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.

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.

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.

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.

Modeling the emergency health-care supply chains: responding to the COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md Kamal Hossain ◽  
Vikas Thakur ◽  
Sachin K. Mangla
Keyword(s):  
Health Care ◽  
Supply Chain ◽  
Theoretical Model ◽  
Structural Model ◽  
Health Care Facilities ◽  
Extensive Literature ◽  
Economic Implications ◽  
Content Type ◽  
Micmac Analysis ◽  
Health Care Supply

Purpose Due to the rapid surge in the number of COVID-19 cases in India, the health-care supply chain (HCSC) disruptions and uncertainties have increased manifold posing severe challenges to health-care facilities and significantly hampering the functioning of the health industry. This study aims to propose a hierarchical structural model of enablers of HCSC in the COVID-19 outbreak and identifies inter-relationships among them in the health-care market. Design/methodology/approach Enablers of emergency HCSC have been identified through extensive literature review and experts’ opinions. Subsequently, total interpretive structural modeling (TISM) and cross-impact matrix-multiplication (MICMAC) analysis have been implemented to determine the hierarchical inter-relationships among enablers and classify them according to their contribution to the overall system. Findings The research has identified and validated 15 enablers of the emergency supply chain in health-care businesses. The study resulted in a seven-level hierarchical structural model based on enabler’s driving and dependence powers. Further, the application of MICMAC analysis resulted in the classification of enablers into four groups, namely, autonomous, dependent, linkage and independent group. Research limitations/implications This study would help health professionals, policymakers and academia to implement the theoretical model constructed to alleviate the effect of COVID-19 by improving the HCSC performances in pandemic situations. This study has social and economic implications in terms of cost-effective and efficient delivery of care services in health emergencies. Originality/value The proposed theoretical model constructed is a new effort addressing the issues of HCSC in the COVID-19 crisis. Procedural implementation of TISM and MICMAC analysis in this study would help researchers to grasp concepts in a very lucid manner. The present study is one of the very few studies analyzing enablers in pandemic situations by implementing the TISM approach.

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.

Analysis of factors influencing AM application in food sector using ISM

2020 ◽  
Vol 15 (3) ◽  
pp. 919-932
Author(s):  
Arun Palaniappan ◽  
S. Vinodh ◽  
Rajesh Ranganathan
Keyword(s):  
Structural Model ◽  
Matrix Multiplication ◽  
Raw Material ◽  
Social Implications ◽  
Food Sector ◽  
Content Type ◽  
Micmac Analysis ◽  
Factors Influencing ◽  
Material Usage ◽  
Commercial Applications

Purpose The purpose of this paper is to report the analysis of factors influencing additive manufacturing (AM) application in the food domain. Design/methodology/approach Based on literature review, 16 factors are being considered in the study. Interpretive structural modelling is used as a modelling approach. The derived structural model indicates the dominant factors. Matriced’ impacts croises-multipication applique and classment (cross-impact matrix multiplication applied to classification) (MICMAC) analysis is being done to group the factors. Findings Based on the study, it has been found that raw material usage, the shelf life of food, demand for the food and accuracy are dominant factors. MICMAC analysis indicated that number of driving, dependent and linkage factors are 6, 4 and 4, respectively. Research limitations/implications In the present study, 16 factors are being considered. In future, additional factors could be considered to deal with advancements in the food domain. Practical implications The study has been executed in discussion with practitioners in AM, and hence derived inferences have practical validity. Food making has become more agile with 3D printer and has become sensitive to customer demand. Social implications Social implications are primarily highlighted by the aspect of controlling the exact amount of nutrients corresponding to the application of food. In certain commercial applications, people can customize their shape and ingredients to be injected into the food. Originality/value The development of a model for the analysis of factors influencing AM in the food domain is the original contribution of the authors.

Modelling and analysis of barriers affecting the implementation of lean green agile manufacturing system (LGAMS)

2019 ◽  
Vol 26 (2) ◽  
pp. 498-529 ◽  
Author(s):  
Rahul Sindhwani ◽  
Varinder Kumar Mittal ◽  
Punj Lata Singh ◽  
Ankur Aggarwal ◽  
Nishant Gautam
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Agile Manufacturing ◽  
Extensive Literature ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Top Managers ◽  
Discrete Manner ◽  
Practical Implications

Purpose Many types of research have already investigated the lean, green or agile manufacturing systems in a discrete manner or as combinations of two of them. In today’s competitive scenario, if industry wants to perpetuate its name in the market, then it has to supervene proper thinking and smart approach. Therefore, the combination of lean, green and agile manufacturing systems can provide better and beneficial results. The purpose of this paper is to discern the barriers to the combined lean green agile manufacturing system (LGAMS), understand their interdependence and develop a framework to enhance LGAMS by using total interpretive structural modeling (TISM) and MICMAC (Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement) Analysis. Design/methodology/approach This paper uses TISM methodology and MICMAC analysis to deduce the interrelationships between the barriers and rank them accordingly. A total of 13 barriers have been identified through extensive literature review and discussion with experts. Findings An integrated LGAMS has been presented that balances the lean, green and agile paradigms and can help supply chains become more efficient, streamlined and sustainable. Barriers are identified while referring to all three strategies to showcase the clear relevance. TISM models the barriers in different levels showcasing direct and important transitive relations. Further, MICMAC analysis distributes the barriers in four clusters in accordance with their driving and dependence power. Research limitations/implications The inferences have been drawn from a model developed on the basis of inputs from a small fraction of the industry and academia and may show variations when considering the whole industry. Practical implications The outcome of this research can contribute to bringing the change to the manufacturing systems used in most developing nations. Also, top managers considering adoption of LGAMS can be cautious of the most influential barriers. Originality/value A TISM-based model of the barriers to an integrated LGAMS has been proposed with evaluation of the influence of the barriers.

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.

Analysis of drivers for anti-food waste behaviour - TISM and MICMAC approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Archana Poonia ◽  
Shilpa Sindhu ◽  
Vikas Arya ◽  
Anupama Panghal
Keyword(s):  
Expert Opinion ◽  
Food Waste ◽  
Matrix Multiplication ◽  
Cultural Norms ◽  
Extensive Literature ◽  
Critical Factors ◽  
Content Type ◽  
Factors Affecting ◽  
Micmac Analysis ◽  
Effective Interventions

Purpose This study aims to identify and analyse the interactions among drivers of anti-food waste behaviour at the consumer level. By understanding the mutual interactions among the drivers, an effort is made to identify the most driving and most dependent drivers through the total interpretive structural modelling (TISM) approach. Modelling offers inputs to propose focused interventions for reinforcing the identified drivers of anti-food waste consumer behaviour using the theoretical lens of social practices theory. Design/methodology/approach A proposed model of factors affecting anti-food waste behaviour is arrived at to suggest the most effective anti-food waste behavioural interventions. The factors were identified through an extensive literature search. A hierarchical structure of identified factors has been developed using TISM and MICMAC analysis through expert opinion. Focused marketing strategies towards promoting the identified factors for encouraging anti-food waste behaviour were suggested further. Findings This study identifies nine drivers based on extensive literature review, brainstorming and expert opinion. The TISM hierarchical model portrays the most important and least important drivers of household anti-food waste behaviour. It establishes that fundamental knowledge and socio-cultural norms are the most critical factors to drive the consumers. Marketers can focus on designing effective interventions to enhance the essential knowledge of the consumers and orient the socio-cultural norms towards anti-food waste behaviour. Practical implications This study offers implications for practitioners, policymakers and cause-driven marketing campaigns targeting anti-food waste behaviour. It provides an indicative list of critical factors relevant to household food waste behaviour, which can be used to drive effective marketing campaigns to nudge anti-food waste behaviours. Originality/value The proposed food waste behaviour management model was developed through modelling technique (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis, and relating them to marketing interventions is a novel effort in the food waste domain.

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