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Idea of circular economy defies the classical “make-use-dispose” approach of linear economic model. In the context of health-care industry, it relies heavily on the supply chain practices implemented by industry stakeholders. The purpose of this study is to explore such relationships, study their structure and put it across for attaining sustainability at large.

This study is an empirical research conducted on 145 health-care firms. The collected data is analysed to develop structural and measurement model. The five constructed hypotheses are examined and tested through structural equation modelling.

The study illustrates the latent relationships that exist among the stakeholders involvement, sustainable supply chain practices, sustainable performance and circular economy for health-care industry. It is found that the adoption of sustainable supply chain practices improves health-care performance, which, in turn, have positive influence on circular economy.

The structural and measurement model is developed in the context of circular health-care economy. It can be validated or improvised by conducting similar research in other industry using different methods. This research work fulfils the long existing gap in research by offering a linkage between various constructs to achieve health-care circular economy. Based on the research results, future researchers can build theories of circular economy and sustainability for health-care industry.

The study attempts to study the supply chain ways to achieve circular economy for Indian health-care sector. It considered latent relationships among the set of constructs, which are needed for theory building at later stage.

The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention.

To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges.

This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector.

The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research.

The COVID-19 pandemic has had a significant and worldwide influence on healthcare delivery, and it has significantly increased the pace at which digital technology is being used. Blockchain, one of these developing digital technologies, is distinguished by a number of properties. This study focuses on a blockchain-enabled healthcare supply chain. The purpose of this work is to investigate how blockchain technology (BCT) benefits the performance of healthcare supply chain management (HSCM).

The present study is based on the empirical research. Blockchain Technology (BCT), Healthcare Sustainable Supply Chain Practices (HSSCP), Healthcare Supply Chain Performance (HSCP) and Stakeholders’ Involvement (SI) practices are identified from the literature review and hypotheses are framed to check their interrelationship. For testing of hypothesis, a questionnaire was developed. Data collection was done by healthcare professionals via Google docs. The IBM SPSS version 22.0 was used to analyze the data and IBM SPSS AMOS 22.0 software was used for the development of structural modal. The data was collected through the Google form from the stakeholders of healthcare sector and analyzed through Structural Equation Modelling.

This research is focused on adoption of BCT enabled Healthcare Sustainable Supply Chain to improve HSCP. From the result, it had been found that BCT is positively effecting the stakeholder's involvement (SI) and HSSCP practices. Cumulatively, they positively impact the performance of HSCP. From this study, it is found that adoption of BCT enabled Healthcare Sustainable Supply Chain succours to combat COVID-19 situation.

This study attempts to show the potential benefits of the adoption of BCT enabled HSSCP to improve HSCP.

The purpose of this paper is to evolve a framework for assessing the efficiencies of municipal solid waste management (MSWM) services.

Stochastic frontier analysis (SFA), a deterministic parametric frontier technique, has been employed for benchmarking of the MSWM services across municipalities in 22 cities in the state of Madhya Pradesh, India.

This paper reviews the solid waste management status and applies SFA to evaluate the relative performances of the services in terms of seven models, of which two models were found significant and acceptable based on the null hypothesis, significance of elasticity of variables, and an absence of data heteroskedasticity. The paper discusses the results in the context of various indicators, and finds that a large number of MSWM municipalities exhibit relative inefficiencies, implying that most municipalities can achieve higher levels of outputs by deploying fewer resources, thereby improving performances without necessarily increasing municipal spendings.

The work suggests some of the changes at the policy level that would affect the way in which solid waste is managed currently in India, and hence the work has social implications whereby the lives of a billion people will be improved through improved solid waste management services.

To the best of the author's information, advanced benchmarking techniques such as SFA have seldom been applied for evolving the efficiencies of solid waste management municipalities, primarily because the sanitation services are yet to take the shape of an industry in the developing world. Lack of availability of data and non‐transparent functions of these services prevent further exploration into estimating the inefficiencies of these service utilities. The paper will be of use to policy makers, solid waste managers, administrators, and sector regulators.

The authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.

SSCIs are identified by reviewing the extant literature and topic modeling. Further, they are evaluated based on existing SDGs and ranked using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Notably, the evaluation of indicators is a multi-criteria decision-making (MCDM) process within a fuzzy environment. The methodology has been explained using a case study from the automobile industry.

The case study identifies appropriate SSCIs and differentiates them among peer suppliers for gaining a competitive advantage. The results reveal that top-ranked sustainability indicators include the management of natural resources, energy, greenhouse gas (GHG) emissions and social investment.

The study outcome will enable suppliers, specialists and decision makers to understand the criteria that improve supply chain sustainability in the automobile industry. The analysis provides a comprehensive understanding of the competitive package of indicators for gaining strategic advantage. This proactive sustainability indicator selection promotes and enhances sustainability reporting while fulfilling regulatory requirements and increasing collaboration potential with trustworthy downstream partners. This study sets the stage for further research in SSCIs’ competitive strategy in the automobile industry along with its supply chains.

This study is unique as it provides a framework for determining relevant SSCIs, which can be distinguished from peer suppliers, while also matching economic, environmental and social metrics to achieve a competitive advantage.

In this paper, the objective is to perform mathematical modeling to optimize the steady-state availability of a multi-state repairable crushing system of a sugar plant using the evolutionary algorithm of Particle Swarm Optimization (PSO). The system availability is optimized by evaluating the optimal values of failure and repair rate parameters concerned with the subsystem of the system.

Mathematical modeling of the multi-state repairable system is performed to develop the first-order differential equations based on the exponential distribution of the failure and repair rates. These differential equations are recursively solved to obtain the availability under normalizing conditions. The availability of the system is optimized by using the PSO algorithm. The results obtained by PSO are validated by using the Genetic Algorithm (GA).

The availability analysis of the system concludes that the cane preparation (F1) is critical of the crushing system and the optimized availability of the system using PSO is achieved as high as 87.12%.

A crushing system of the sugar plant is evaluated as it is the main system of the sugar plant. The maintenance data associated with failure and repair rate parameters were analyzed with the help of maintenance records/logbook and by conducting personal meetings with maintenance executives of the plant. The results obtained in the paper helped them to plan maintenance strategies accordingly to get optimal system availability.

The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.

In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.

In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.

There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.

The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

This paper presents the performance analysis of the automatic ticket vending machine (ATVM) through the functioning of its different hardware and software failures.

Frequent failures in the working of ATVM have been observed; therefore, the authors of the paper intend to analyze the performance measures of the same. Authors have developed a mathematical model based on different hardware and software failures/repairs, which may occur during the operation, with the help of the Markov process. The developed model has been solved for two kinds of failure/repair rates namely variable failures (very much similar to real-time failure) and constant failures. Lagrange's method and Laplace transformation are used for the solution of the developed model.

Reliability and mean time to failure of the ATVM are determined. Sensitivity analysis for ATVM is also carried out in the paper. Critical components of the ATVM, which affect the performance of the same, in terms of reliability and MTTF are also identified.

A mathematical model based on different hardware and software failures/repairs of ATVM has been developed to analyze its performance, which has not been done in the past.

This study aims to explore the factors influencing the bottom of the pyramid (BOP) consumers’ adoption and usage intention towards mobile payment (m-payment) to achieve financial inclusion and sustainable development goals.

A qualitative research design is used to explore the enablers and inhibitors that influence BOP consumers’ m-payment adoption and usage intention. To collect the qualitative responses, semi-structured in-depth interviews with BOP respondents were conducted. The thematic analysis using the text mining technique will be used to analyse qualitative data for exploring the predominant factors affecting m-payment adoption intention and usage.

The results suggested awareness, social influences and self-efficacy as crucial enablers and privacy and security risks and vulnerability concerns as crucial inhibitors towards m-payment adoption and usage.

As a novel contribution to the BOP, financial inclusion, sustainable development goals and m-payment literature, this study unfolds several unknown perceived benefits and perceived sacrifices that influence the BOP consumers’ m-payment adoption intention and usage. The study’s findings help the government and banks formulate and implement strategies to achieve financial inclusion among BOP consumers.