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The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

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.

The purpose of this paper is to make a performance model of a shoe upper manufacturing unit of a shoe manufacturing industry by computing both the availabilities, i.e. time dependent system availability (TDSA) and the long‐term availability.

The present work is carried out by developing performance model based on Markov birth‐death process. The unit consists of four subsystems. The first order governing differential equations are derived using the mnemonic rule and further solved by adaptive step‐size control Runge‐Kutta method to calculate the TDSA, while the long‐term availability is calculated using normalizing condition, initial boundary conditions and recursive method. Both the availabilities are considered for system's performance criterion.

The subsystem A, i.e. sewing machine is the most critical from maintenance point of view, which has more impact on the system's performance as compare to other subsystems. The repair priorities of other subsystems have also been proposed.

These methods can also be used to find out the performance of other manufacturing industries.

The results of the present work are very useful for finding the critical subsystem and its effect on the system performance in terms of availability. Further, based on findings the maintenance priorities of various subsystems can be decided.

The purpose of this study is to develop a structured hierarchical interrelationship-based model to evaluate the critical failure factors (CFFs) that affect the sustainable Lean Six Sigma (SLSS) framework implementation in a healthcare organization. Further, solution approaches have been provided that guide to eliminate them.

The CFFs has been identified through empirical study and clustered into six major categories for their better understanding. The interrelation among CFFs has been developed through total interpretive structural modeling (TISM) and classifies the nature using MICMAC technique. Further, prioritized the CFFs based on its driving and dependents power. The methodology enabled the decision-makers, practitioners to systematically analyze the CFFs and develop a structural model for implementing SLSS in the healthcare environment.

A total of 14 leading CFFs have been identified, and 7-level structured interrelationship-based model has been formed. The experts have provided the solution approach after careful analysis of the developed model. Based on the analysis, it was observed that the significant CFFs affect the deployment of the SLSS framework in healthcare organizations.

The structured model and methodological approach have been tested in a healthcare organization. In the future, the approach can be applied in the different service sectors.

The present study has been conducted in a real-time industrial problem. The practitioners, decision-makers and academicians expressed the usefulness of methodology for understanding the CFFs interrelation and their effect on SLSS implementation. This study also guides decision-makers to systematically tackle related problems.

The development of a structured CFFs based model for SLSS framework implementation using the integrated TISM-MICMAC with a detailed solution approach is a unique effort in a healthcare environment.

This paper aims to quantify the trend of variables used for building a theoretical model with the help of analysing bibliometrics data of inventory management (IM) and Total Quality Management (TQM) practices and their effects on firm performance.

Scopus' research database and the Web of Science (WoS) (including Elsevier, Emerald Group Publishing, Taylor and Francis, Wiley, IEEE, Informs and SAGE) were used to find relevant articles. The articles, review papers and conference proceedings were screened from 1993 to 2021. The articles were analysed to explain the different types of IM practices, TQM practices and their effect on the firm's performance. Thematic analysis was done using a bibliometric package of “R” (Biblioshiny) and VOSviewer to identify the key trends, approaches and research agenda.

The research covered 28 years of publications and summarised 497 articles, review papers and conference papers. Researchers concluded that IM highly correlates with the inventory turnover ratio and has no relation to firm performance. Further, TQM positively affects firm performance, but integrating IM and TQM will be a research scope for future study as none of the researchers previously covered this.

Due to the systematic literature review (SLR) and Bibliometric review, the study is limited to the Scopus and WoS (including Elsevier, Emerald Group Publishing, Taylor and Francis, Wiley, IEEE, Informs and SAGE) databases.

Being the research on a bibliometric and systematic review, relating IM practices with TQM practices would be the novelty of this paper.

Humanitarian supply chain management (HSCM), operating in a complex environment, needs to be agile and robust. The advent of digital technologies has revolutionized HSCM operations, and thus, this study identifies and evaluates key drivers of artificial intelligence (AI) incorporation in HSCM.

In total, 20 key drivers were identified through a review of the relevant extant literature and finalized with experts’ inputs using a Likert scale survey. With a Kappa analysis, these drivers were classified into four groups: technical (T), organization (O), human (H) and institution (I). An integrated multi-criteria decision-making (MCDM) method of the Fermatean fuzzy set (FFS) analytic hierarchy process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) was used to rank the key drivers and explore their causal interrelationships.

Improved performance output, organizational preparedness, user acceptance and continued support, guarantee of job security for technologically semi-skilled workers and government support are the five key drivers of AI incorporation in HSCM.

This study evaluates the key drivers of AI integration in HSCM with FFS-AHP-DEMATEL.

Theorising from a resource-based view perspective, the intersection of supply chain management and the use of information technology (IT) has been investigated in this study. This paper aims to investigate supply chain performance (SCP) as an essential outcome of the use of IT and explores the effect of supply chain collaboration (SCC) on SCP. In addition, volume uncertainty (VU) has been explored and tested to establish whether various associated uncertainties can be mitigated when the use of IT is involved.

A sample of 121 senior executives from agri-tech firms was collected by travelling and meeting the executives in person in various states of India. Structural equation modelling was used to test the hypothesized relationship of VU to SCP via the use of IT and SCC.

The results show that VU significantly impacts SCC via the use of IT and SCP via SCC. The use of IT positively and significantly impacts SCP via SCC.

Witnessing the potential benefits of the emerging use of IT in the uncertainty reduction as reported in this study, agri-tech firms operating in emerging rural and agricultural economies can enhance SCC to improve SCP.

This study unfolds how risks in agricultural supply chains (ASCs) sourced because the VU can be mitigated through the use of IT and SCC to influence SCP in rural agricultural and developing economies.

VU at agri-tech firms and farmers is a ground reality that has led to an inability to plan and prepare, resulting in wastages and disruptions in ASCs and farmers’ struggles.

This study aims to identify, analyze and rank the critical success factors (CSFs) of Lean Six Sigma (LSS) implementation in Indian manufacturing sector based micro, small and medium enterprises (MSMEs). This study provides critical insight for managers and researchers aspiring for successful implementation of LSS in Indian manufacturing MSMEs.

The CSFs were extracted from literature followed by a questionnaire-based survey from 120 industry professionals with extensive knowledge and experience about LSS working in Indian manufacturing MSMEs. Further, the CSFs were grouped based on their fundamental relevance and ranked using best worst method (BWM) approach using inputs from LSS experts.

This study provides insights on success factors that have helped Indian manufacturing MSMEs to implement LSS. The findings signify that “Strategy based CSFs” were ranked as the top most important factors, followed by two other category factors namely “Bottom-Line CSFs” and “Supplier based and other category-based CSFs”.

The proposed research is specifically relevant to the context of MSMEs in the Indian manufacturing sector. In the future, the same approach can be extended to a global context, encompassing service sector-based MSMEs in healthcare and finance.

This study provides valuable inputs for managers, decision-makers, industrial practitioners and researchers about Indian manufacturing MSMEs. The identified CSFs and their prioritization offer a roadmap for successful adoption of LSS. Managers can allocate resources, and make strategic decisions based on the prioritized CSFs. Decision-makers can align their initiatives with the identified CSFs. Industrial practitioners gain insights to enhance their LSS initiatives, and researchers can focus their efforts on areas critical to LSS implementation in Indian MSMEs. Furthermore, the structured approach employed in this study can be adopted by various MSME sectors globally, thereby broadening the comprehension of LSS implementation.

This study contributes to the existing body of knowledge by addressing the gaps in literature on CSFs related to LSS adoption within Indian manufacturing MSMEs. While LSS has been widely studied, there is limited focus on its adoption in the context of Indian MSMEs. The combination of extensive literature review, questionnaire-based survey and the application of the BWM approach for prioritizing CSFs adds originality to the research.

The purpose of this study is to identify sustainability practices and to propose a framework for evaluating the sustainability index of logistics service providers (LSPs).

Sustainable practices followed by LSPs are identified through literature review and analysis of a case study. Thirteen such sustainable practices are identified. Thereafter, with expert inputs, nine sustainable practices are shortlisted and considered for the evaluation of the sustainability index in the proposed framework. Graph Theory Matrix Approach has been applied to evaluate the sustainability index of an LSP.

Major practices identified for evaluating sustainability index include the use of recyclable packaging, use of renewable energy sources, green procurement, reduction in carbon emissions, use of CNG/electric vehicles, rainwater harvesting and so on. The sustainability index of an LSP is evaluated by using the proposed framework.

LSPs can benchmark their sustainability index with respect to the best in the industry. Based on it, LSPs can also identify potential areas for improvement.

Novelty of the study lies in the proposed framework for evaluation of sustainability index which can be used to develop strategies for green logistics. LSPs can also improve their performance in terms of sustainability measures by adopting green logistics.