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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.

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.

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.

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.

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.

This study aims to identify the key enablers for the adoption of Industry 4.0 (I4.0) in the automobile industry of India, which has been severely impacted by COVID-19. Adopting I4.0 will provide organizations greater flexibility and resilience during the COVID-19 pandemic.

Based on the literature review and experts’ opinions, 21 enablers were identified. Further, contextual relationships among the identified factors and a hierarchical digraph was developed by using the total interpretive structural modelling (TISM) technique. Finally, fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was conducted to classify the enablers into different categories based on their dependence and driving power.

The results indicate that top management support, clarity on government policy, strategic vision on I4.0 and development of new industrial policy are the most influential factors, with the highest driving power placed at the bottom of the TISM hierarchical model. Furthermore, agile workforce, smart HR practices and IT standardization and security are identified as linkage enablers with the most driving and dependency power.

The hierarchical TISM model and fuzzy MICMAC approach provide a comprehensive understanding of the I4.0 implementation process through a visual, logical structure to the managers. It will help the researchers and practitioners understand the contextual relationship among various enablers in fostering the I4.0 adoption process and digital reorganization in the automobile industry during the COVID-19 pandemic.

This study provides a holistic TISM hierarchical framework on I4.0 adoption that will elevate the next maturity level of innovation adoption and may act as a blueprint for automobile industries during the COVID-19 pandemic.

The purpose of this paper is to “identify”, “analyze” and “construct” a framework to quantify the relationships between several determinants of organizational preparedness for change in the start-ups during the COVID-19 emergencies.

Total interpretive structural modelling (TISM) is used to find characteristics that assist in analyzing the readiness or preparedness level before initiating a change deployment process in start-ups. A cross-impact matrix multiplication applied to classification (MICMAC) analysis is performed to determine the driving and dependent elements of change in start-ups.

From literature research and an expert interview, this study selected ten variables of change preparedness to explore inner interconnections and comprehend the inner connections factors. The findings depict that clarity of mission and goals, reward system, technological advancement and motivational readiness have been considered the most important readiness factor for deploying organizational change in start-ups during the COVID-19 emergencies.

This research will aid the management and researchers gain a better understanding of the factors that influence change preparedness. Constant observation of current changes in the start-ups and the external environment will aid in improving the quality of products or services provided by the start-ups during the COVID-19. The start-ups can use these criteria linked to change readiness. The priority of each element is determined using MICMAC analysis and ranking using the TISM technique, which assists start-ups in ordering the enablers from highest to lowest priority.

There is no research regarding factors influencing organizational readiness for change in start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to organizational readiness for change in start-ups. This gap inspired the present study, which uses the “Total Interpretive Structural Modelling (TISM)” technique to uncover change determinants and investigate hierarchical interconnections among factors influencing organizational readiness to change in start-ups during the COVID-19 emergencies.

This paper investigates adoption of Advanced Manufacturing Technologies (AMT) in India. AMT in India have been classified into three levels (simple – Level I, modest – Level II, and sophisticated – Level III) for the present analysis. A general conclusion can be reached that Indian firms we surveyed have high adoption degree of Level I technologies, are going to adopt moe Level II technologies, and are not yet ready to invest in Level III technologies. Factor Analysis is used to identify common components among 25 AMT that were surveyed. Out of these technologies, three are stand alone infrastructure technologies of computer network and therefore are treated individually. The remaining technologies can be nicely interpreted by four common factors: “expensive”, “social”, “production”, and “quality”. Discriminant Analysis is used to identify critical success factors that contribute significantly to the success of AMT adoption. The accuracy achieved is 81.1 per cent (7.9 per cent error rate) by using only four variables out of 19 critical success factors.These influential factors are pace of implementation, hands‐on training program, long‐term automation objectives, and external consultants. However, only pace of implementation has a positive effect on the success of AMT adoption, while both hands‐on training program and external consultants have negative effect, and the effect of long‐time automation objectives is small.

The pandemic crisis has resulted in global chaos that had caused massive disruption to the supply chain. The pharmaceutical industry, in particular, has been working tirelessly to ensure that they can cater to the people who need them. With restrictions being imposed to prevent the spread of the COVID-19 virus, the movement of raw materials required has been affected, thus creating the need for the procurement function to be innovative. This study proposes the application of Industry 4.0 concepts into the procurement activities of an organization to make it more resilient and efficient.

To study the intensity of the challenges, Total Interpretive Structural Modelling is used alongside the “Matrice des Impacts Croises Multiplication Appliquee a un Classement” (MICMAC) technique.

Resilience can be achieved through the collaboration between the organization and its network of suppliers. This is however easier said than done. High and unclear investments have been identified as the challenge that is taking a toll on all technological investments in the pandemic era. The study also shows that organizational inertia which is present in established and structured firms are a deterrent as well.

This study is based on the application of procurement 4.0 to ensure that pharmaceutical supply chains stay least affected since they are essentials. This study using a multi-criteria decision-making approach to prioritize the challenges. This will help practitioners make decisions faster.

This paper studies the textile supply chain tactical planning under demand fuzziness through considering environmentally friendly and social responsibility. Hence, carbon emission in textile production and transportation is considered along with supply chain profitability.

The authors present a fuzzy multi-objective mathematical optimization model with credibilistic chance constraints to determine the fabric procurement quantities and production plan under uncertainty. The solution procedure makes use of credibility measure and fuzzy aggregation operator to attain compromise solutions.

A trade-off among carbon emissions, social performance and supply chain total profit is conducted. The analyses indicate the importance of transportation costs and carbon emission while determining the supply chain's tactical plan.

The textile supply chain's social sustainability alongside carbon emissions of textile operations is contemplated to provide apparel production and distribution logistics planning under uncertainty. In doing so, the authors propose a hybrid credibility-possibility mathematical optimization model to determine a compromise solution for textile managers.

The purpose of this paper is to propose a novel hybrid approach to assess marketing-based flexibility with respect to its source factors, enablers and attributes.

The study demonstrates an application of a hybrid principal component analysis (PCA)-analytical hierarchical process (AHP)-multi-grade fuzzy approach (MFA) to measure marketing-based flexibility. Using PCA method, attributes, enablers and source factors of marketing-based flexibility were identified and a conceptual model was developed. AHP and MFA were used to compute marketing-based flexibility index.

The proposed approach measures existing level of marketing-based flexibility and therefore it identifies weak areas that should be taken care to improve flexibility.

The scope of the study is limited to plant level. The validity of the proposed approach is shown using a case study. For generalisation point of view, the application of this proposed approach should be investigated in a large number of firms in different industrial settings.

The study gives a reliable and valid method, which combines both statistical and MCDM techniques to measure existing level of flexibility and identify weak areas for flexibility improvement.

The findings provide insight into factors that should be worked upon to improve flexibility.

The purpose of this paper is to evaluate the enablers for improving the utilization level of advanced manufacturing technologies (AMTs) in Indian manufacturing industry.

In this paper, a large number of manufacturing organizations have been extensively surveyed, to ascertain the contributions made by success factors of AMTs towards realizing manufacturing performance improvements. The correlations between enablers of AMTs' utilization and manufacturing performance parameters have been evaluated and validated by employing various statistical tools.

The paper focuses upon significant contributions of AMTs' success factors namely: education and training of employees; interdisciplinary team formation; rewards and recognition to employees; and industry‐institute interaction, towards affecting improvements in manufacturing performance in Indian industry. The inter‐relationships between various AMTs' utilization success factors with the manufacturing performance parameters have been evaluated.

The study has been conducted only in Northern Indian manufacturing organizations.

The paper highlights the strong potential for enablers of AMTs' utilization in improving organizational performance parameters.

Marketing is all about understanding your consumers and giving them what they want. However, this process becomes more complicated in times of economic crisis and national slowdown. Consumers can become scattered and unpredictable in their behaviour, making it hard to understand what they want or need. At times like these, it is more important than ever to rely on qualitative market research to understand the views of consumers and managers. Thus, this study aims to look at the significant consumer challenges that arise during times of crisis and the marketing strategies that managers prefer to counter the crisis.

The data collected for this study involves an extensive literature review followed by personal interviews with industry experts. This study presents two separate models, indicating hierarchical relationships among consumer challenges during crisis and marketing strategies using the total interpretive structural modelling approach. Further MICMAC analysis (popularly known as cross-impact matrix multiplication) was also performed to assess each variable's driving and dependence power.

“Price sensitiveness” and “adaptive buying” result as driving factors with the highest driving and lowest dependence power, which further gives rise to other consumer behaviour challenges. Likewise, the most critical strategies are “information systems” and the formation of “crisis management teams” during a crisis. At the same time, other strategies have resulted as linkage and dependent factors and none as the autonomous factor.

This paper provides a systematic understanding of how a manager can understand the challenges consumers face during a crisis and suggests a powerful summary of strategies companies can implement to sail through a crisis.