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Effective total quality management (TQM) practices rely on the accurate classification of critical success factors (CSFs). The impact matrix cross-reference multiplication technique for classification (MICMAC) or/and fuzzy MICMAC (FMICMAC) can be used to identify key factors in the complex set. However, TQM includes both “hard” and “soft” factors, limiting application of the traditional MICMAC/FMICMAC method.

Previous literature on TQM was reviewed, CSFs were identified, and factors were sorted into soft and hard categories. The combined fuzzy integration and dual-aspect MICMAC (fuzzy dual-aspect MICMAC approach) was then applied to identify, cluster and prioritize the CSFs of TQM.

A total of 20 factors (10 soft and 10 hard) were identified and isolated to assess the manufacturing- and service-related TQM practices of the Pearl River Delta Region of China. Seven driver factors and one linkage factor emerged as the key CSFs that managers should prioritize.

A major limitation of this study is the dependency of the results on the definitions of linguistic labels. If the linguistic definitions of TQM CSFs do not closely correspond to the expert opinion data, then the analysis results may be inaccurate. Additionally, although expert opinions are utilized in the proposed method for comprehensive assessments, these opinions may influence the final results due to their inherent subjectivity.

A novel fuzzy dual-aspect MICMAC approach was developed to identify and classify CSFs for optimal TQM practices. This approach allows clustering of CSFs so that decision-makers can prioritize factors according to their dependence and driving powers. Practitioners should concentrate on the CSFs with higher driving powers for successful TQM.

The purpose of this paper is to present an innovative operational proposal for measuring the intellectual capital (IC) of higher education institutions (HEIs) through a strategic prospective lens of analysis.

After providing a literature review on the methods for measuring IC that focuses on the organisational IC of HEIs, four case studies applied to Portuguese HEIs are presented, using a matrix of cross-referenced impacts – multiplications applied to a classification (MICMAC) approach.

The empirical findings reveal how human capital, structural capital and relational capital make up the core components and provide a fairly diversified list of the measurement indicators for the operational evaluation of the IC of HEIs.

It contributes into the literature of strategic prospective analysis of HEIs by: analysing the measurement systems for the organisational IC interrelated with HEIs; identifying the key components to the organisational IC of HEIs and their respective measurement indicators; and draufting a new method for operationally implementing organisational IC through the systematic application of the components and indicators identified.

Through an innovative vision, the present study reconciles and systematically structures the methods already proposed by other authors before presenting an innovative operational approach and an alternative to the already existing methods. In addition, the structure of this proposal itself enables HEIs to choose from among the various indicators proposed for IC, correspondingly those that best align with the type of institution under evaluation.

The failure of a bridge is a complex phenomenon due to the involvement of a large number of factors. Bridge failure cannot be attributed to a single cause due to the intricacies involved in the relationship among the factors causing failure. The aim of this paper is to identify the various factors causing bridge failure and show an interdependence/relationship among them.

Interpretive structural modeling (ISM), which is a tool of structural analysis for interactive learning, has been used to develop a hierarchical structure of the phenomenon of bridge failure showing the interrelationship of the causative factors. Impact Matrix Cross Reference Multiplication Applied to a Classification (MICMAC) tool has been used to quantify and classify the factors for bridge failure on the basis of their influence and dependence.

In total, 14 factors, which may contribute to the failure of a bridge, have been identified through the literature survey and interaction with bridge experts. Even though all the 14 factors are important, the authors have found that the environmental factor has the maximum influence, while the age factor is the most dependent of all factors.

The proposed research utilizes the ISM and MICMAC tools and thus has all the limitations of structural modeling. The application of structural equation modeling in the civil engineering field is limited, but it is hoped that a similar approach can be applied in other areas of engineering.

The factors causing bridge failure have been identified and hierarchical interdependence model has been arrived at by using ISM. On the basis of MICMAC analysis, the factors have also been classified into three categories, namely, influent, autonomous and dependent. The study reaffirms the importance of all factors in the overall performance of a bridge.

Green lean six sigma (GLSS) is a sustainable development approach that leads to improved patient care with improved safety and quality of service to patients. This study aims to identify, study, model and analyze GLSS success factors for the Indian health-care facility.

Interpretive structural modeling (ISM) and Impact Matrix Cross-Reference Multiplication Applied to a Classification analyses have been used to understand the hierarchical structure among the GLSS success factors. This enabled the development of dependency relationships between success factors, in particular, which factors support the development of other factors.

Specifically, this study found that the success factors “commitment of management” and “financial availability” are the most critical to GLSS implementation success, as they support the development of all other success factors. Meanwhile “embedding sustainable measures at each stage of the service”; “the capability and effectiveness of real-time data collection”; and “feedback and corrective actions” most directly support the GLSS implementation in the health-care facility and serve as the final indicators of implementation progress.

The major implication of this research work lies in suggesting a direction for practitioners to execute the GLSS approach through a systematic understanding of classification and structural relationships among different enablers. This study also facilitates health-care managers to explore different GL wastes in hospitals and challenges to sustainability pursuits in health-care that assist in an organization’s efforts toward sustainable development.

This research work is the first of its kind that deals with the identification and analysis of the prominent factors that foster the inclusive implementation of GLSS within the health-care facility.

The development and application of systematic procedures enabling Kaizen methodology development are still very rare, especially in the service industry. The purpose of this paper is to suggest a set of innovative improvement methodologies of service quality to enhance the extent and depth of Kaizen in the service domain.

Combining the importance-performance analysis (IPA) method with impact matrix cross-reference multiplication applied to a classification (MICMAC), which is more convenient in calculation and application, and Fuzzy theory not only effectively integrates opinions of multiple experts, but also improves the conventional MICMAC method, which can only make binary judgment on the mutual influence of elements, by making it able to judge the degree of influence, thereby delivering more accuracy in evaluating the results.

This innovative method which integrates Fuzzy-MICMAC with IPA displayed its usability in the analysis and research of a certain university library on service quality, and significant differences were found on comparing it with the conventional IPA method. The comparison and suggestion were proposed.

The number of the experts involved and samples under investigation were limited, hence findings were not comprehensive enough and cannot be generalized. This sets the direction for future research.

This proposed method helps in overcoming the lack of systematic procedures enabling Kaizen methodology for service quality. It has the advantage of integrating existing tools and models effectively, which not only improve the shortcomings of the conventional methods, but also result in more convenient calculation and application, to promote Kaizen in service domain.

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.

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.

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.

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.

Material extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors.

A group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process.

The derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system.

The development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.

This study aims at modelling the enablers of dairy supply chain (DSC) in Indian context.

Interpretive structural modelling (ISM) approach has been used to model the enabler of dairy supply chain. The opinion has been taken from the industry experts and experienced academicians. Further, Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) used to classify the enablers based on driving and dependence power.

Findings show that stakeholder trust and top management support/leadership are the very crucial enablers in dairy supply chain; they are at a lower level of hierarchical structure and work as primary enablers to development of DSC. While customer satisfaction and financial performance are at top of the digraph, it shows these enablers are the outcome of a smooth supply chain. The MICMAC analysis suggests that the identified enablers are largely classified into dependent and independent enablers; there are no autonomous enablers in the dairy supply chain.

The study can aid businesses in the dairy processing industry in managing demand fluctuations, enhancing product quality, implementing effective information systems and adapting procedures, thereby enhancing supply chain performance.

There is very limited study on enablers of the dairy supply chain in general, while in the Indian context, there is no specific study on modelling the enablers of dairy supply chain.

The purpose of this paper is to provide the empirical evidence supporting the existence of a multi-level hierarchical TQM model showing the structural inter-relationships among a total of 16 TQM variables (i.e. drivers, enablers and outcomes).

The set of identified TQM variables is the product of an in-depth review of the literature, and a robust reiterative process of verification and validation. Inter-relationships among the TQM variables were subjected to the scrutiny of a panel of experts, and were used as a basis for developing a web-based survey to explore the existence as well as strength of the structural relationship between each and every pair of the identified variables using interpretive structural modelling and MICMAC (Impact Matrix Cross-Reference Multiplication Applied to a Classification).

TQM variables were classified and clustered based on their influence and dependence on each other. Variables such as commitment by top management and customer satisfaction appear to have a strong chance to affect change, whereas variables such suppliers and competitors are very dependent on, and sensitive to, the evolution of the influent variables.

The paper demonstrates a multi-level TQM model encompassing all identified TQM drivers, enablers, and outcomes. The paper not only addresses a gap in the relevant literature (reduces the evidence scarcity about the hierarchical nature of TQM variables), but also gives insights into the variables with most driving power needing greater management attention.

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.

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.

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.

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.

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.