Search results

Due to the increasing demand of customer and competitive market pressure, manufacturing organizations should be modernized in strategies, production operations, processes and its procedures to remain competitive. So, a flexible manufacturing system (FMS) was adopted by the manufacturing system to fight with competitive pressure. The purpose of this paper is to enhance the performance of manufacturing system, with a focus on its factors.

In this research, the ranking of the performance factor of FMSs is done by using multiple attribute decision-making (MADM) methods as multi-objective optimization on the basis of ratio analysis (MOORA) and preference selection index (PSI). Weights of attributes are defined by the AHP method.

Ranking of performance factor is done on the basis of six variables which affect three elements of performance of FMS, i.e. productivity, flexibility and quality. MOORA is applied in three ways such as the ratio-based, reference point and full multiplicative method. In the MOORA method, ranking was done considering weights of attributes and also without it. A PSI method is used to find the best factor among the factors. The results of these methodologies, i.e. MOORA and PSI, are same, i.e. productivity is the primary factor in the manufacturing system. The ranking is validated by the result of different methodology used in this research.

This research has evaluated the important factors and performance variables which can enhance the performance of manufacturing organizations. So, the manufacturing persons can focus on these to enhance its performance.

Combined MADM methods, i.e. MOORA and PSI methodologies, are used in this paper to deal with the ranking of performance factors of the FMS considering qualitative characteristics. These approaches show the conversion of a qualitative attribute to quantitative attributes by using fuzzy logic.

Advanced digitalization techniques combined with artificial intelligence and automated robotic systems have created “Smart” organizations resulting in a new revolution in the industrial production systems as Industry 4.0 (I4.0). The research is aimed to do a meticulous scanning of internal and external environment pertaining to I4.0 implementation in the manufacturing industry in India.

A survey was conducted among the manufacturing managers and information technology professionals about the factors affecting I4.0 application, and 20 such internal and external factors were identified. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were executed for factor analysis, and four dimensions in terms of strengths, weaknesses, opportunities and threats (SWOT) factors were determined from the variables. The analytical hierarchy process (AHP) methodology was then applied.

Results show that increased productivity and efficiency appeared to be the biggest strength of I4.0 while the biggest weakness is the need for specialized training and skills. The biggest opportunity is found to be increasing trust of customers in Internet transactions and employee resistance to adopting new technologies turned out to be the biggest threat.

Organizations will be able to evaluate the strengths, work upon weakness, exploit the opportunities and protect against external challenges and threats beforehand while implementing I4.0 technologies.

The four dimensions in terms of SWOT pertaining to manufacturing industry have been identified by collecting original data from the manufacturing industry, and AHP and CFA were then carried out to prioritize and verify them.

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.

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.

The results of the model and MICMAC analysis indicate that diet restriction, body pain and satisfaction with treatment are the top-level factors.

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.

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.

The purpose of this paper is to evaluate the interplay of various measures used by different governments around the world in combatting COVID-19.

The research uses the interpretative structural modelling (ISM) for assessing the powerful measures amongst the recognized ones, whereas to establish the cause-and-effect relations amongst the variables, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used. Both approaches utilized in the study aid in the comprehension of the relationship amongst the assessed measures.

According to the ISM model, international support measures have the most important role in reducing the risk of COVID-19. There has also been a suggestion of a relationship between economic and risk measures. Surprisingly, no linkage factor (unstable one) was reported in the research. The study indicates social welfare measures, R&D measures, centralized power and decentralized governance measures and universal healthcare measures as independent factors. The DEMATEL analysis reveals that the net causes are social welfare measures, centralized power and decentralized government, universal health coverage measure and R&D measures, while the net effects are economic measures, green recovery measures, risk measures and international support measures.

The study includes a list of numerous government measures deployed throughout the world to mitigate the risk of COVID-19, as well as the structural links amongst the identified government measures. The Matrice d'Impacts croises-multiplication applique and classment analysis can help the policymakers in understanding measures used in combatting COVID-19 based on their driving and dependence power. These insights may assist them in employing these measures for mitigating the risks associated with COVID-19 or any other similar pandemic situation in the future.

This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.

To determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.

This study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.

This study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.

According to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

The vision of Industry 4.0 concept is to create smart factories that will change the current processes of production and manufacturing system using smart machines to produce smart and intelligent products. The main aim of this research is to explore the enablers with regard to Industry 4.0 application in manufacturing industry in India as the available literature shows that manufacturing sector is still doubtful about the implementation of Industry 4.0.

Seventeen enablers that can affect the adoption of Industry 4.0 in the manufacturing industry in India have been explored through an extensive review of available literature and viewpoints of industry and academic experts. Total Interpretive Structural Modelling methodology (TISM) has been used to evaluate the interrelationships among these factors. A TISM model has been developed to extract the key enablers influencing Industry 4.0 adoption.

The result shows that Internet facility from government at reduced price, financial support and continued specialized skills training are the major enablers as they have strong driving power.

Proper understanding of these enablers will help the managers and policymakers to explore the impact of each enabler on other enablers as well as the degree of relationships among them and to take concrete steps so that Industry 4.0 can be implemented successfully in the manufacturing sector in India.

This study is pioneer in exploring the enablers Industry 4.0 which is the most advanced concept that has the capability to change the future of Indian manufacturing sector if implemented judiciously and cautiously.

The purpose of this paper is to identify the flexible manufacturing system performance variables and analyze the interactions among these variables. Interpretive structural modeling (ISM) has been reported for this but no study has been done regarding the interaction of its variables. Therefore, fuzzy TISM (total ISM) has been applied to deduce the relationship and interactions between the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

Fuzzy TISM and fuzzy MICMAC analysis have been applied to deduce the relationship and interactions among the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

In total, 15 variables have been identified from the extensive literature review. The result showed that automation, use of automated material handling, an effect of tool life and rework percentage have high driving power and weak dependence power in the fuzzy TISM model and fuzzy MICMAC analysis. These are also at the lowest level in the hierarchy in the fuzzy TISM model.

Fuzzy TISM model has been suggested for manufacturing industries with fuzzy MICMAC analysis. This proposed approach is a novel attempt to integrate TISM approach with the fuzzy sets. The integration of TISM with fuzzy sets provides flexibility to decision-makers to further understand the level of influences of one criterion over another, which was earlier present only in the form of binary (0, 1) numbers; 0 represents no influence and 1 represents influence.

Medical tourism encourages the traveling of patients, expert healthcare professionals and promotes cross-border trade in healthcare services. The Indian medical tourism sector is facing new challenges as well as certain ethical and legal issues because of continuous market changes and patient’s requirements while at the same time advancements in current health services have also been observed. It is therefore very important to understand and address the issues of the medical tourists. The purpose of this paper is to evaluate the important factors which can make India an affordable medical tourism destination.

In this paper, the factors influencing Indian medical tourism sector have been explored by conducting literature review, they are ranked according to the results of a questionnaire-based survey and further analyzed by using the interpretive structural modeling (ISM) approach. The mutual relationships between these factors were identified to develop an ISM model so as to find out the important factors which can make India an affordable place for medical tourism.

The results of the survey and the model show that cost of medical procedures, facilitation, and care, the infrastructure of Indian hospitals, clinical excellence and the competence of doctors and staff are the top level factors.

It is very important to address the concerns of the patients coming to a developing country like India for availing medical services. This research has evaluated the important factors which can make India an affordable medical tourism destination.

This research assesses the effects of globalization on delivery of healthcare services in India by conducting critical analysis of the medical tourism industry by collecting original data from the international patients coming to India for different types of medical procedures so that a comprehensive model can be prepared which will help the hospitals and policymakers to improve the processes related to medical tourism.

Lean concept is implemented in healthcare organizations, as it deals with improvement processes so that best services may be provided to the patients and competitive advantage may be achieved. The purpose of this paper is to evaluate the important factors which influence implementation of lean principles in the healthcare industry.

The factors influencing lean implementation in the healthcare industry have been determined through literature review and results of a survey where questionnaires were distributed among 325 healthcare professionals. Fuzzy Interpretive Structural Modeling (FISM) approach has been used to analyze the interrelationships among these factors. A FISM model has been developed to extract the key factors influencing lean implementation.

Results of the survey and model show that lean leadership, professional organizational culture and teamwork and interdepartmental cooperation are the top level factors. Clarity of organizational vision, communication of goals and results, follow up and evaluations are the factors with strong driving as well as strong dependence power. Even a slight action taken on these factors will have a significant impact on other factors.

The healthcare professionals and managers can acquire information from the drive power dependence matrix so that they can thoroughly understand the relative importance, interdependencies and relationships among these factors. The model will help in determining the hierarchy of various actions and activities which may be taken by the management for managing the factors that remarkably affect the lean management in hospitals.

In this paper, only 15 variables appropriate for the Indian healthcare industry have been identified. The model developed in the present research has not been validated statistically which can be done by structural equation modeling (SEM).

Though there are various studies which depict that lean principles have been implemented successfully in various industries, there are few studies specifying the application of lean principles in healthcare sector in India. This paper is an attempt to identify various factors which are important for application of Lean concept in the healthcare sector.

Tuberculosis (TB) continues to c-exist with humans despite many TB control programs and elimination strategies. This depicts that some barriers are not allowing achieving the desired results. The current study aims to focus on identification and ranking of such barriers to facilitate TB control programs in developing countries.

In the present study, 13 barriers that can influence success rate of TB elimination strategies have been recognized with an in-depth assessment of related literature and opinions of specialists from medical industry and academic world. The interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) techniques have been employed for the ranking of barriers.

Based on driving power of barriers, the study coined that underinvestment is a major barrier followed by poor implementation of government policies and programs, poverty and poor primary health care infrastructure.

The findings may guide healthcare service providers and researchers in analyzing the barriers and understanding the necessity of further advancements to decrease the count of already existing and incident cases.

Policy- and decision-makers may utilize the information on dependence and driving power of barriers for better planning and effective execution of TB control strategies.

Although a lot of literature is available on different barriers that are affecting success of TB strategies, the current study analyzes all the key barriers collectively for the prioritization of barriers.