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In the semiconductor electronics industry, effective heat removal from the integrated circuits (IC) chip, through the electronic package to the environment is crucial to maintain an allowable junction temperature of the IC chip. Thermal performances of such electronic packages are characterized by package thermal resistance called θ‐JA and are widely used in the electronic industry. Improving thermal performance is numerically predicted using computational fluid dynamics (CFD) technique and experimental tests are carried out to verify the numerical predictions. To provide new/additional data and demonstrate CFD technique for thermal characterization of electronic packages with experimental results.

The thermal performance of electronic packages has been studied using a CFD technique. The finite volume method is a technique used for solving a set of partial differential equations in a domain, using control volume based discretization. A detailed thermal model of an electronic package was created using a CFD tool and validated against the experimental data obtained in a natural convection environment, compliant to JEDEC standards. The thermal performance of the package was evaluated for different die sizes and epoxy molding compounds at different power levels. The use of a heat slug was investigated to identify its effect on heat dissipation for the future generations of IC, which are expected to be smaller in size and to dissipate more power. Free convective flow velocities, detailed temperature and heat flow distributions around the package will also be presented.

The study demonstrates that applying CFD techniques can provide accurate results on estimating thermal characterization of an electronic package. Predicted device junction temperatures as well as the thermal resistance of packages can be predicted with a good accuracy for different ranges of power levels in natural convection. The numerically estimated die junction temperatures have also been found to be accurate and reliable.

The analysis is limited to an incompressible fluid. The effect of forced convection is not considered.

New and additional generated data will be helpful in the design and decision making time of the product to choose a low cost and viable thermal performance solution in the cooling of electronic components at low power.

The electronic package involves multi‐material and applying CFD technique is useful to determine the accurate thermal performance and simple and fast to apply for different conditions/material sets. Predictions of junction‐to‐ambient thermal resistance and device junction temperature values are compared against measurements. Excellent correlation was obtained. The results thus obtained compare well with the experimental results, but the computational effort and time required in the analysis is much small as compared.

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.

This study adopted interview methods and field visits to collect the data. An audio recording was done for the whole interview and presented as facts in this case. Field visits were done to see the packs and understand the consumers and their purchase habits of pickles.

Pandian Pickles is a pickle manufacturer located in Madurai, Tamil Nadu, a state in the southern part of India. Mr Kandasamy, one of the partner of the Pandian pickle, had been thinking of ways to grow the business. Pandian Pickles dominated the low-price unit (LPU) market with a unique packing of pickles done in “arecanut” leaf. This added a unique flavour to their pickles. Mr Kandasamy envisioned to grow the business by introducing higher stock-keeping units in the form of jars and tap the middle class and the upper-middle-class segments in the market. In this category, there were much more prominent and branded players. Being a small regional player, Govindan wondered how Pandian Pickles would take these more prominent players in the industry head-on.

The case is ideally suited for discussing the concept of product line stretching, particularly in the product mix strategies of a small and medium enterprise (SME). The case can best fit into the courses such as Entrepreneurship Development, Product and Brand Management, Marketing Management for the Undergraduate levels and in the courses such as Strategic Marketing, Bottom of the Pyramid Markets and Strategies Management of SMEs in the postgraduate levels.

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 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 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 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 paper is to develop a comprehensive framework to identify and classify key medical tourism enablers (MTEs) and to study the direct and indirect effects of each enabler on the growth of medical tourism in India.

In this paper, an integrated approach using interpretive structural modeling (ISM) and Fuzzy Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (FMICMAC) analysis has been developed to identify and classify the key MTEs, typically identified by a comprehensive review of literature and expert opinion. The key enablers are also modeled to find their role and mutual influence.

The key finding of this modeling helps to identify and classify the enablers which may be useful for medical tourism decision makers to employ this model for formulating strategies in order to overcome challenges and to become a preferred medical tourism destination. Integrated model reveals enablers such as medicine insurance coverage, international healthcare collaboration, and efficient information system as dependent enablers. No enabler is found to be autonomous enablers. The important enablers like healthcare infrastructure facilities and global competition are found as the linkage enablers. Research in medicine and pharmaceutical science, medical tourism market, transplantation law, top management commitment, national healthcare policy, competent medical and para-medical staffs are found as the independent enablers. Integrated model also establishes the direct and indirect relationship among various enablers.

The research provides an integrated model using ISM and FMICMAC to identify and classify various key enablers of medical tourism in India. In conventional cross-impact matrix multiplication applied to classification analysis, binary relationship of various enablers is considered. FMICMAC analysis helps to establish possibility of relationship among various enablers so that low-key hidden factors can be identified. The low-key hidden factors may initially exhibit marginal influence but they may show significant influence later on during analysis. The uncertainty and fuzziness of relationship among various enablers can be conveniently handled by FMICMAC and expert opinions can easily be captured. This research will help medical tourism decision makers to select right enablers for the growth of medical tourism in India.

The purpose of this paper is to focus on studying the concept of “Smart Organization” and providing a comprehensive framework for the various factors as barriers for the smart organization, identifying and classifying the key criterion of these factors based on their direct and indirect relationships.

In this paper an extensive literature survey and experts’ opinion have been used to identify major barriers of smart organization. These barriers are then modeled using interpretative structural modeling (ISM) methodology. The model so developed has been further improved and an integrated model has been developed using fuzzy-MICMAC.

Various barriers of smart organization have been identified and a structural model has been developed for barriers using the ISM methodology. The critical barriers have been found out by fuzzy-MICMAC analysis. The driver power and dependence graph has been plotted for barriers. The barriers are classified into four categories which are, autonomous, linkage, dependent and independent according to their driver power and dependence. From the ISM model and the integrated model, and from further discussions with the experts, it has been found that the barriers “(B1) organizational structure” and “(B6) Managerial actions” are the two most important barriers, every other barrier is directly or indirectly driven by these.

The basis of developing the ISM model, i.e, the structural self-interaction matrix is based on experts’ opinion, therefore the result may get influenced if there is any biasing in judging the barriers. The future research scope for this paper will be to test the model generated in this paper. The testing of the model can be done by applying structural equation modeling technique, it has the capability of testing the hypothetical model. Further a framework of smart organizations can be created to find out the smartness of different organizations.

The paper can be used by organizations in understanding the barriers in becoming “smart” on the basis of their inter-relationships. This model can help manufacturing organization of North India in understanding the barriers which needs to be worked upon and the inter-relationship among these factors. This model-based study may be helpful in understanding and implementing the practices of smart organization by removing the possible critical barriers.

This is the first study to identify the barriers of smart organizations and to develop a model of these barriers using ISM and fuzzy-MICMAC.