scholarly journals Interpretive structural modeling of knowledge network in car industry’ R&D centers

2018 ◽  
Vol 9 (2) ◽  
pp. 562
Author(s):  
Ali Rezaeian ◽  
Rouhollah Bagheri
Keyword(s):  
Network Model ◽  
Applied Research ◽  
Graphical Model ◽  
Structural Modeling ◽  
Knowledge Network ◽  
Key Factors ◽  
Interpretive Structural Modeling ◽  
Car Industry ◽  
Micmac Analysis

The current research has been done with the aim of knowledge network interpretive structural modeling in car industry’s R&D centers. The key factors for implementing a knowledge network in car industry’s R&D centers have been determined and then the final graphical model has been drawn by Interpretive Structural Modeling (ISM) approach.The method of the current applied research includes a survey of experts and then the variables extracted through investigating research background, after that the MATLAB R2013 software is used for making compatible matrix as well as drawing graphical relations of the model by Interpretive Structural Modeling approach.After studying related works & interviewing with under-studied firms’ managers, interpretive structural modeling (ISM) & MICMAC analysis was used to generate a model for knowledge network. Previous studies had not investigated the knowledge network in car industry’s R&D centers; however, the present study implemented the knowledge network model in R&D Centers.

Modeling Deming’s quality principles to improve performance using interpretive structural modeling and MICMAC analysis

2019 ◽  
Vol 36 (7) ◽  
pp. 1159-1180 ◽  
Author(s):  
Nishant Mukesh Agrawal
Keyword(s):  
Manufacturing Industry ◽  
Structural Modeling ◽  
Total Quality ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Practical Applications ◽  
Micmac Analysis ◽  
Significant Relationships ◽  
Quality Principles ◽  
Quality Framework

Purpose The purpose of this paper is to study the 14 principles of Edwards Deming and create significant relationships between them. No research has been reported on the implementation of Total Quality Management (TQM) using Deming’s 14 principles. To fill this gap, Interpretive Structural Modeling (ISM) and MICMAC analysis have been developed to understand mutual interactions among variables and find both the dependence and driving power of these variables. Design/methodology/approach The research paper discusses a blend of practical applications and introduces a theoretical framework. An ISM-based methodology is used to study and examine interactions between identified variables, while MICMAC analysis is used to identify the dependence and driving power. Findings This research utilizes Deming’s 14 quality principles, with experts from academia and industry consulted to identify contextual relationships among variables. The result shows that the stated principles “take action to accomplish the transformation,” “institute training,” “encourage education to employees” and “institute leadership” are strategic requirements, while “drive out fear,” “break down barrier between staff areas” and “eliminate numerical quotas” are tactical requirements. “Adopt the new philosophy,” “create constancy in improvement of product and service” and “cease dependence on mass inspections” are operational requirements for TQM applications. Originality/value An ISM-based quality framework, dependence power and driving power of variables using MICMAC analysis have been recommended to the service and manufacturing industry as a new focus area in the implementation of TQM.

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vishal Ashok Wankhede ◽  
Vinodh S.
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Structural Model ◽  
Matrix Multiplication ◽  
Structural Modeling ◽  
Influential Factors ◽  
Time Data ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis

Purpose The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration. Design/methodology/approach AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor. Findings The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively. Research limitations/implications In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies. Practical implications The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption. Originality/value The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.

scholarly journals Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network

2021 ◽  
Vol 18 (6) ◽  
pp. 9233-9252
Author(s):  
Mahmood Ahmad ◽  
◽  
Feezan Ahmad ◽  
Jiandong Huang ◽  
Muhammad Junaid Iqbal ◽  
...  
Keyword(s):  
Graphical Model ◽  
Influence Factors ◽  
Structural Modeling ◽  
Bayesian Belief Network ◽  
Soil Liquefaction ◽  
Support Vector ◽  
Probabilistic Graphical Model ◽  
Interpretive Structural Modeling ◽  
Liquefaction Potential ◽  
Belief Network

<abstract> <p>This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.</p> </abstract>

scholarly journals Probabilistic Evaluation of CPT-based Seismic Soil Liquefaction Potential: Towards the Integration of Interpretive Structural Modeling and Bayesian Belief Network

2021 ◽  
Author(s):  
Mahmood Ahmad ◽  
Xiao-Wei Tang ◽  
Feezan Ahmad ◽  
Nima Pirhadi ◽  
Xusheng Wan ◽  
...  
Keyword(s):  
Graphical Model ◽  
Influence Factors ◽  
Structural Modeling ◽  
Bayesian Belief Network ◽  
Soil Liquefaction ◽  
Support Vector ◽  
Probabilistic Graphical Model ◽  
Interpretive Structural Modeling ◽  
Liquefaction Potential ◽  
Belief Network

Abstract This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict CPT-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naïve Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

Modeling reconfigurability in supply chains using total interpretive structural modeling

2017 ◽  
Vol 14 (2) ◽  
pp. 194-221 ◽  
Author(s):  
Pallab Biswas
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
New Product Development ◽  
Structural Changes ◽  
Structural Modeling ◽  
Global Market ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Expert Opinions

Purpose The purpose of this paper is to identify, analyze, and categorize the major enablers of reconfigurability that can facilitate structural changes within a supply chain in a global scenario. The paper also addresses five reconfigurability dimensions in the perspective of supply chains and the major enablers to attain them. The paper further aims to understand the mutual interactions among these enablers through the identification of hierarchical relationships among them. Design/methodology/approach A framework that holistically considers all the major enablers of reconfigurability has been developed. The hierarchical interrelationships between major enablers have been presented and interpreted using a novel qualitative modeling technique, i.e., total interpretive structural modeling (TISM), which is an extension of ISM. SPSS 22.0 is employed to carry out a one-tailed one-sample t-test further to test the hypotheses for validating the results of TISM. Impact matrix cross-reference multiplication applied to a classification (MICMAC) analysis has been employed to identify the driving and dependence powers of these reconfigurability enablers. Findings In this paper, 15 enablers for reconfigurability paradigm have been identified through literature review and expert opinions. The authors established interrelationships and interdependencies among these enablers and categorized them as enablers of each dimension. New product development and customer satisfaction come at the highest level of priority. The levels of these enablers were obtained using TISM. The authors compared the results with the clusters derived from MICMAC analysis, and the results are found to be well within the acceptable range. Research limitations/implications The study has implications for both practitioners and academia. The work provides a comprehensive list of enablers that are relevant to reconfigure supply chains in today’s volatile global market. This research will also help decision makers to strategically focus on the top-level enablers and their concerned dimensions. The research is based on an automobile company case study and can be extended to products with volatile and changing demands. Originality/value The proposed model for reconfigurability enablers using TISM is a new effort altogether in the area of supply chain management. The novelty of this research lies in its identification of specific enablers to reconfigure a supply chain through different dimensions.

Modelling of Cloud Computing Enablers Using MICMAC Analysis and TISM

2019 ◽  
pp. 876-888 ◽  
Author(s):  
Nitin Chawla ◽  
Deepak Kumar
Keyword(s):  
Cloud Computing ◽  
Matrix Multiplication ◽  
Structural Modeling ◽  
Relationship Matrix ◽  
Interpretive Structural Modeling ◽  
Micmac Analysis ◽  
Business Efficiency ◽  
It Departments ◽  
Power And Dependence ◽  
The Relationship

This article describes how Cloud Computing is not just a buzzword but a shift from IT departments to the outsourcing vendors without impacting business efficiency. Some organizations are moving towards cloud computing but many have resistance to adopting cloud computing due to limitations in knowledge and awareness of the classifying elements, which effect decisions on the acceptance of cloud computing. Therefore, this article has focused on accumulating the elements, which can act as enablers, by reviewing existing literature and studies from both professional and academic viewpoints. All the identified enablers have been structurally modeled to develop the relationship matrix and establish the driving power and dependence power of every element. This is done by employing Total Interpretive Structural Modeling (TISM) and Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis.

Modelling of Cloud Computing Enablers Using MICMAC Analysis and TISM

2018 ◽  
Vol 9 (3) ◽  
pp. 31-43
Author(s):  
Nitin Chawla ◽  
Deepak Kumar
Keyword(s):  
Cloud Computing ◽  
Matrix Multiplication ◽  
Structural Modeling ◽  
Relationship Matrix ◽  
Interpretive Structural Modeling ◽  
Micmac Analysis ◽  
Business Efficiency ◽  
It Departments ◽  
Power And Dependence ◽  
The Relationship

This article describes how Cloud Computing is not just a buzzword but a shift from IT departments to the outsourcing vendors without impacting business efficiency. Some organizations are moving towards cloud computing but many have resistance to adopting cloud computing due to limitations in knowledge and awareness of the classifying elements, which effect decisions on the acceptance of cloud computing. Therefore, this article has focused on accumulating the elements, which can act as enablers, by reviewing existing literature and studies from both professional and academic viewpoints. All the identified enablers have been structurally modeled to develop the relationship matrix and establish the driving power and dependence power of every element. This is done by employing Total Interpretive Structural Modeling (TISM) and Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis.

Modeling the factors affecting the quality of life in diabetic patients in India using total interpretive structural modeling

2019 ◽  
Vol 26 (3) ◽  
pp. 951-970 ◽  
Author(s):  
Puneeta Ajmera ◽  
Vineet Jain
Keyword(s):  
Quality Of Life ◽  
Diabetes Mellitus ◽  
Structural Modeling ◽  
World Health ◽  
Psychological State ◽  
Diabetic Patients ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis

Purpose Diabetes mellitus has become a major world health problem that has unenviable impacts on health of the people including quality of life (QOL) also and in which person’s physical and psychological state, social commitments and relationships and his interaction with the environment is affected. This shows that there is an urgent need for behavior change and considerable educational strategies for proper management and rehabilitation (Reddy, 2000). This research has identified and ranked the significant factors which affect the QOL in diabetic patients in India. The paper aims to discuss these issues. Design/methodology/approach In this paper, nine factors which affect the QOL in diabetic patients in India have been identified through review of the literature and evaluated by total interpretive structural modeling (TISM) approach, i.e. an extended version of ISM. In this approach, interpretations of the interrelationship among factors have been discussed. Therefore, TISM approach has been used to develop the model and the mutual interactions among these factors. Findings The results of the model and MICMAC analysis indicate that diet restriction, body pain and satisfaction with treatment are the top-level factors. Practical implications Identification of the factors that have a remarkable effect on the QOL in diabetic patients is very important so that the doctors and other healthcare professionals may handle these factors efficiently and proper rehabilitation can be provided to such patients. Originality/value This paper has used an application of the TISM approach to interpret the mutual relationship by using the tool of interpretive matrix and has developed a framework to calculate the drive and the dependence power of factors using MICMAC analysis. The issues related to QOL are extremely important, as they can strongly anticipate a person’s capability to govern his lifestyle with disease like diabetes mellitus and maintain good health in the long run. This shows the urgent requirement of an optimized model which can predict and interpret the relationships among these factors. In this research, the interrelationships among these factors have been developed and interpretations of these interactions have been given to develop a comprehensive model so that QOL of diabetic patients may be improved.

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