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The paper aims to identify key human and operational focused barriers to the implementation of Total Quality Management (TQM). It develops a comprehensive structural relationship between various barriers to successfully implement TQM for sustainability in Indian organizations.

With the help of expert opinions and extant literature review, we identified the case of TQM failure companies and barriers to implement TQM effectively. Interpretive Structural Modeling (ISM) and fuzzy MICMAC techniques are employed to develop a structural model and the identified barriers are categorized based on their dependence and driving power in the various categories.

From the intensive case analysis, we identify fourteen barriers that constrain the successful implementation of TQM. The findings also provide a hierarchy of barriers in which the absence of top management involvement and ineffective leadership are the human barriers having the highest dependence.

The critical inputs show the implementation of TQM in the firms being more proactive and well prepared in the selected five companies. The study's emphasis on barriers will help organizations in implementing TQM for better sustainability in an organizational context.

In the successful implementation of TQM, barriers need to be identified because failure has often eliminated the organizations from the market. Thus, TQM is the source of strength to achieve higher productivity, profitability, and sustainable business performance. The barriers must be identified to improve organizational performance to contribute to sustainable development.

The purpose of this paper is to understand possible linkage between variables that constitute a lean manufacturing enterprise. In the study the authors have tried to decode the complex relationship among variables which is missing in extant literature.

In the study the authors have used systematic literature review (SLR) approach to identify the variables from extant literature and used interpretive structural modelling (ISM) and Fuzzy MICMAC analysis to understand complex equation among variables from Indian manufacturing firm perspective.

The findings using ISM modeling indicate top management support is the bottom level and business performance is the top level. In order to further resolve conflicts the authors have further analyzed variables using Fuzzy MICMAC analysis which has further divided variables into four clusters. The Fuzzy MICMAC output suggests that top management support, real time production information, training and team work are the driving variables and business performance, total quality management and lean behavior are the dependence variables.

Like any study, the study have its own limitations. In the study the authors have developed the model based on expert opinion. The number may be not enough to validate this model statistically. However, it can be regarded as a platform for further investigation using structural equation modeling.

The present study using ISM model has proposed a model based upon experts, identified from Indian major manufacturing firms. This model can further provide empirical platform for further investigation which can resolve lean manufacturing issues.

The purpose of this paper is to identify various factors influencing additive manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them.

The methodology includes three phases, namely, identification of factors through systematic literature review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify it by deriving the interrelationship between the factors using interpretive structural modeling (ISM), followed with the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis.

This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors, namely, manufacturing flexibility, operational excellence and firm competitiveness are placed at the top level of the model, which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom-level factors.

Additional factors may be considered, which are important for AM implementation from different industry contexts. The variations from different industry contexts and geographical locations can foster the theoretical robustness of the model.

The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors.

This paper contributes by identification of AM implementation factors based on in-depth literature review as per SLR methodology and validation of these factors from a variety of industries and developing hierarchical model by integrative ISM-MICMAC approach.

The purpose of this paper is to identify Lean Six Sigma enablers (LSSEs) and analyse the interaction among the enablers via a hierarchical model developed by employing interpretive structural modelling (ISM) and determine the driving and dependence power of enablers through fuzzy MICMAC (Matriced’ Impacts Croise´s Multiplication Applique´ea´un Classement) analysis.

An expert group of industry professionals and academicians is consulted at the initial stage as an input for ISM methodology to explore the paired relationship among LSSEs for each parameter of Lean Six Sigma (LSS) implementation. The outcome of ISM is further utilized by fuzzy MICMAC analysis to discover the enablers that are strong drivers and highly dependent. Fuzzy set is included in MICMAC analysis in order to obtain more precise output and effective model.

In total, 20 key enablers are identified through a literature review and expert opinion that emerged as the most significant factors towards LSS implementation. The identified enablers are portrayed into a structural form representing as input and output variables. Later, the driving and the dependence power of each enabler is presented in cluster form.

The paired relationships among LSSEs are obtained through the interpretation made by the experts. The judgments of experts are subjective and may be biased; as difference in expert opinion may influence the final outcome. Conducting a large-scale survey may provide a better catch for interactions of LSSEs.

This study provides strong practical implications for researchers as well as industry practitioners. The industry professionals must deliberately focus on the identified LSSEs more conservatively during LSS implementation and the top management should plan strategically to avoid any implementation failure.

The present study identifies 20 crucial enablers of integrated LSS and presents them in a hierarchical form which will be beneficial for researchers and practitioners. The interactions among the enablers shown in cluster form will help in better execution of LSS.

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.

World over organizations are focusing on sustainable goals, where along with economic success their role in protecting the planet and people are becoming important. Whilst transforming the supply chain into a sustainable one, there would be some barriers which might hinder this process. This paper aims to study these barriers in the context of the electronics industry so that organizations can better implement sustainable supply chain programs.

In this research, barriers affecting sustainability implementation in the electronics supply chain are shortlisted from literature review and experts’ opinion. Using the combined methodology of Grey DEMATEL, the causal factors, the effect factors and degree of prominence of barriers is found out. The overall relationship among barriers is established by a diagraph. Sensitivity analysis is performed to check the robustness of the results.

It is found that lack of regulation and guidance from authorities is the primary causal barrier affecting operations of sustainable supply chain management. There are five barriers which fall in the influenced group and among them, complexity in measuring and monitoring sustainability practices has the largest net effect value on the implementation of a sustainable supply chain. The barrier having the highest correlation with other barriers is the high cost for disposal of hazardous wastes. The implications of these findings on managers and academicians is explored in the study.

In this research, the number of barriers shortlisted is limited to 11 in the context of the electronics supply chain. More factors could be added in future research based on the industry being studied.

The research analyses 11 barriers under categories of policy, technology, financial and human resources in the Indian electronics industry by evaluating the cause and effect group of barriers. These results can guide policymakers of the electronic sector and industry for mitigating barriers during the implementation of sustainable programs.

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 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 aim of this paper is, first, the desire to present the issue of retail sector competitiveness with the simultaneous determination of factors having an impact on competitiveness and their development. The main aim is to identify the factors and relationships among those factors to strengthen the competitive positioning of apparel retail stores.

The literature review and experts’ opinion helped to identify the key factors. The relationships among the factors were obtained by using interpretive structural modelling (ISM). Experts’ opinions were collected again for the fuzzy direct relationship matrix. Factors were further classified by driver and dependence power using the fuzzy matrix of cross-impact multiplications applied to classification (FMICMAC) analysis.

Total nine strengthening factors (SFs) identified here, and developed an integrated model using ISM and classified it into four clusters with the help of driver and dependence power. The model hierarchy shows the interrelationships among these SFs. The retail environment, Information and Communication Technology, technology adoption and human resource management were found to be the most significant factors needing some spotlight by the top-level authority.

The study will help managers to understand the variables and their relationships and to select the right factors to achieve a potential competitive position. Relationships among the factors were obtained through the opinions of experts and academicians. Expert opinion is a subjective judgement, and biasing in judgement might affect the result.

The research presents the first kind of an integrated model using ISM and FMICMAC to identify nine factors and classify them by their driving and dependence power. The developed model helps in the identification, classification and selection of factors as per requirement. This study will assist managers to understand the variables and their relationships and to select right factors to achieve a potential competitive position.

The purpose of this paper is to investigate the current level of supply chain practices (SCPs) in Indian manufacturing organizations.

The 15 SCPs are identified based on the literature support and opinion of industry experts and academia, and data were collected from 292 organizations. Data were analyzed using the statistical package for the social science software to see the current level/penetration of SCPs in Indian manufacturing organizations.

The practices, namely, organizational culture, customer relationship, information and communication technology, benchmarking and performance measurement, lean manufacturing, agile manufacturing, supplier relationship are highly penetrated practices in Indian manufacturing organizations. The practices, namely, outsourcing, information sharing, just in time manufacturing, green supply chain management are moderately penetrated practices, while the practices, namely, reverse logistics, postponement, vendor managed inventory, radio frequency are least penetrated practices in Indian manufacturing organizations.

Further study can be extended to see the of penetration practices applicable to service and agriculture sectors.

The result of this paper will enable the organizations to identify and direct their focus on the areas that requires improvement. Also, the organizations will become more aware of the SCPs that will help in boosting up their performance and competitiveness and indirectly boost the growth and contribute to India’s economic development.

This is the first kind of study which checked the level of selected SCPs in Indian manufacturing organizations.