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The purpose of this paper is to report a study on the application of interpretative structural modelling (ISM) integrated with multi-criteria decision-making (MCDM) techniques for enabling the sustainability supplier selection.

In this paper, two approaches of hybrid MCDM methods are followed and the selection of sustainable supplier was based on the comparative results obtained from both the methods. The first hybrid approach is ISM – analytic network process (ANP) – ELimination and Et Choice Translating REality (ELECTRE II) and the second hybrid approach is ISM – ANP – Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). ISM was used to identify the inter relationship between the criteria. Inter-relationship of criteria obtained from ISM will serve as an input for ANP. The weights obtained from ANP will be used in ELECTRE II and VIKOR. ELECTRE II is an outranking method, whereas VIKOR is a compromise ranking method; comparison of both the methods was carried out in this study.

In this study, two modules ISM–ANP – ELECTRE and ISM–ANP – VIKOR were compared for the problem of sustainable supplier selection. ELECTRE results with a single solution showed that Supplier 2 can be selected as the best supplier; VIKOR result shows that Supplier 1 and Supplier 2 can be selected as the best suppliers.

The selection of sustainable supplier considering the interrelationship of criteria using ISM and ranking the alternatives using compromise and outranking techniques was found to be original and novel contribution of the author.

The purpose of this paper is to prioritize and establish relationships among the barriers that affect green, lean and Six Sigma (GLSS) implementation in the Indian construction sector.

A hierarchal model consisting of several levels is generated by the interpretive structural modelling (ISM) methodology. For establishing the priority weights and the ranking of the barriers, the relationships among barriers from the model in ISM are used to provide an output from the analytic network process (ANP). The 12 vital barriers that affect implementation of GLSS adoption were shortlisted from literature and then finalized in consultation with experts belonging from both industry and academia.

Based on the ISM model “Lack of awareness for green products, Lack of top management commitment and involvement as well as Lack of funds along with an improper estimation” are at the highest level. Similar results were found while ranking the barriers through ISM–ANP integration.

This study identified and prioritized the barriers that affect GLSS implementation using ISM–ANP approach, such a study has not been attempted previously for the construction sector. The ISM model and ANP ranks are based on the inputs gathered from experts and academicians so as to ensure practical validity. This approach is assists decision-makers to focus on the key barriers priority basis and enables them to implement GLSS smoothly.

This paper aims to discuss the concept of leanness and provide an effective assessment tool for measuring leanness of small and medium enterprises (SMEs).

A hybrid interpretive structural modelling–analytical network process (ISM-ANP) approach is used to develop leanness index for SMEs. The parameters for leanness are extracted from the literature survey, and the inter-relationships among them are identified through ISM approach. Further, the ANP tool is used to derive the weights of the parameters, and the leanness index is developed for SMEs.

A leanness assessment model is developed, which considers the interdependencies among leanness parameters. Continuous improvement, Just in Time and active management participation, respectively, get first, second and third ranks for leanness measurement in SMEs.

This study is based on the expert’s opinion, and it may tend to be biased. However, future study will be performed as empirical research to catch more explicit concept and more insights of leanness in context of SME sector.

This paper provides guidelines to the managers of SMEs for measuring the leanness index and planning for future. This leanness index gives information regarding the degree of lean adoption in the organization.

The proposed model has been developed by the expert opinion of academicians and practitioners. The proposed model can provide guidelines and directions for managers for leanness assessment in SME context.

In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.

This study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.

The study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.

This study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.

The literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.

The purpose of this research is to first explore various third party logistic service provider supply chain enablers. Thereafter the interrelationship amongst the various supply chain enablers has been studied using ISM Methodology. Despite the complex relationships third party logistic service providers (3PLs) share with their clients or firms, they often attract a demand owing to the flexibility and competitive edge they provide to their client firms in adapting to the rapidly changing market conditions, focusing on their core competencies and developing long-term growth strategies for them. Choosing and evaluating the right third-party logistic service provider is an important responsibility for logistic managers. This largely depends on selecting appropriate 3PLs supply chain enablers that assess the 3PLs on different fronts.

This paper presents an ISM approach for studying the interrelationships between various 3PLs enablers and accordingly constructing a hierarchical structure of them.

The results suggest that delivery, service reliability and risk and uncertainty factors have the highest importance.

Selection of the 3PLs is a critical issue when they are required to be selected by the company at the global level. This often requires doing a comparative study for both domestic as well as global service providers. Choosing appropriate supply chain enablers as the basis for selection of 3PLs therefore will serve as a research topic to be further explored both by researchers as well as company managers. Further studying the inter-relationships amongst various supply chain enablers will provide basis to managers to justify their choice.

The novelty of the research lies in the application of methodology to the case of third-party logistic service provider selection

Re-use of products in the supply chain has become a significant consideration in the last decade. It has resulted in the development of several product recovery alternatives. Remanufacturing in the supply chain is one such product recovery option that yields social, economic and environmental benefits. This study aims is to identify and evaluate the key performance indicators (KPIs) of the remanufacturing supply chain (RSC).

The KPIs of RSC are classified along with the five primary management processes (plan, source, make, deliver and return) of the supply chain operations reference (SCOR) model. A grey decision-making trial and evaluation laboratory (DEMATEL) technique is applied to investigate the complex interrelationships amongst the identified KPIs and categorize them into cause and effect group. The applicability of the proposed framework is demonstrated through a case organization involved in remanufacturing business.

The KPIs are identified based on literature analysis and subsequent discussion with decision panel experts. The present research work results reveal that “consumer awareness program”, “technological compatibility” and skilled workforce' are the most influential indicators.

This research work provides a framework to evaluate the causal relationship between the RSC KPIs. The framework proposed in this study is empirically applied to a case organization. Based on the study findings some important recommendations are presented to the decision-makers/policy planners to help them develop an action plan. This would help the case organization reduce resource consumption, increase market share and enable sustainable development.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

The already-strained vaccine supply chain (VSC) of the expanded program for immunization (EPI) require a more robust and structured distribution network for pandemic/outbreak vaccination due to huge volume demand and time constraint. In this paper, a lean-agile-green (LAG) practices approach is proposed to improve the operational, economic and environmental efficiency of the VSC.

A fuzzy decision framework of importance performance analysis (IPA)–analytical hierarchy process (AHP)–technique for order for preference by similarity in ideal solution (TOPSIS) has been presented in this paper to prioritize the LAG practices on the basis of the influence on performance indicators. Sensitivity analysis is carried out to check the robustness of the presented model.

The derived result indicates that sustainable packaging, coordination among supply chain stakeholders and cold chain technology improvement are among the top practices affecting most of the performance parameters of VSC. The sensitivity analysis reveals that the priority of practices is highly dependent on the weightage of performance indicators.

This study's finding will help policymakers reframe strategies for sustainable VSC (SVSC) by including new management practices that can handle regular immunization programs as well as emergency mass vaccination.

To the best of the authors' knowledge, this is the first study that proposes the LAG framework for SVSC. The IPA–Fuzzy AHP (FAHP)–Fuzyy TOPSIS (FTOPSIS) is also a novel combination in decision-making.

Today, proficient practices are required to stimulate along various boundaries of the supply chain (SC) to exploit manufacturing resources economically, effectually and gracefully for retaining operational excellence. Accordingly, varieties of paramount practices, i.e. Lean, Agile, Resilient and Green practices, are integrated in present study with the objective to develop a Decision Support Framework (DSF) to select robust supplier under the extent of Lean-Agile-Resilient-Green (LARG) practices for a manufacturing firm. The framework is developed and validated in the Indian automotive sector, where the primary data is collected based on perceptions of the respondents working in an automotive company.

LARG metrics can ponder ecological balance, customer satisfaction, associations, effectiveness and sustainability and thus, the study consolidated LARG practices in one umbrella to develop a DSF. The analytical approach under DSF is developed by the integration AHP, DEMATEL, ANP, Extended MOORA and SAW techniques in present study to evaluate a robust supplier under the aegis of LARG practices in SC. DSF is developed by scrutinizing and categorizing LARG characteristics, where the selected LARG characteristics are handled by fuzzy sets theory to deal with the impreciseness and uncertainty in decision making.

The study has identified 63 measures (15 for Lean, 15 for Agile, 14 for resilient and 19 for Green) to support the robust supplier selection process for manufacturing firms. The findings of study explicate “Internal communication agility”, “Interchangeability to personnel resources”, “Manufacturing flexibility”, “degree of online solution”, “Quickness to resource up-gradation”, “Manageability to demand and supply change”, “Overstocking inventory practices” as significant metrics in ranking order. Additionally, “Transparency to share information”, “Internal communication agility”, “Manufacturing Flexibility”, “Green product (outgoing)” are found as influential metrics under LARG practices respectively.

A technical DSF to utilize by the managers is developed, which is connected with knowledge-based theory and a case of an automobile manufacturing firm is presented to illustrate its implementation. The companies can utilize presented DSF to impose service excellence, societal performance, agility and green surroundings in SC for achieving sustainable outcomes to be welcomed by the legislations, society and rivals. The framework represents an important decision support tool to enable managers to overcome imprecise SC information sources.

The study presented a proficient platform to review the most significant LARG alternative in the SC. The study suggested a cluster of LARG metrics to support operational improvement in manufacturing firms for shifting gear toward sustainable SC practices. The present study embraces its existence in enrolling a high extent of collaboration amongst clients, project teams and LARG practices to virtually eradicate the likelihood of absolute project failure.

The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.

Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.

This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.

It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.

Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.

This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.