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Purpose – The purpose of this paper is to examine the status of green supply chain management (GSCM) research in terms of how the field is represented along a number of dimensions including journal, year, country, university, publishing house, authors, research design, research methods, data analysis techniques, multi criteria decision-making methods, research topics/issues and major industries actively involved. Design/methodology/approach – A range of online databases from 1998 to August 2013 were searched containing the word “green supply chain” in their title and in the phrases to provide a comprehensive listing of journal articles on GSCM. Based on this a total of 177 articles were found and the information on a series of variables was gathered. Each of these articles was further reviewed and classified. The review and classification process was independently verified. All papers were allocated to the main and sub-categories based on the major focus. Findings – The major findings shows that survey research holds greater credibility and the trend in survey research is moving from exploratory to model building and testing. GSCM research related to organizational practices, environmental issues, process, performance and sustainability were found to be most widely published topics within the GSCM domain. Research limitations/implications – This paper is limited in reviewing those articles which contains the word “green supply chain” in the title and the phrases of the articles. Originality/value – The present review will provide increased understanding of the current state of research and what still needs to be investigated in the GSCM discipline.

The need to integrate environmental and social aspects into the supply chain has created a debate amongst academicians and researchers on two topics: sustainability and the supply chain. A large number of journals and special volumes are publishing research that carries out a case study or survey, develops a mathematical model or builds a conceptual model/framework on sustainable supply chain management (SSCM). However, classified analysis of articles that develop a framework on SSCM has not been carried out by any researcher. The study thus aims to review SSCM framework articles to identify the major contributions and gaps. A total of 92 framework articles on SSCM from the sample of 349 over a period of 19 years (1998–2016) are structurally analyzed to meet the objectives. The selected literature is categorized by novel or adapted framework, source of framework, framework verification, method used for framework verification and identifying the constructs/elements that build the structural framework. Analyzing the literature sample using these categories reveals that framework development in the context of SSCM is still an area with ample future opportunities. The findings of the study are especially interesting for academicians and practitioners to investigate the critical gaps in the field of sustainable supply chain management frameworks (SSCMFs). Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

PurposeThe purpose of this paper is to identify the supply chain management enablers (SCMEs) and establish relationships among them using interpretive structural modeling (ISM) and find out driving and dependence power of enablers, using fuzzy MICMAC (Matriced' Impacts Croisés Multiplication Appliquée á un Classement) analysis.Design/methodology/approachA group of experts from industries and academics was consulted and ISM is used to develop the contextual relationship among various SCMEs for each dimension of SCM implementation. The results of ISM are used as an input to fuzzy MICMAC analysis, to identify the driving and dependence power of SCMEs.FindingsThis paper has identified 24 key SCMEs and developed an integrated model using ISM and the fuzzy MICMAC approach, which is helpful to identify and classify the important SCMEs and reveal the direct and indirect effects of each SCME on the SCM implementation. The integrated approach is developed, since the ISM model provides only binary relationship among SCMEs, while fuzzy MICMAC analysis provides precise analysis related to driving and dependence power of SCMEs.Research limitations/implicationsThe weightage for ISM model development and fuzzy MICMAC are obtained through the judgment of academicians and a few industry experts. It is only subjective judgment and any biasing by the person who is judging the SCMEs might influence the final result. A questionnaire survey can be conducted to catch the insight on these SCMEs from more organizations.Practical implicationsThis study has strong practical implications, for both practitioners as well as academicians. The practitioners need to concentrate on identified SCMEs more cautiously during SCM implementation in their organizations and the top management could formulate strategy for implementing these enablers obtained through ISM and fuzzy MICMAC analysis.Originality/valueThis is first kind of study to identify 24 SCMEs and further, to deploy ISM and fuzzy MICMAC to identify and classify the key SCMEs that influence SCM implementation in the organization.

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