technologies for cleaner production' are the most important causal dimensions, and it provides several insights to the decision makers to formulate robust business strategies to the adoption of ESSCM. [ABSTRACT FROM AUTHOR]

Reviewing and integrating decisions in the supply chain and project management is crucial. This problem is known as a project-driven supply chain problem. By the growing dependency of project-driven companies on suppliers since they prefer to concentrate on their core and subcontracting processes, the project-based organization's dependence on suppliers has increased. A project-driven supply chain (PDSC) focuses on integrating project management activities into the supply chain. In this paper, a new model is presented for the integration of supply chain and project management decisions. Firstly, a new method is proposed to determine the resilience score of suppliers. This new model consists of three parts: the weighting of criteria, the weighting of experts, and the ranking of suppliers. To give weight to the experts, a new version of the combinative distance-based assessment (CODAS) method is presented based on the average and positive ideal concepts. Also, the importance of the criteria is specified by an expanded best-worst method (BWM). Computing the resilience score of suppliers is done through a developed CODAS method that is extended by the average and positive concepts. Furthermore, in order to consider the uncertainty of project-driven supply chain problems, interval type-2 fuzzy sets (IT2FSs) are used. Secondly, a new subtraction operation is defined to avoid producing negative in recursive critical path method (CPM) calculations in the IT2F environment. Also, the criticality score of the project's activities is defined. According to the criticality and resilience indexes of activities, suggestions are made to project management to prevent delays. Finally, a case study of hospital construction is solved to illustrate the strengths of the proposed model. • Proposing a new analytical method to evaluate project driven supply chain. • Developing a new version of CODAS method to achieve supplier resilience score. • Extending a modified version of CODAS method for determining weights of experts. • Proposing an improved CPM to avoid negative numbers and produce criticality index. • Applying interval type-2 fuzzy sets to deal with the uncertainty. [ABSTRACT FROM AUTHOR]

Due to the complexity of real-world multi-criteria decision-making (MCDM) issues, analyzing different opinions from a group of decision makers needs to ensure appropriate decision making. The group decision-making methods collect preferences of the decision makers and present the best preferences using mathematical equations. The best–worst method (BWM) is one of the recently introduced MCDM methods that requires fewer pairwise comparisons to obtain the criteria weights than the other MCDM methods. In this research, we develop a novel approach to group decision-making problems based on the BWM called G-BWM. This approach helps us to analyze the preferences of decision makers to carry out democratic decision making using the BWM structure. In order to assess the applicability of the proposed methodology and represent its novelty, two numerical examples from the literature with the application to supply chain management (SCM) (i.e., green supplier selection and supplier development/segmentation) are examined and discussed. The results demonstrate the performance of our proposed G-BWM for group decision making in terms of a large number of decision makers, ease of use and achieving democratic decisions in the decision-making process.

Abstract The concept of green supply chain management emerged as a response to increasing public awareness of environmental protection in the past few decades. As companies tend to outsource a variety of their activities, green supplier selection as an imperative function of green supply chain management, has a crucial role in helping companies to maintain their strategic competitiveness. Despite the plethora of studies introducing supplier selection models based on economic criteria, studies that take into account the environmental issues are rather limited. In this study, a comprehensive grey-based green supplier selection model is proposed that incorporates both economic and environmental criteria. A novel weight assignment model is proposed by combining best-worst method and fuzzy grey cognitive maps to capture the interdependencies among the criteria. Improved grey relational analysis is advanced to be able to use grey weights of criteria to evaluate green suppliers which are subsequently ranked using an interval analysis approach. This study contributes to the decision-making theory by addressing the shortcomings of the available green supplier selection models. A real-world case study is also presented to show the applicability and effectiveness of the proposed model. The results of this study proved the proposed comprehensive model to be well capable of addressing the green supplier selection problem by taking in to account the interdependencies between criteria as well as the uncertainties associated with experts' judgments. [ABSTRACT FROM AUTHOR]

Green supplier selection has recently become one of the key strategic considerations in green supply chain management, due to regulatory requirements and market trends. It can be regarded as a multi-criteria group decision-making (MCGDM) problem, in which a set of alternatives are evaluated with respect to multiple criteria.MCGDMmethods based on Analytic Hierarchy Process (AHP) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are widely used in solving green supplier selection problems. However, the classic AHP must conduct large amounts of pairwise comparisons to derive a consistent result due to its complex structure. Meanwhile, the classic TOPSIS only considers one single negative idea solution in selecting suppliers, which is insufficiently cautious. In this study, an improved TOPSIS integrated with Best-Worst Method (BWM) is developed to solve MCGDM problems with intuitionistic fuzzy information in the context of green supplier selection. The BWM is investigated to derive criterion weights, and the improved TOPSIS method is proposed to obtain decision makers' weights in terms of different criteria. Moreover, the developed TOPSIS-based coefficient is used to rank alternatives. Finally, a green supplier selection problem in the agri-food industry is presented to validate the proposed approach followed by sensitivity and comparative analyses. [ABSTRACT FROM AUTHOR]

• Identified major challenges in sustainable supply chains of electric vehicle batteries. • Used hybrid approach comprising of Delphi and Best Worst Method for analysing their importance. • Ineffective recycling and reuse of batteries, insufficient charging infrastructure, dependency on high concentration on the supply chains in East Asia are found major challenges. • Findings would contribute in developing effective strategies by policy makers for sustainable growth of EVs. • Findings are validated with the help of survey data collected from 87 experts having experience on this field. Due to global warming and fast depletion of fossil fuels, the option of battery-operated electric vehicles (EVs) has emerged as one of the most popular alternatives for sustainable transport. In the present study, India is considered as a case country to explore the challenges in sustainable supply chain of electric vehicle batteries. India, being the second most populated country after China and having limited reserves of fossil fuels, has great potential to excel in electric vehicle supply chains. In addition, growth of electric vehicle markets in India is in the emerging phase. With increasing demand for EVs, the industry is facing many challenges for sustainable supply chain of electric vehicle batteries. The lithium-ion battery is a major component of electric vehicles. Many challenges for sustainability of electric vehicle battery supply chains have been extracted through literature review and discussions with industry experts. These challenges may be categorized as operational, technological, economic, environmental and social. Delphi technique is utilized to finalize major challenges for analysis. For further prioritization of these challenges, Best-Worst Method (BWM) is used. Finally, findings of the BWM are validated through an empirical study by collecting responses from 87 respondents. It is observed that ineffective recycling and reuse of batteries, disposal of batteries, and insufficient charging infrastructure are the three most important challenges in EV battery supply chain in India. The findings may be equally relevant in many developing countries having similar technological and infrastructure constraints. It will help policymakers in developing strategies for sustainable transport systems in developing countries. [Display omitted] [ABSTRACT FROM AUTHOR]

Due to the complexity of real-world multi-criteria decision-making (MCDM) issues, analyzing different opinions from a group of decision makers needs to ensure appropriate decision making. The group decision-making methods collect preferences of the decision makers and present the best preferences using mathematical equations. The best–worst method (BWM) is one of the recently introduced MCDM methods that requires fewer pairwise comparisons to obtain the criteria weights than the other MCDM methods. In this research, we develop a novel approach to group decision-making problems based on the BWM called G-BWM. This approach helps us to analyze the preferences of decision makers to carry out democratic decision making using the BWM structure. In order to assess the applicability of the proposed methodology and represent its novelty, two numerical examples from the literature with the application to supply chain management (SCM) (i.e., green supplier selection and supplier development/segmentation) are examined and discussed. The results demonstrate the performance of our proposed G-BWM for group decision making in terms of a large number of decision makers, ease of use and achieving democratic decisions in the decision-making process. [ABSTRACT FROM AUTHOR]

In the present competitive business environment and era of globalized marketing, supply chain (SC) of the leather industry is facing a variety of risks. Hence, one of the fundamental concerns in the leather industry supply chain (LISC) is recognizing and prioritizing the various risk factors for attaining sustainability. The present study is an attempt to determine a comprehensive evaluation of SC risk factors considering the case of the leather industry. Based on the literature search and interviews with the domain experts', forty-four risk factors in the context of LISC are identified. The identified risk factors are further segregated into five-dimensions to sustainability (social, environmental, economic, technical, and institutional). A Pareto analysis is performed to discover the most pertinent risk factors. Further, the best-worst method (BWM) is embraced for evaluating the importance of each pertinent risk factor for the decision-making purpose. The findings from the study reflect that 'inefficient effluent treatment', 'change in consumer preference', 'improper dumping of solid waste', 'volatility of price and cost' and 'fiscal changes' are the crucial risk factors that are required to be addressed for the successful execution of sustainable supply chain management (SSCM) practices in an emerging economy context. It is expected that the results and findings will assist the leather industry managers in decision-making for better administration and alleviation of supply chain risks to achieve sustainability. Image 1 • Forty-four risk factors in the context of Leather Industry Supply Chain (LISC) are identified. • The identified risk factors of LISC are segregated into five-dimensional approaches to sustainability. • Pareto based best-worst method is used to identify and assess the importance of the risk factors. • 'Inefficient effluent treatment' identified as the most crucial risk factor to SSCM practices in the LISC. [ABSTRACT FROM AUTHOR]