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.

Sustainable industry 4.0 – an exploratory study for uncovering the drivers for integration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
N. Harikannan ◽  
S. Vinodh ◽  
Anand Gurumurthy
Keyword(s):  
Industry 4.0 ◽  
Structural Model ◽  
Public Awareness ◽  
Sustainable Manufacturing ◽  
Matrix Multiplication ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Societal Pressure ◽  
Practical Implications

Purpose The concept of sustainable manufacturing has been adopted by manufacturing organizations to develop eco-friendlier products and processes. In recent times, industries are progressing toward Industry 4.0 (I4.0). Guided with smart intelligent devices, I4.0 can possibly decrease excess production, material movement and consumption of energy. If so, it is hypothesized that there is a good synergy between I4.0 and sustainability, which warrants an integrated approach for implementation. This amalgamation is termed as “Sustainable industry 4.0.” Hence, this paper aims to systematically identify and analyze the drivers for this integration. Design/methodology/approach This paper presents the analysis of 20 drivers identified from literature review for simultaneous deployment of I4.0 and sustainable manufacturing. Interpretive structural modeling (ISM) is used to derive the structural model for analyzing the causal association between drivers. Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis is being performed to group the drivers. Findings The results showed that the dominant drivers derived are societal pressure and public awareness (D18), government policies on support I4.0 (D12), top management involvement and support (D15) and government promotions and regulations (D16). Also, the MICMAC analysis revealed many driving, dependent, linkage and autonomous drivers. Research limitations/implications The opinion from experts with combined expertise on I4.0 and sustainability was obtained. The respondent size could be increased in future studies. Practical implications The study has been done based on inputs from industry practitioners. Managerial and practical implications are presented. ISM shows that the drivers for deploying sustainable I4.0 are highly inter-related. It also reveals the pre-requisites for each level of the drivers. Originality/value The idea of analyzing the drivers for sustainable I4.0 is the original contribution of the authors.

TISM-based analysis of important factors for additive manufacturing in healthcare:a case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
S. Logesh ◽  
S. Vinodh
Keyword(s):  
Health Care ◽  
Additive Manufacturing ◽  
Structural Model ◽  
Matrix Multiplication ◽  
Extensive Literature ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Factors Influencing

Purpose This paper aims to focus on developing a theoretical framework for the analysis of factors influencing additive manufacturing (AM) in the health-care domain. Design/methodology/approach A total of 18 factors are considered through extensive literature review and the relationship between each factor is studied using total interpretive structural modeling (TISM) and the model is logically developed. TISM model is developed using appropriate expert inputs. In addition, cross-impact matrix multiplication applied to classification (MICMAC) analysis is conducted to group the factors. Findings It was found that “ease of design” and “research and development” are the two most important factors with the highest driving power and dependencies. Through MICMAC analysis, the significance of factors is studied. Practical implications The study has been done based on inputs from academic experts and industry practitioners. The inferences from the study have practical relevance. Originality/value The development of a structural model for the analysis of factors influencing AM in the health-care domain is the original contribution of the authors.

Application of total interpretive structural modelling (TISM) for analysis of factors influencing sustainable additive manufacturing: a case study

2019 ◽  
Vol 25 (7) ◽  
pp. 1198-1223 ◽  
Author(s):  
Rohit Agrawal ◽  
Vinodh S.
Keyword(s):  
Additive Manufacturing ◽  
Structural Model ◽  
Matrix Multiplication ◽  
Structural Modelling ◽  
Content Type ◽  
Interpretive Structural Modelling ◽  
Micmac Analysis ◽  
Factors Influencing ◽  
Total Interpretive Structural Modelling

Purpose The purpose of this study is to develop a structural model based on total interpretive structural modelling (TISM) approach for analysis of factors influencing sustainable additive manufacturing (AM). Design/methodology/approach A total of 20 factors influencing sustainable AM are identified on the basis of literature review. Appropriate inputs from experts are obtained and TISM model is developed. Also, cross-impact Matrix multiplication applied to classification (MICMAC) analysis is carried out to categorize the factors. Findings Based on TISM model, “Flexibility in manufacturing”, “Time to develop new product” and “Local availability of technology” are found to be the dominant factors. MICMAC analysis indicates that 10 factors belong to driving and 10 factors belong to dependent category. Research limitations/implications In the present study, 20 factors have been considered. In future, additional factors can be considered to deal with technological advancements. Practical implications The conduct of the study will enable AM experts to systematically analyze the factors influencing sustainable AM. Originality/value The development of structural model for analysis of factors influencing sustainable AM manufacturing is the original contribution of authors.

Analysis of interaction among the barriers to 5S implementation using interpretive structural modeling approach

2017 ◽  
Vol 24 (7) ◽  
pp. 1834-1853 ◽  
Author(s):  
Rajesh Attri ◽  
Bhupender Singh ◽  
Sunil Mehra
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Modeling ◽  
Equation Modeling ◽  
Successful Implementation ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Modeling Approach ◽  
Manufacturing Organizations ◽  
Top Management Commitment

Purpose The purpose of this paper is to ascertain and analyze the interactions among different barriers of 5S implementation in manufacturing organizations. Design/methodology/approach In this paper, 15 barriers affecting the implementation of 5S in manufacturing organizations have been identified from literature analysis and discussion with academic and industrial experts. Afterwards, identified barriers were validated by using nation-wide questionnaire-based survey. Then, interpretive structural modeling (ISM) approach has been utilized to find out the interaction among the identified barriers in order to develop hierarchy-based model. Findings The research identifies several key barriers which have high driving power and weak dependence power. In this concern, these barriers entail extreme care and handling for successful implementation of 5S. Financial constraints, lack of top management commitment, and no proper vision and mission are found to be the key barriers. Research limitations/implications The developed ISM model is based on experts’ opinion. This developed hierarchy-based model requires further validation by using structural equation modeling approach or by performing detailed case studies. Originality/value In this paper, ISM-based structural model has been recommended for Indian manufacturing organizations, which is a novel exertion in the area of 5S implementation.

TISM for analysis of barriers affecting the adoption of lean concepts to electronics component manufacture

2020 ◽  
Vol 11 (6) ◽  
pp. 1141-1173
Author(s):  
Senthil Kumar D. ◽  
S. Vinodh
Keyword(s):  
Structural Model ◽  
Matrix Multiplication ◽  
Change Agents ◽  
Practical Case ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Management Commitment ◽  
Top Management Commitment ◽  
Component Manufacturing ◽  
Selection Of

Purpose The purpose of this paper is to present the analysis of barriers affecting the adoption of lean concepts to electrical and electronics component manufacturing. Design/methodology/approach Lean concepts are being increasingly applied by electrical and electronics component manufacturers to enhance product value through streamlined process. To facilitate smooth adoption of lean concepts, barriers need to be analyzed and prioritized. In this context, a structural model of 24 barriers is developed through total interpretive structural modeling (TISM) approach. Findings ‘Changing governmental policies,’ ‘poor selection of change agents and improvement teams,’ ‘lack of top management commitment understanding and support of the system,’ ‘lack of team autonomy,’ ‘lack of flexibility and versatility’ and ‘lack of customer focus/involvement’ are found to be the dominant barriers based on TISM study. Interpretation statements are being derived from TISM model. Cross-impact matrix multiplication applied to classification analysis is conducted. Research limitations/implications In the present paper, 24 barriers are considered. In future, additional barriers could be considered to deal with managerial advancements. Practical implications The paper reports the practical case of analysis of barriers to lean adoption in electronics component manufacture. Hence, the inferences have practical relevance. Originality/value The development of structural model for the analysis of barriers to lean implementation in electronics component manufacturing small- and medium-sized enterprises is the original contribution of the authors.

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.

Understanding determinants of social networking service fatigue: an interpretive structural modeling approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lin Xiao ◽  
Ting Pan ◽  
Jian Mou ◽  
Lihua Huang
Keyword(s):  
Social Networking ◽  
Impression Management ◽  
Structural Model ◽  
Structural Modeling ◽  
Extensive Literature ◽  
Privacy Concern ◽  
Social Networking Service ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Factors Influencing

PurposeThe purpose of this paper is to build a comprehensive structural model to demonstrate the interrelationships of factors influencing social networking service (SNS) fatigue and to identify the varying degrees of influence.Design/methodology/approachA total of 14 factors influencing SNS fatigue are identified through an extensive literature review. Interpretive structural modeling (ISM) and Matrice d'Impacts Croisés Multiplication Appliqué à un Classement (MICMAC) analysis are employed to build a hierarchical model and classify these factors into four clusters.FindingsThe results revealed that ubiquitous connectivity and immediacy of feedback are key factors contributing to SNS fatigue through their strong influence on other factors. Privacy concern, impression management concern and work–life conflict lead directly to SNS fatigue. In contrast, system feature overload and system pace of change are relatively insignificant in generating SNS fatigue.Originality/valueThis study represents an initial step toward comprehensively understanding the interrelationships among the factors leading to SNS fatigue and reveals how determinants of SNS fatigue are hierarchically organized, thus extending existing research on SNS fatigue. It also provides logical consistency in the ISM-based model for SNS fatigue by grouping identified factors into dependent and independent categories. Moreover, it extends the applicability of the integration of the ISM and MICMAC approaches to the phenomenon of SNS fatigue.

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.

Factors influencing organizational agility in higher education

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shalini Menon ◽  
M. Suresh
Keyword(s):  
Higher Education ◽  
Organizational Structure ◽  
Human Resource ◽  
Structural Model ◽  
Readiness To Change ◽  
Matrix Multiplication ◽  
Extensive Literature ◽  
Organizational Agility ◽  
Interpretive Structural Modeling ◽  
Content Type

PurposeThe purpose of this paper is to explore the factors that can facilitate agility in higher education and to analyze the interrelationship between the factors.Design/methodology/approachA structured model of factors facilitating agility in higher education was developed using total interpretive structural modeling (TISM). Cross-impact matrix multiplication (MICMAC) analysis helped in classifying the factors on the basis of their driving and dependency power.FindingsAn extensive literature review and expert opinion helped in identifying eight enablers that can promote agility in higher education. The ability to sense the environment, organizational structure, adoption of ICT, organizational learning, human resource strategies, leadership, readiness to change and collaboration with the stakeholders were the eight factors identified. The structural model revealed leadership as the most crucial enabler followed by human resource strategies and organizational structure.Research limitations/implicationsThe model has incorporated and prioritized all the crucial drivers of agility that can help universities and colleges design, adopt and implement policies and practices that would facilitate agility.Originality/valueSo far, the research on agility in higher education has looked into each factor in isolation. This research provides a comprehensive list of the factors and establishes the interplay between the factors making this study new and original.

A framework to enhance agile manufacturing system

2017 ◽  
Vol 24 (2) ◽  
pp. 467-487 ◽  
Author(s):  
Rahul Sindhwani ◽  
Vasdev Malhotra
Keyword(s):  
Manufacturing System ◽  
Structural Model ◽  
Agile Manufacturing ◽  
Top Management ◽  
Management Support ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Model Hierarchy ◽  
Or Organization

Purpose The purpose of this paper is to identify and analyze the interactions among different enablers of agile manufacturing system (AMS). The existing enablers available in the past literature are scattered and not able to meet specific requirements of the customers. So, it becomes a necessity to encapsulate these enablers in appropriate proportions to enable traditional organizations to AMS. To fill this gap total interpretive structural modeling (TISM) and MICMAC analysis-based framework model have been developed to understand the mutual interactions between among the enablers. Design/methodology/approach Identification of enablers followed by application of TISM, which is an innovative version of ISM and MICMAC analysis, is used to study and analyze the mutual interactions between identified enablers. Findings The result reveals that top management support, organizational structure and information technology integration have strong driving power and weak dependence power and are at the lowest level in the TISM model hierarchy, while the outcome enablers of AMS have low-driving power but have high-dependence power. Research limitations/implications This model is developed on the basis of inputs from few experts and may not reflect the opinion of whole industry community. Practical implications Top management must stress on enablers having strong driving power for efficient implementation of AMS. Managers in the area of manufacturing may drive useful insights from the empirical study presented in this paper. Managers should plan an effective strategy for proper implementation of AMS which makes organization more agile, productive, competitive and profitable. Originality/value TISM-based framework structural model has been proposed for industry or organization which is a new effort for implementation of AMS.

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