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In a quest to meet increasing pressure to incorporate environmental and sustainability factors due to the legislations and growing public awareness, companies are rethinking of strategizing their supply chain network to take control of the reverse flow of products as well. This growing interest has also led to tremendous growth in publications occurring in several reputed journals in the last few years. In this context, the purpose of this article is to perform a systematic literature survey of recent and state-of-the-art papers in order to draw insights and highlight the future directions.

This paper has selected and reviewed 204 papers published from the year 2002–2017. The papers were collected from the web of science and Google scholar database which have the DOI number. The selected papers were then categorized into main five core areas of RSC management namely reverse supply chain (RSC), Reverse Logistics (RL), Remanufacturing, Closed-Loop Supply Chain (CLSC) and Product Recovery Systems (PRS) and then analyzed with great detail. Finally, the research gaps that were identified from the study have been highlighted for future research opportunities.

This paper would serve as a roadmap to the managers who wish to align their forward and reverse supply chains for overall growth and development. It provides an in-depth knowledge on RSC to the researchers working in this domain. The scholars would be able to identify the areas of RSC which have been already addressed and the areas which remain unaddressed.

This paper presents a systematic literature survey of state-of-the-art papers that was published in the reputed journal in the area of RSC. Total 204 numbers of papers which were published in the reputed journals between 2002 and 2017 are reviewed, categorized and analysed to draw the opportunities and future research direction in the area of RSC.

The Indian micro small and medium scale enterprises (MSMEs) are potential suppliers to the original equipment manufacturers (OEM). The implementation of sustainable supply chain practices (SSCPs) enhances the chances of developing a long-term partnership with these OEMs.

A hybrid framework of analytical network process (ANP) and fuzzy logic (FL) is developed for implementing SSCPs in Indian MSMEs. This model has been validated through a case study.

The study has identified several critical success factors (CSFs) for the implementation of SSCPs in MSMEs. This study has observed that Government regulation is the most important CSF for the implementation of SSCP in Indian MSMEs followed by management support and policy framework.

The article presents a mechanism, i.e. an adaptability test that enables the OEM decision-makers to assess the suitability of an MSME for a long-term partnership.

Enterprises face the wrath of the government for taking part in environmental conservation and adoption of sustainable initiatives along with customer demands. Therefore, enterprises are forced to adopt sustainable supply chain practices (SSCPs), which leads to competitive advantage. Now, sustainable supply chain management (SSCM) is a management process that promotes the adoption of eco-friendly activities in conventional supply chains (SCs). Enterprises in India are under tremendous pressure to include SSCPs into their conventional SCs. The goal of this paper is to evaluate the barriers for the implementation of SSCPs into Indian Micro, Small and Medium Enterprises (MSMEs).

This study aims to identify critical barriers for adoption of SSCPs in the textile MSME SCs located in Eastern India, Odisha with the help of interpretive structural modeling (ISM).

The paper develops a framework for the evaluation of barriers to the adoption of SSCP in the textile SC. This paper also provides appropriate suggestive measures to deal with the barriers and overcome the same to attain a sustainable textile SC.

Opportunities exist for extension of this research on wider geographical area. In addition to this, some other quantitative modeling approaches can be applied, like analytical hierarchy process, to prioritize the barriers.

The framework offers help to SC managers in their decision-making process by enabling them to analyze the barriers and ways to overcome them.

The paper deals with a particular geographical area where such kinds of studies are rare. The proposed framework provides a foundation for further research.

The business enterprises are increasingly focusing on buying and supplying of products and services in a manner to reduce the adverse impacts on the environment, society, and economy. In view of the above, the concept of sustainable supply chain management (SSCM) has received attention of the industry and academia due to its importance on environmental, social and corporate responsibility through economic performance. The paper aims to discuss these issues.

The structured literature review attempts to map the various theories in the SSCM literature from the perspectives of economic performance, environmental dimensions, and social values and ethics.

As supply management is vital for enhancing organizational competitiveness, the present work attempts to investigate the theoretical perspectives in SSCM to develop an understanding of the current research activities and future potentials.

This work aims to gain a number of valid insights for the practitioners and the researchers. It also focuses on the perspectives of governance mechanisms for successful implementation SSCM practices in the business enterprises.

As the theory building initiatives with implications on the conceptualization of SSCM is limited in literature, this work has also been able to identify the trends and relevant research gaps to define the potential areas for future research.

This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP).

Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators.

The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders.

Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries.

The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.

The manufacturing sector is witnessing the need to continuously improve overall performance by eliminating inefficiencies in the supply chain. The adoption of lean concepts to address wasteful or non-value-adding activities in the supply chain is crucial. This article determines key factors of lean supply chain management (LSCM) for continuous improvement in the manufacturing sector.

The methodology comprises three steps. The first step identifies critical factors of LSCM in manufacturing from prior research and a series of expert consultations. Critical factors are identified and validated that industries can leverage to attain their lean goals. The second step uses the decision-making and trial evaluation laboratory (DEMATEL) method to determine the causal relationship among the factors. DEMATEL analysis categorizes factors into cause and effect, which will assist industry personnel in decision-making. The third step involves further data analysis to visualize the importance of the most critical factors. It develops a machine learning (ML) model in the form of a decision tree that helps in assessing the factors into cause or effect groups via a threshold value of expert ratings.

IT tools, JIT manufacturing and material handling and logistics form the most critical factors for LSCM implementation.

The analysis from DEMATEL and ML together will be beneficial for manufacturing practitioners to improve the supply chain performance based on the identified factors and their criticality towards LSCM implementation.