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Article
Publication date: 16 November 2021

Pankaj Singh and Gaurav Agrawal

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Benchmarking: An International Journal, vol. 29 no. 8
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 1 August 2016

Anil S. Dube and Rupesh S. Gawande

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…

1444

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Benchmarking: An International Journal, vol. 23 no. 6
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 29 March 2013

S.J. Gorane and Ravi Kant

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…

2797

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 25 no. 2
Type: Research Article
ISSN: 1355-5855

Keywords

Article
Publication date: 11 February 2019

Vineet Jain and Vimlesh Kumar Soni

The purpose of this paper is to identify the flexible manufacturing system performance variables and analyze the interactions among these variables. Interpretive structural…

Abstract

Purpose

The purpose of this paper is to identify the flexible manufacturing system performance variables and analyze the interactions among these variables. Interpretive structural modeling (ISM) has been reported for this but no study has been done regarding the interaction of its variables. Therefore, fuzzy TISM (total ISM) has been applied to deduce the relationship and interactions between the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

Design/methodology/approach

Fuzzy TISM and fuzzy MICMAC analysis have been applied to deduce the relationship and interactions among the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

Findings

In total, 15 variables have been identified from the extensive literature review. The result showed that automation, use of automated material handling, an effect of tool life and rework percentage have high driving power and weak dependence power in the fuzzy TISM model and fuzzy MICMAC analysis. These are also at the lowest level in the hierarchy in the fuzzy TISM model.

Originality/value

Fuzzy TISM model has been suggested for manufacturing industries with fuzzy MICMAC analysis. This proposed approach is a novel attempt to integrate TISM approach with the fuzzy sets. The integration of TISM with fuzzy sets provides flexibility to decision-makers to further understand the level of influences of one criterion over another, which was earlier present only in the form of binary (0, 1) numbers; 0 represents no influence and 1 represents influence.

Details

Journal of Modelling in Management, vol. 14 no. 1
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 29 September 2023

Jih Kuang Chen

Effective total quality management (TQM) practices rely on the accurate classification of critical success factors (CSFs). The impact matrix cross-reference multiplication…

Abstract

Purpose

Effective total quality management (TQM) practices rely on the accurate classification of critical success factors (CSFs). The impact matrix cross-reference multiplication technique for classification (MICMAC) or/and fuzzy MICMAC (FMICMAC) can be used to identify key factors in the complex set. However, TQM includes both “hard” and “soft” factors, limiting application of the traditional MICMAC/FMICMAC method.

Design/methodology/approach

Previous literature on TQM was reviewed, CSFs were identified, and factors were sorted into soft and hard categories. The combined fuzzy integration and dual-aspect MICMAC (fuzzy dual-aspect MICMAC approach) was then applied to identify, cluster and prioritize the CSFs of TQM.

Findings

A total of 20 factors (10 soft and 10 hard) were identified and isolated to assess the manufacturing- and service-related TQM practices of the Pearl River Delta Region of China. Seven driver factors and one linkage factor emerged as the key CSFs that managers should prioritize.

Research limitations/implications

A major limitation of this study is the dependency of the results on the definitions of linguistic labels. If the linguistic definitions of TQM CSFs do not closely correspond to the expert opinion data, then the analysis results may be inaccurate. Additionally, although expert opinions are utilized in the proposed method for comprehensive assessments, these opinions may influence the final results due to their inherent subjectivity.

Originality/value

A novel fuzzy dual-aspect MICMAC approach was developed to identify and classify CSFs for optimal TQM practices. This approach allows clustering of CSFs so that decision-makers can prioritize factors according to their dependence and driving powers. Practitioners should concentrate on the CSFs with higher driving powers for successful TQM.

Details

The TQM Journal, vol. 36 no. 3
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 10 April 2017

Gunjan Yadav and Tushar N. Desai

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Article
Publication date: 12 March 2018

Mahamaya Mohanty

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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

Details

Management of Environmental Quality: An International Journal, vol. 29 no. 2
Type: Research Article
ISSN: 1477-7835

Keywords

Article
Publication date: 8 April 2020

Sachin Yadav and Surya Prakash Singh

The main objective of this paper is to justify the implementation of blockchain (BC) over the traditional method deployed in the supply chain (SC) after using the fuzzy–analytic…

1517

Abstract

Purpose

The main objective of this paper is to justify the implementation of blockchain (BC) over the traditional method deployed in the supply chain (SC) after using the fuzzy–analytic network process (fuzzy-ANP) application. Over the past two decades, the overall product cost is affected by the SC at a global level. Organizations are working on their existing SC for improving their performance. BC technology is a newly emerging technology and magnetizes the attention of researchers and industrialists. This technology is still at the initial stage, and only little investigation is available in the literature and it has not been much investigated by researchers.

Design/methodology/approach

Literature and expert opinion interpretation in BC characteristics are further analyzed and modeled using fuzzy–interpretive structural modeling (fuzzy-ISM), fuzzy-MICMAC and fuzzy-ANP. The combined approach of both fuzzy-ISM and fuzzy-MICMAC is applied to identify the common drivers to integrate the BC technology in the light of efficient supply chain management (SCM).

Findings

Comparative analysis between traditional and BC-based supply chain (BCSC) using fuzzy-ANP is carried out, considering the common driving characteristics. The proposed integrated (combined) approach of fuzzy-ISM, fuzzy-MICMAC and Fuzzy-ANP found that integration of BC with SCM is better prioritized than traditional supply chain management (TSCM). The findings in the article endorse that the TSCM can be made efficient by integrating the BC technology considering five most driving characteristics, namely, data safety and decentralization, accessibility, documentation, data management and quality.

Originality/value

The current proposed research work identifies 12 characteristics after studying numerous literature reviews and having a discussion with SC experts with knowledge of BC. The integrated approach of fuzzy-ISM and fuzzy-MICMAC is implemented here. After that, fuzzy-ANP is used to give ranking among BCSCM and TSCM. The study carried out in this article motivates industries to implement BC in their SC system. It will reduce the transaction cost, documentation work, save time and eliminate human error at the national and international levels. The common characteristics identified in this proposed work would help in managerial decisions for the adoption of BC to ensure that the system becomes more transparent, easily traceable and finally improve the performance.

Details

Journal of Enterprise Information Management, vol. 34 no. 1
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 6 March 2017

Rakesh Kumar Malviya and Ravi Kant

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…

1234

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Benchmarking: An International Journal, vol. 24 no. 2
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 26 June 2023

Jiangtao Hong, Yuting Quan, Xinggang Tong and Kwok Hung Lau

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Journal of Business & Industrial Marketing, vol. 39 no. 2
Type: Research Article
ISSN: 0885-8624

Keywords

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