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.

A framework for Crosby’s quality principles using ISM and MICMAC approaches

2019 ◽  
Vol 32 (2) ◽  
pp. 305-330 ◽  
Author(s):  
Nishant Agrawal
Keyword(s):  
Manufacturing Industry ◽  
Quality Measurement ◽  
Total Quality ◽  
Supervisor Training ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Initial Stage ◽  
Recognition Quality ◽  
Quality Principles

Purpose The purpose of this paper is to examine Philip B. Crosby’s 14 quality principles and analyze the interaction between them. Hitherto no research has been published on the implementation of total quality management (TQM) using Crosby’s 14 principles. To fill this gap, interpretive structural modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) analysis have been designed to prioritize, sequence and categorize variables to find both the dependence and driving power of these variables. Design/methodology/approach At the initial stage experts from industry as well as from academia were contacted to provide an input for ISM methodology and examine interactions between identified variables. In this approach, interpretations of the interrelationships among variables have been discussed, whereas MICMAC analysis is used to discover dependence and driving power. Findings The results of the investigation revealed that “Management Commitment,” “Quality Improvement Team,” “Quality Awareness,” “Supervisor Training,” “Goal Setting” and “Cost of Quality Evaluation” are strategic requirements; “Corrective Action,” “Zero Defects Day” and “Error Cause Removal” are tactical requirements. “Recognition,” “Quality Measurement,” “Quality Councils” and “Do It Over Again” are operational requirements for TQM applications. Originality/value ISM is used as a part of this research to provide valuable insights into interrelationships among Crosby’s quality principles through a systematic framework. The research opens up a new focus area on the implementation of TQM for services as well as for the manufacturing industry.

Modeling the performance measures of world class manufacturing using interpreting structural modeling

2015 ◽  
Vol 10 (1) ◽  
pp. 4-22 ◽  
Author(s):  
Abhijeet Keshaorao Digalwar ◽  
Anil Jindal ◽  
Kuldip Singh Sangwan
Keyword(s):  
Performance Measures ◽  
Model Performance ◽  
Structural Modeling ◽  
Decision Makers ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Practical Applications ◽  
Manufacturing Organizations ◽  
World Class ◽  
World Class Manufacturing

Purpose – The purpose of this paper is to study the performance measures of world class manufacturing (WCM) and to establish relationship among them using interpretive structural modeling (ISM). Design/methodology/approach – The research paper presents a blend of theoretical framework and practical applications. In the paper, 16 performance measures are identified from literature survey and experts’ opinion, and then these are validated by questionnaire survey in India. Finally, ISM is used to obtain structural relationship among these performance measures of WCM. Findings – The results of the survey and the ISM methodology have been used to evolve the mutual relationships among these performance measures. Practical implications – The adoption of such an ISM-based model on WCM performance measures in manufacturing organizations would help managers, decision-makers and practitioners of WCM in better understanding of these performance measures and to focus on appropriate performance measures while implementing WCM in their organizations. Originality/value – Performance measures are of paramount importance for the implementation of WCM practices. Knowing the key performance measures and relationship among them can help many organizations to implement WCM practices. It is one of the foremost attempts to model performance measures of WCM. The paper provides useful insights into the WCM practitioners, consultants and researchers.

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.

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.

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.

Modeling the key barriers to lean construction using interpretive structural modeling

2017 ◽  
Vol 12 (4) ◽  
pp. 652-670 ◽  
Author(s):  
Sorokhaibam Khaba ◽  
Chandan Bhar
Keyword(s):  
Structural Equation ◽  
Resistance To Change ◽  
Structural Modeling ◽  
Lean Construction ◽  
Equation Modeling ◽  
Interpretive Structural Modeling ◽  
Lean Implementation ◽  
Content Type ◽  
Micmac Analysis ◽  
Relationship Of

Purpose The purpose of this study is to identify and analyze the key barriers to lean implementation in the construction industry using interpretive structural modeling (ISM) and Matrice d’ Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) analysis. Design/methodology/approach In this study, 13 barriers to lean construction (LC) have been identified through extensive review of literature and subsequently eliciting expert opinions. A proper hierarchy and contextual relationship of the barriers have been developed using ISM, and based on the driving and dependence power of the barriers, three groups of barriers have been found using MICMAC analysis. Findings Cultural differences are found to be the most important barrier to LC, whereas employees’ resistance to change and lack of performance measurement systems are the least significant barriers. Research limitations/implications The work is limited to literature review and experts’ opinion, and the model may be tested using structural equation modeling to verify the relationship of the barriers. Practical implications This ISM-based model would help the decision-makers, researchers and practitioners to prioritize and manage these barriers by better utilizing their resources for eliminating or minimizing the barriers to lean implementation. Originality/value The study of barriers to LC through an ISM-based model and the classification of barriers is a new attempt in the field of construction.

Enablers of halal food purchase among Muslim consumers in an emerging economy: an interpretive structural modeling approach

2020 ◽  
Vol 122 (7) ◽  
pp. 2273-2287 ◽  
Author(s):  
Waseem Khan ◽  
Asif Akhtar ◽  
Saghir Ahmad Ansari ◽  
Aruna Dhamija
Keyword(s):  
Purchase Intention ◽  
Strong Dependence ◽  
Structural Modeling ◽  
Marketing Strategies ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Food Purchase ◽  
Micmac Analysis ◽  
Halal Food ◽  
Effective Product

PurposeThis study aims at identifying a set of determinants that affect halal food purchase intention and measures the relative ranks of these determinants in purchasing halal food among Muslim consumers in India.Design/methodology/approachInterpretive structural modeling (ISM) approach has been employed in the research, which is an expert opinion-based approach. The opinions of experienced academicians and marketing professionals have been recorded for reaching to the conclusions. Matrice d' impacts croises multiplication appliqué an classement (MICMAC) analysis has also been applied to examine the driving and dependent power of these determinants.FindingsDriver power–dependence matrix reveals that although knowledge of halal and attitude are weak drivers, yet they are strongly dependent upon other determinants. These two variables are at the top of the ISM digraph hierarchy. Food safety and halal labeling have strong driving power, as well as strong dependence. Three determinants, namely brand origin, religiosity and price, have strong driving powers and weak dependence. These variables lay at the bottom level of the ISM model.Practical implicationsThis study provides a better understanding of the determinants of halal food purchase intention. This will help the marketers for making appropriate and effective product design and other marketing strategies suited to the needs of the consumer.Originality/valueThis is the first study that examines the interrelationships between determinants and relative rank of these determinants in halal food purchase, using ISM approach and MICMAC analysis.

Competitiveness of Electronics manufacturing industry in India: an ISM–fuzzy MICMAC and AHP approach

2018 ◽  
Vol 22 (1) ◽  
pp. 88-116 ◽  
Author(s):  
Manoj Kumar Singh ◽  
Harish Kumar ◽  
M.P. Gupta ◽  
Jitendra Madaan
Keyword(s):  
Manufacturing Industry ◽  
Matrix Multiplication ◽  
Structural Modeling ◽  
Electronics Manufacturing ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Industry Level ◽  
Fuzzy Micmac ◽  
The Government ◽  
Practical Implications

PurposeThe purpose of this paper is to identify and build a hierarchy of the factors influencing competitiveness of electronics manufacturing industry (EMI) at the industry level and apply the interpretive structural modeling, fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á UN Classement (i.e. the cross-impact matrix multiplication applied to classification; MICMAC) and analytic hierarchy process (AHP) approaches. These factors have been explained with respect to managerial and government policymakers’ standpoint in Indian context. Design/methodology/approachThis study presents a hierarchy and weight-based model that demonstrates mutual relationships among the significant factors of competitiveness of the Indian EMI. FindingsThis study covers a wide variety of factors that form the bedrock of the competitiveness of the EMI. Interpretive structural modeling and fuzzy MICMAC are used to cluster the influential factors of competitiveness considering the driving and dependence power. AHP is used to rank the factors on the basis of weights. Results show that the “government role” and “foreign exchange market” have a significantly high driving power. On the other hand, the “capital resource availability” and “productivity measures” come at the top of the interpretive structural modeling hierarchy, implying high dependence power. Research limitations/implicationsThe study has strong practical implications for both the manufacturers and the policymakers. The manufacturers need to focus on the factors of competitiveness to improve performance, and at the same time, the government should come forward to build a suitable environment for business in light of the huge demand and frame suitable policies. Practical implicationsThe lackluster performance of the industry is because of the existing electronics policies and environmental conditions. The proposed interpretive structural modeling and fuzzy MICMAC and AHP frameworks suggest a better understanding of the key factors and their mutual relationship to analyze competitiveness of the electronics manufacturing industry in view of the Indian Government’s “Make in India” initiatives. Originality/valueThis paper contributes to the industry level competitiveness and dynamics of multi-factors approach and utilize the ISM–fuzzy MICMAC and AHP management decision tool in the identification and ranking of factors that influence the competitiveness of the EMI in the country.

A research note on the article of “Quality framework in education through application of interpretive structural modeling”

2019 ◽  
Vol 31 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Kosar Roshani ◽  
Mohammad S. Owlia ◽  
Mohammad H. Abooie
Keyword(s):  
Structural Modeling ◽  
Research Note ◽  
Administrative Staff ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Reachability Matrix ◽  
Counter Example ◽  
Correct Analysis ◽  
Quality Framework ◽  
Design Characteristics

Purpose This paper is a research note on “Quality framework in education through application of Interpretive Structural Modeling” (Sahney, S., Banwet, D.K. and Karunes, S. (2010), Quality framework in education through the application of Interpretive Structural Modeling: an administrative staff perspective in the Indian context, The TQM Journal, 22(1)). Sahney et al. applied Interpretive Structural Modeling (ISM) to prioritize, sequence and categorize elements critical to quality management in education; however, it seems that the final reachability matrix, and consequently the results, may not be true as some transitivity was not incorporated. So, the purpose of this paper is to apply a method to develop a transitive and compatible reachability matrix. Design/methodology/approach A counter-example was used to show that the final reachability matrix was incorrect, and then, based on Warfield’s studies, a transitive and compatible reachability matrix was developed directly. Findings As high priority should be placed on tackling the design characteristics which have a high driving power and thus possessing the capability to influence other elements, the correct analysis of ISM and its consequences is important to the priorities. The results from this study differed from the results of the Sahney et al. to a large extent. According to their analysis, all attributes were in the linkage area which has high driving power and high dependence. However, the authors reached different values for the relative importance and the interdependencies among the elements resulting in three different clusters. Originality/value This note clarifies and corrects the way the ISM methodology can be applied.

Healthcare waste management: an interpretive structural modeling approach

2016 ◽  
Vol 29 (5) ◽  
pp. 559-581 ◽  
Author(s):  
Vikas Thakur ◽  
Ramesh Anbanandam
Keyword(s):  
Waste Management ◽  
Structural Modeling ◽  
World Health ◽  
Interpretive Structural Modeling ◽  
Indian States ◽  
Content Type ◽  
Healthcare Waste ◽  
Micmac Analysis ◽  
Healthcare Waste Management ◽  
Health Organization

Purpose – The World Health Organization identified infectious healthcare waste as a threat to the environment and human health. India’s current medical waste management system has limitations, which lead to ineffective and inefficient waste handling practices. Hence, the purpose of this paper is to: first, identify the important barriers that hinder India’s healthcare waste management (HCWM) systems; second, classify operational, tactical and strategical issues to discuss the managerial implications at different management levels; and third, define all barriers into four quadrants depending upon their driving and dependence power. Design/methodology/approach – India’s HCWM system barriers were identified through the literature, field surveys and brainstorming sessions. Interrelationships among all the barriers were analyzed using interpretive structural modeling (ISM). Fuzzy-Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis was used to classify HCWM barriers into four groups. Findings – In total, 25 HCWM system barriers were identified and placed in 12 different ISM model hierarchy levels. Fuzzy-MICMAC analysis placed eight barriers in the second quadrant, five in third and 12 in fourth quadrant to define their relative ISM model importance. Research limitations/implications – The study’s main limitation is that all the barriers were identified through a field survey and barnstorming sessions conducted only in Uttarakhand, Northern State, India. The problems in implementing HCWM practices may differ with the region, hence, the current study needs to be replicated in different Indian states to define the waste disposal strategies for hospitals. Practical implications – The model will help hospital managers and Pollution Control Boards, to plan their resources accordingly and make policies, targeting key performance areas. Originality/value – The study is the first attempt to identify India’s HCWM system barriers and prioritize them.

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