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The purpose of this paper is to identify barriers to implement green supply chain and to understand their mutual relationship. Green supply chain management (GSCM) barriers are identified using available GSCM literature and on consultations with experts from industry and academician. Interpretive structural model (ISM) was developed to identify the contextual relationship among these barriers.

A group of experts from industries and academics was consulted and ISM is used to develop the contextual relationship among various GSCMBs for each dimension of GSCM implementation. The results of ISM are used as an input to fuzzy matrix of cross-impact multiplications applied to classification (MICMAC) analysis, to identify the driving and dependence power of GSCMBs.

This paper has identified 14 key GSCMBs and developed an integrated model using ISM and the fuzzy MICMAC approach, which helps to identify and classify the important GSCMBs and reveal the direct and indirect effects of each GSCMB on the GSCM implementation. ISM model provides only binary relationship among GSCMBs, while fuzzy MICMAC analysis provides precise analysis related to driving and dependence power of GSCMB, to overcome this limitation, integrated approach is developed.

ISM model development and fuzzy MICMAC analysis were obtained through the judgment of academicians and industry experts. It is the only subjective judgment and any biasing by the person who is judging the GSCMBs might influence the final result.

This is first kind of study to identify GSCMBs and further, to deploy ISM and fuzzy MICMAC to identify and classify the key GSCMEs that influence GSCM implementation in the organization. The results will be useful for business managers to understand the GSCMBs and overcome these GSCMBs during GSCM implementation in an organization.

The purpose of this paper is to develop the relationships among the identified supply chain management barriers (SCMBs) and understand mutual influences of these SCMBs on supply chain implementation. Further, this paper seeks to identify driving and dependent SCMBs using an interpretive structural modelling (ISM) and fuzzy MICMAC (Matrix of Cross-Impact Multiplications Applied to Classification) analysis.

The methodology used in the paper is the ISM with a view to evolving mutual relationships among SCMBs. The identified SCMBs have been classified further, based on their driving and dependence power using fuzzy MICMAC analysis.

This paper has identified 15 key SCMBs which hinder the successful supply chain management (SCM) implementation in an organization and has developed the relationships among the SCMBs using the ISM methodology. Further, this paper analyses the driving and dependent SCMBs using fuzzy MICMAC analysis. The integrated approach is developed here, as the ISM model provides only binary relationship among SCMBs. The fuzzy MICMAC analysis is adopted here, as it is useful in specific analysis related to driving and the dependence power of SCMBs.

The weightage for the ISM model development and fuzzy MICMAC is obtained through the judgement of academics and industry experts. Further, validation of the model is necessary through questionnaire survey.

The identification of SCMBs, ISM model development and fuzzy MICMAC analysis provide academics and managers a macro picture of the challenges posed by the SCM implementation in an organization.

The results will be useful for business managers to understand the SCMBs and overcome these SCMBs during the SCM implementation in an organization.

The 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.

A 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.

This 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.

The 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.

This 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.

This 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.

The purpose of this study is to explore and prioritize the barriers that affect weather index-insurance (WII) adoption among customers by utilizing interpretive structural modelling (ISM) and fuzzy-MICMAC.

This paper utilized the combined approach in two phases. In first phase comprehensive literature study and expert mining method have been performed to identify and validate WII adoption barriers. In second phase, ISM has been utilized to examine the direct relationships among WII adoption barriers in order to develop a structural model. Further, fuzzy-MICMAC method has been utilized to analyse indirect relationships among barriers to explore dependence and driver power.

This study has identified 15 key barriers of WII adoption among customers and developed a structural model based on binary direct relationship using ISM. Later, the outcomes of ISM model have been utilized for analysing the dependence and driver power of each WII adoption barriers in cluster form using fuzzy-MICMAC. The customer awareness related WII adoption barrier are mainly at the top level, WII demand related barriers are in the centre and WII supply related barriers at the bottom level in ISM model.

The findings offered important insights for WII insurers to understand mutual relationships amongst WII adoption barriers and assists in developing strategy to eliminate dominant key barriers in order to enhance their customer base.

Based on best of author's knowledge this paper firstly integrates the ISM fuzzy-MICMAC method into identification and prioritization of barriers that affects WII adoption among customers.

The purpose of this paper is to identify and develop the relationships among the green supply chain management enablers (GSCMEs), to understand mutual influences of these GSCMEs on green supply chain management (GSCM) implementation, and to find out the driving and the dependence power of GSCMEs.

This paper has identified 35 GSCMEs on the basis of literature review and the opinions of experts from academia and industry. A nationwide questionnaire-based survey has been conducted to rank these identified GSCMEs. The outcomes of the survey and interpretive structural modeling (ISM) methodology have been applied to evolve mutual relationships among GSCMEs, which helps to reveal the direct and indirect effects of each GSCMEs. The results of the ISM are used as an input to the fuzzy Matriced’ Impacts Croisés Multiplication Appliquéeá un Classement (MICMAC) analysis, to identify the driving and the dependence power of GSCMEs.

Out of 35 GSCMEs 29 GSCMEs (mean⩾3.00) have been considered for analysis through a nationwide questionnaire-based survey on Indian automobile organizations. The integrated approach is developed, since the ISM model provides only binary relationship among GSCMEs, while fuzzy MICMAC analysis provides precise analysis related to driving and the dependence power of GSCMEs.

The weightage for ISM model development and fuzzy MICMAC are obtained through the judgment of few industry experts. It is the only subjective judgment and any biasing by the person who is judging might influence the final result.

The study provides important guidelines for both practitioners, as well as the academicians. The practitioners need to focus on these GSCMEs more carefully during GSCM implementation. GSCM managers may strategically plan its long-term growth to meet GSCM action plan. While the academicians may be encouraged to categorize different issues, which are significant in addressing these GSCMEs.

Arrangement of GSCMEs in a hierarchy, the categorization into the driver and dependent categories, and fuzzy MICMAC are an exclusive effort in the area of GSCM implementation.

The purpose of this paper is to present an approach to successful supplier selection process (SSP) by understanding the dynamics between SSP enablers (SSPEs), using interpretive structure modelling (ISM) methodology and find out driving and the dependence power of enablers, using fuzzy MICMAC (Matriced’ Impacts Croisés Appliquée á un Classement) analysis.

The group of experts from industries and the academics were consulted and ISM is used to develop the contextual relationship among various SSPEs for each dimension of supplier selection. The results of the ISM are used as an input to the fuzzy MICMAC analysis to identify the driving and the dependence power of SSPEs.

The research presents a hierarchy-based model and mutual relationships among SSPEs. The research shows that there is a group of SSPEs having a high driving power and low dependence, which requires maximum attention and is of strategic importance, while another group consists of those SSPEs that have high dependence and low driving power, which requires the resultant actions.

The weightage obtained for the ISM model development and fuzzy MICMAC are obtained through the judgment of academician and few industry experts. It is the only subjective judgment and any biasing by the person who is judging the SSPEs might influence the final result. A questionnaire survey can be conducted to catch the insight on these SSPEs from more organizations.

This category provides a useful tool for top management to differentiate between independent and dependent SSPEs and their mutual relationships which would help them to focus on those key SSPEs that are most significant for effective supplier selection.

Arrangement of SSPEs in a hierarchy, the categorization into the driver and dependent categories, and fuzzy MICMAC are an exclusive effort in the area of supplier selection.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

The purpose of this paper is to model the enablers of an integrated logistics. The integration is accounted for incorporating sustainability, thereby aiming in its theory building. Existing models have focused on enablers of sustainable supply chain independently which lacks a holistic view in understanding the integrated logistics for sustainable supply chain.

An extensive literature review, expert opinion from both industry and academia based on questionnaire survey, is conducted to find the relevant enablers. The modeling of these enablers is done using total interpretive structural modeling (TISM). Finally, TISM along with its respective fuzzy-matriced impact croises multiplication applique (fuzzy-MICMAC) analysis is depicted.

The result of the survey and TISM model with its respective fuzzy-MICMAC has been used to evolve the mutual relationships among the important enablers of integrated logistics of consumer durables. The strategic factors obtained from TISM are integration and collaboration in the supply chain, vehicle type, and capacity; reduction in average length of haul; and real-time information system. Route selection and scheduling, reduction of fuel consumption, customer relationship management, green technology, cost reduction, etc., are some of the operational factors. Sustainable environment performance is obtained as the performance factor. Fuzzy-MICMAC is more responsive than the traditional MICMAC analysis.

The study has limitation for the development of a conceptual framework for integrated logistics in uncertain environments. So it can be extended by combining soft computing methodologies. There is a lack of mathematical quantification of the proposed model where the enablers of sustainability can be measured.

The study on integrated logistics for sustainable supply chain is itself a new area to be explored, as very few studies on this relevant topic exist. The research concentrates on TISM for the integrated logistics and the movement of consumer durables through different distribution channels of a supply chain. The study has implications for practitioners, academicians, and policy makers. For practitioners, it provides a list of strategic factors, operational factors, and performance factors. For academicians, this methodology can be opted to conduct an exploratory study by identifying the essential enablers. For policy makers, the regulations can be developed using the above model.

It is an effort to model the important enablers and establish sustainability in integrated logistics of consumer durables.

The purpose of this paper is to identify the information and communication technology management enablers (ICTMEs) and establish the hierarchical relationship among them using interpretive structural modelling (ISM) and analyse their driving and dependence power, using integrated ISM fuzzy-MICMAC analyses.

For identifying the ICTMEs, along with extensive literature review a large number of academicians and practitioners of repute are consulted. The contextual relationships between ICTMEs are established with the help of a well-established ISM methodology and further ICTMEs are analysed on the basis of their driving and dependence power and classified them into four different clusters by using fuzzy-MICMAC.

This paper has identified 25 key ICTMEs related to human resource, organization culture, technology, strategic planning, ICTM practices and organizational performance measurement and created a diagraph representing hierarchical relationship among them. Further these enablers are analysed and classified into four clusters on the basis of their driving and dependence power.

The developed relational model is based on the inputs of academicians and practitioners and any biasing from the person judging the ICTM enablers might influence the power of this model.

Top management of the organization could formulate and execute their strategies keeping in mind these identified critical enablers and relationship among them which will finally result into higher performance of ICTM.

This is the first kind of study which has identified 25 key enablers of ICTM, established hierarchical relationship among them and analysed them on the basis of their driving and dependence power using integrative ISM fuzzy-MICMAC analysis.

The purpose of this paper is to identify and build a hierarchy of the factors influencing competitiveness of electronics manufacturing industry (EMI) at the industry level and apply the interpretive structural modeling, fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á UN Classement (i.e. the cross-impact matrix multiplication applied to classification; MICMAC) and analytic hierarchy process (AHP) approaches. These factors have been explained with respect to managerial and government policymakers’ standpoint in Indian context.

This study presents a hierarchy and weight-based model that demonstrates mutual relationships among the significant factors of competitiveness of the Indian EMI.

This study covers a wide variety of factors that form the bedrock of the competitiveness of the EMI. Interpretive structural modeling and fuzzy MICMAC are used to cluster the influential factors of competitiveness considering the driving and dependence power. AHP is used to rank the factors on the basis of weights. Results show that the “government role” and “foreign exchange market” have a significantly high driving power. On the other hand, the “capital resource availability” and “productivity measures” come at the top of the interpretive structural modeling hierarchy, implying high dependence power.

The study has strong practical implications for both the manufacturers and the policymakers. The manufacturers need to focus on the factors of competitiveness to improve performance, and at the same time, the government should come forward to build a suitable environment for business in light of the huge demand and frame suitable policies.

The lackluster performance of the industry is because of the existing electronics policies and environmental conditions. The proposed interpretive structural modeling and fuzzy MICMAC and AHP frameworks suggest a better understanding of the key factors and their mutual relationship to analyze competitiveness of the electronics manufacturing industry in view of the Indian Government’s “Make in India” initiatives.

This paper contributes to the industry level competitiveness and dynamics of multi-factors approach and utilize the ISM–fuzzy MICMAC and AHP management decision tool in the identification and ranking of factors that influence the competitiveness of the EMI in the country.