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.

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.

Total Interpretive Structural Modeling (TISM): approach and application

2017 ◽  
Vol 14 (2) ◽  
pp. 162-181 ◽  
Author(s):  
J. Jena ◽  
Sumati Sidharth ◽  
Lakshman S. Thakur ◽  
Devendra Kumar Pathak ◽  
V.C. Pandey
Keyword(s):  
Design Methodology ◽  
Matrix Multiplication ◽  
Structural Modeling ◽  
Decision Makers ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Mutual Dependence ◽  
Step Procedure ◽  
Power And Dependence ◽  
Practical Implications

Purpose The purpose of this paper is to elucidate the methodology of total interpretive structural modeling (TISM) in order to provide interpretation for direct as well as significant transitive linkages in a directed graph. Design/methodology/approach This study begins by unfolding the concepts and advantages of TISM. The step-by-step methodology of TISM is exemplified by employing it to analyze the mutual dependence among inhibitors of smartphone manufacturing ecosystem development (SMED). Cross-impact matrix multiplication applied to the classification analysis is also performed to graphically represent these inhibitors based on their driving power and dependence. Findings This study highlights the significance of TISM over conventional interpretive structural modeling (ISM). The inhibitors of SMED are explored by reviewing existing literature and obtaining experts’ opinions. TISM is employed to classify these inhibitors in order to devise a five-level hierarchical structure based on their driving power and dependence. Practical implications This study facilitates decision makers to take required actions to mitigate these inhibitors. Inhibitors (with strong driving power), which occupy the bottom level in the TISM hierarchy, require more attention from top management and effective monitoring of these inhibitors can assist in achieving the organizations’ goals. Originality/value By unfolding the benefits of TISM over ISM, this study is an endeavor to develop insights toward utilization of TISM for modeling inhibitors of SMED. This paper elaborates step-by-step procedure to perform TISM and hence makes it simple for researchers to understand its concepts. To the best of the authors’ knowledge, this is the first study that analyzes the inhibitors of SMED by utilizing TISM approach.

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 Identification and Prioritization of Critical Success Factors for Off-Site Construction Using ISM and MICMAC Analysis

2021 ◽  
Vol 13 (16) ◽  
pp. 8911
Author(s):  
Seoyoung Jung ◽  
Seulki Lee ◽  
Jungho Yu
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Interpretive Structural Modeling ◽  
Micmac Analysis ◽  
Critical Success ◽  
Management Measures ◽  
Proposed Model ◽  
Complex Relationships ◽  
Power And Dependence ◽  
The Relationship

Many studies have been conducted to define the critical success factors (CSFs) for off-site construction (OSC) activation, but there has been a lack of identification of the relationship with the identified CSFs. However, it is necessary to clearly identify the hierarchy and relationships with the success factors in order to develop specific strategies for OSC activation. This work presents a study that was conducted to identify the CSFs for OSCs and establish the relationships of the identified CSFs for OSC. First, 20 CSFs for OSCs were identified through prior study reviews related to CSFs for OSC. Next, the interpretive structural modeling (ISM), which has advantages in developing an understanding of complex relationships, was leveraged in order to analyze the relationships between 20 CSFs for OSC to derive a hierarchical model consisting of seven levels. The CSFs for OSC were classified into four groups using MICMAC analysis, which is useful for classifying factors by the strength of the relationship with factors based on driving power and dependence power. This proposed model can be used as a basis for developing management measures for OSC project success.

Interpretive Structural Modeling of GIoT enablers

2020 ◽  
Vol 13 (2) ◽  
pp. 129-140
Author(s):  
Pooja Gupta ◽  
Vijay Kumar Jain
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Literature Review ◽  
Structural Model ◽  
Structural Modeling ◽  
Smart Objects ◽  
Interpretive Structural Modeling ◽  
Adoption Of Technology ◽  
Research Article ◽  
Power And Dependence

Recent emerging developments and enthusiastic adoption of technology are leading towards a smarter world but have also led to an increase in carbon traces. The Green Internet of Things (G-IoT) has been widely promoted as a strategy to make environment greener, safer and more sustainable. The authors investigate and discuss different enabling technologies (smart objects, ICT, Cloud Computing, etc.) that can be cleverly deployed to attain G-IoT. This research article is an effort to build a structural model of different enablers, vital to implement G-IoT. An array of enablers of G-IoT accomplishment has been recognized from literature review and experts' opinions. After a number of brainstorming sessions, contextual relationships have been identified among these enablers. In addition to this, enablers have been categorized based upon the driving power and dependence. Further, a structural model of G-IoT enablers has also been developed by means of Interpretive Structural Modeling (ISM) procedure. A total of nine enablers have been acknowledged from the literature and experts' opinions.

Knowledge management implementation in Indian automobile ancillary industries

2016 ◽  
Vol 11 (3) ◽  
pp. 802-810 ◽  
Author(s):  
Nitin Yashwant Patil ◽  
Ravi M. Warkhedkar
Keyword(s):  
Knowledge Management ◽  
Industrial Area ◽  
Total Quality ◽  
Limited Data ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Relationship Of ◽  
Power And Dependence ◽  
The Relationship

Purpose In the past decade, much has been written about knowledge management (KM) in the manufacturing; however, less attention has been paid to the Indian automobile ancillary industries located in Chinchwad, Pune. It is suitable to find out the relationship of the factors of the study. It helps in identifying the hierarchy of factors to be taken, and interlinking of production department with KM improves the productivity of the industries. Categorization of these principles based on their driving power (principles which hold other principles) and dependence (principles which are dependent on other principles) has also been examined for KM implementation to study the driving power and dependence power of these principles. This paper aims to determine the roadmap of KM implementation and categorize KM principles based on their driving power for manufacturing industries with the use of the interpretive structural modeling (ISM)-based model. The results indicate that the principles possessing higher driving power, such as KM, inventory control, quality control, productivity and scheduling and their interlinking. The major contribution of this research lies in the development of contextual relationship among various identified factors of KM and determination of their driving and dependence power through a single systemic framework. Design/methodology/approach In this paper, author find out the suitability ISM for Indian Automobile industries to find the relation among the variables. Findings ISM model has been developed for the hierarchy of the identified KM. As ISM model results a hypothetical hierarchy which needs a proper quantitative analysis to evaluate their percentage effectiveness in the hierarchy. Research limitations/implications It is applied to automobile industries with limited number of variables that will show the dependence variable and driving variables and their interrelations. It can be applied other fields to fine the relationship of variables. Practical implications The ISM may be used in supply chain management and total quality management to find interlinking between the variables. Originality/value The limited data collected from Pimpri Chinchwad industrial area of Pune from Maharashtra state (India).

Factors affecting strategic plan implementation using interpretive structural modeling (ISM)

2018 ◽  
Vol 31 (5) ◽  
pp. 406-414 ◽  
Author(s):  
Mohammadkarim Bahadori ◽  
Ehsan Teymourzadeh ◽  
Hamidreza Tajik ◽  
Ramin Ravangard ◽  
Mehdi Raadabadi ◽  
...  
Keyword(s):  
Strategic Planning ◽  
Planning Process ◽  
Strategic Plan ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Plan Implementation ◽  
Content Type ◽  
Factors Affecting ◽  
Strategic Planning Process ◽  
The Relationship

PurposeStrategic planning is the best tool for managers seeking an informed presence and participation in the market without surrendering to changes. Strategic planning enables managers to achieve their organizational goals and objectives. Hospital goals, such as improving service quality and increasing patient satisfaction cannot be achieved if agreed strategies are not implemented. The purpose of this paper is to investigate the factors affecting strategic plan implementation in one teaching hospital using interpretive structural modeling (ISM).Design/methodology/approachThe authors used a descriptive study involving experts and senior managers; 16 were selected as the study sample using a purposive sampling method. Data were collected using a questionnaire designed and prepared based on previous studies. Data were analyzed using ISM.FindingsFive main factors affected strategic plan implementation. Although all five variables and factors are top level, “senior manager awareness and participation in the strategic planning process” and “creating and maintaining team participation in the strategic planning process” had maximum drive power. “Organizational structure effects on the strategic planning process” and “Organizational culture effects on the strategic planning process” had maximum dependence power.Practical implicationsIdentifying factors affecting strategic plan implementation is a basis for healthcare quality improvement by analyzing the relationship among factors and overcoming the barriers.Originality/valueThe authors used ISM to analyze the relationship between factors affecting strategic plan implementation.

Interpretive Structural Modeling (ISM) and its application in analyzing factors inhibiting implementation of Total Productive Maintenance (TPM)

2015 ◽  
Vol 32 (3) ◽  
pp. 308-331 ◽  
Author(s):  
Prasanth S. Poduval ◽  
V. R. Pramod ◽  
Jagathy Raj V. P.
Keyword(s):  
Action Plan ◽  
Structural Modeling ◽  
Total Productive Maintenance ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Organization Management ◽  
Inhibiting Factors ◽  
Quality Of Products ◽  
The Relationship

Purpose – The purpose of this paper is to highlight the application of Interpretive Structural Modeling (ISM) to analyze the barriers in implementation of Total Productive Maintenance (TPM). TPM is explained in brief with emphasis on maintenance programs to improve quality of products, reliability of processes and reduction in cost. Barriers in implementation of TPM are also discussed. Concept of ISM and steps in developing ISM are described in detail. The authors then illustrate the research methodology which involves applying ISM to analyze barriers in TPM. Design/methodology/approach – The paper starts off by describing the concepts of TPM and ISM. Barriers in implementation of TPM are discussed. It explains ISM as a methodology to understand the underlying interrelationship among the inhibiting factors. The authors draw up an action plan to carry out research on the usage of ISM to study the TPM inhibitors, to develop an integrated model to establish the relationship among the different TPM inhibiting factors and to suggest action plan to mitigate these factors. Findings – Interpretive Structural Modeling (ISM) can be used to analyze the driving and dependence power of the variables inhibiting implementation of TPM. The barriers to implement TPM are described with detailed explanation. The complexity of the problem and the degree of interconnection among the variables can be found out. This will help Managers take action on mitigating the barriers. Practical implications – By analyzing the interrelationships among the barriers and their strengths, management can chalk out the strategy to implement TPM in an organization. Management will become aware of the barriers which have the maximum influence and then can act accordingly to mitigate these barriers. This will help in implementing TPM faster and in an organized manner. Originality/value – Many authors have used ISM to study various issues. A couple of authors have used ISM to determine barriers in implementation of TPM. The authors feel that most of the papers describe ISM in brief making it slightly difficult for readers to understand. This paper aims to explain elaborately step-by-step on how to develop an ISM making it easier for researchers to understand the ISM concept. Even though there are papers on TPM and difficulties in implementation of TPM, this paper explains the barriers in implementing TPM based on the experience of the corresponding author having worked in the refinery industry.

scholarly journals Integration of Interpretive Structural Modeling with Fuzzy Bayesian Network for Risk Assessment of Tunnel Collapse

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Leping He ◽  
Tao Tang ◽  
Qijun Hu ◽  
Qijie Cai ◽  
Zhijun Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Analysis ◽  
Bayesian Network ◽  
Risk Model ◽  
Multicriteria Decision Making ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Construction Risk ◽  
Tunnel Collapse ◽  
The Relationship

Frequent collapse accidents in tunnels are associated with many construction risk factors, and the interrelationship among these risk factors is complex and ambiguous. This study’s aim is to clarify the relationship among risk factors to reduce the tunnel collapse risk. A multicriteria decision-making method is proposed by combining interpretive structural modeling (ISM) and fuzzy Bayesian network (FBN). ISM is used to determine the hierarchical relationships among risk factors. FBN quantitatively analyzes the strength of the interaction among risk factors and conducts risk analysis. The ISM-FBN method contains three steps: (1) drawing the ISM-directed graph; (2) obtaining the probability of the FBN nodes; and (3) using GeNle to implement risk analysis. The proposed method is also used to assess the collapse risk and detect the critical factors in the Canglongxia Tunnel, China. This method’s tunnel collapse risk model can provide managers with clear risk information and better realize project management.

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.

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