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Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This article presents a methodological application of decision support with the purpose of identifying and better aligning sustainable banking strategies. Those strategies are based on best practices declared by employees and conducted during efficient periods affecting sustainable production, the health quality of clients, the organization’s profitability and social impact on the local community across different sectors.

The approach involves a two-phase process: first, it employs directional data envelopment analysis (DEA) to benchmark knowledge based on employee opinions gathered through interviews to evaluate strategies related to banking services; then, using the best-worst method and ELECTRE outranking incorporating elements of fuzzy set theory based on an experienced decision-maker’s input, sustainable banking strategies are ranked according the different perspectives for leveraging outputs from the first step.

The outcomes yield a ranking of strategies, emphasizing the crucial role of technology in banking services while highlighting the need for more agile services to ensure customer satisfaction. This underscores the necessity of aligning with the market perspective, as fintech companies are reshaping the socio-technological-environmental landscape of financial services.

The research combined DEA and multicriteria analysis in the context of the banking sector, providing a comprehensive and analytically robust approach translated as a decision-making framework for promoting sustainability by aligning operational efficiency and social responsibility. These tools can guide banks in adopting more sustainable practices that benefit the institution, society and the environment.

Decisions in the banking sector encompass a wide array of concepts, from internal technical factors to customer feedback on service processes and offerings. The proposed approach considers decision analysis in complex environments, and the application developed in this study considered not only internal banking activity-oriented concepts but also the preferences of human agents developing them and the managerial perspective focused on issues involving components associated with sustainability.

By integrating DEA with multicriteria analysis, this study paves the way for a more efficient, environmentally conscious and socially responsible management scenario in the Brazilian banking sector. This research assesses operational efficiency and offers a comprehensive framework for selecting and implementing sustainable practices in the banking sector.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.