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A literature review revealed that government of various developing economies have put an effort on health-care supply chain through the executing critical factors (CFs) directly. Although they have attained some significant benefits in this tactic, but it was not up to satisfactory level. One of the reasons can be attributed to the fact that government/policy makers are not quantifying the impact of CFs on health-care supply chain. This paper aims to propose a methodology to quantify and estimate the impact of CFs on government-supported health-care supply chain (GHSC).

The Graph Theoretic Approach is proposed for estimating the impact and utility of CFs on an Indian GHSC. This study is also extended to scenario analysis for comparing results with different performance situation.

The results obtained from this study show that performance of Indian GHSC is satisfactory, but performance gaps exist which need to be reduced. In this research work, 12 CFs are identified under two significant categories (SCs), i.e. enablers and barriers and the intensity of enablers and barriers have been calculated to show the impact or influence of CFs on GHSC. The value of intensity shows that the role or impact of enabler category (i.e. performance measurement, employee recognition and reward, technology adoption, training cell, inbuilt analytical tool for IT system) is higher on Indian GHSC in comparison to barriers category to enhance the performance of GHSC.

The obtained numerical results are completely in specific to the Indian perspective only; hence, they cannot be generalized for other countries. Simultaneously, this study is related to government supported health-care system; hence, the selection of expert panel was crucial due to the unavailability of doctors and other stakeholders of government system.

The proposed approach is aimed at providing a procedure for evaluating the impact of CFs on HSC in general and GHSC in specific. This study is an attempt to assist government and top management of GHSC to assess the impact of CFs on GHSC and accordingly define its course of actions.

Although various issues related to the CFs have been broadly identified and analyzed, no dedicated study has been reported in the field for quantification of impacts of CFs. Furthermore, this proposed model has an ability to recognize the specific contribution of each CF and overall contribution.

The purpose of this study is to develop a methodology for the identification, categorization and prioritization of operational government-supported healthcare supply chain barriers (GHSCBs).

This study develops a theoretical background for identifying and segregating relevant GHSCBs and proposes a 5W2H (a Toyota production system) with fuzzy DEcision MAking Trial and Evaluation Laboratory (DEMATEL) embedded approach to quantify the causal–effect relationships among the identified operational GHSCBs.

Seven GHSCBs (i.e. uncertainty of demand management, lack of continuous improvement and learning, lack of deadline management, lack of social audit, warehousing equipment unavailability, human resource shortage and inadequate top level monitoring) were identified as significant cause group where the government, top management and decision-makers of government-supported healthcare supply chain (GHSC) have to put efforts.

The results obtained are specific to the GHSC of Indian perspective, which could be extended to global context. However, the proposed approach can be a base and provide a platform to understand and analyze the interactions among GHSCBs.

The proposed methodology will show the appropriate areas for allocating efforts and resources to mitigate the impact of GHSCBs for successful implementation of healthcare supply chain.

According to best of the authors' knowledge, this is the first study of operational barrier for GHSC in India in specific. The use of 5W2H embedded fuzzy DEMATEL approach for the development and analysis of the theoretical framework of Indian GHSCBs is unique in barrier literature.

Despite the existence of multiple asset replacement theories, the economic life replacement method remains a major practical technique for making rational machine replacement decisions. The purpose of this paper is to bridge this method with comprehensive data analytic tools and make it applicable it to modern business reality with abundant data on operating and replacement costs.

This study employs operations research, discrete and continuous optimization, applied mathematical modeling, data analytics, industrial economics and real options theory.

Constructed stochastic algorithms extend the deterministic economic life method and are compared to the contemporary theory of stochastic asset replacement based on real options and dynamic programming. It is proven that both techniques deliver similar results when the cost volatility is small. A major theoretic finding is that the cost uncertainty speeds up the replacement decision.

This research suggests that the proposed stochastic algorithms may become an important tool for managerial decisions about replacement of many similar machines with detailed data on operating and replacement costs.

Compared to the real options replacement theory, major advantages of the proposed algorithms are that they work equally well for any distribution of age-dependent stochastic operating cost. The algorithms are tested on a real industrial case about replacement of medical imaging devices. Numeric simulation supports obtained analytic outcomes.

This study identifies barriers faced by online e-waste collection platforms in India and proposes a novel approach to manage strategies that contribute towards making such online platforms economically, socially, and environmentally sustainable by mitigating their interacting barriers.

The approach consists of a barrier assessment process and a strategy evaluation and planning process. The barrier assessment process uses multiple criteria decision making (MCDM) based method to evaluate the interactive effects of the barriers to derive their direct and indirect impacts on sustainability. The strategy evaluation and planning process evaluates the overall contribution value of strategies for mitigating interacting barriers and then categorizes them into planning zones based on their contribution and feasibility.

The interaction among the barriers is a significant component of the overall impact of the barriers on sustainability. The most impactful barriers are the lack of dissemination of information, lack of government support, insufficient infrastructure, and awareness and attitude of consumers. Lack of government support is the most influential causal barrier. Lack of information dissemination is a significant causal barrier with the highest overall impact on sustainability. Priority strategies that must be implemented to ensure sustainability include government support, offering higher prices or discounts for giving up e-waste, and increasing information dissemination.

Different from existing approaches for evaluating sustainability strategies, the strategies in this study are identified and evaluated based on their overall contribution towards mitigating interacting barriers to sustainability. The approach is applied in the Indian context to reveal the most influential barriers to the sustainability of online e-waste collection platforms and the most effective strategies for mitigating these barriers. The outcomes of this study contribute to strategy planning for sustainable online e-waste collection platforms in India.

Epoxy acrylate which is commercially utilized for UV curable coatings although has excellent adhesion, flexibility, hardness and chemical resistance, they lack in antimicrobial properties. Citric acid (CA) is economical as well as a bio-based compound which possess an antimicrobial activity. So, the purpose of this research investigation is the preparation of CA-based oligomer which can be further incorporated with epoxy acrylate and tri (propylene glycol) Diacrylate (TPGDA) to form uv curable coating and the study of its antimicrobial property.

A UV-curable unsaturated oligomer (CUV) was synthesized from CA and glycidyl methacrylate (GMA). The chemical structure of CUV was confirmed by FTIR, ¹H NMR, GPC, hydroxyl value, acid value and iodine value. Further, CUV was assimilated as an antimicrobial as well as crosslinking agent to copolymerize with epoxy acrylate oligomer and a series of UV-cured antimicrobial coatings were concocted by employing UV-curing machine. The consequence of varying the fraction of CUV on the mechanical, chemical, thermal and antimicrobial properties of UV-cured wood coatings was explored.

Results exhibited good mechanical, chemical and thermal properties. In addition, it was perceived that the zone of inhibition against S. aureus got enlarged with increasing content of CUV in the coating formulation.

The synthesized bio-based CUV reveals an extensive potential to ameliorate the antimicrobial properties of UV-curable coatings.

This chapter provides an overview of the explanations and factors affecting dividend policy. This study employs a systematic literature review approach to review a large sample of studies related to the dividend puzzle. Although the analysis reveals mixed evidence involving the theories and determinants of dividend policy, some determinants appear in numerous studies. However, no consensus exists on an optimal dividend to resolve the dividend puzzle, and the authors propose a model to deal with the same. When examining dividend policy, researchers should consider the firm, market, behavior, and other determinants. When making significant dividend or stock decisions, managers and shareholders should also contemplate the factors, interactions, inadequacies, and consequences. Future researchers should strive to take a more comprehensive view when resolving the dividend puzzle. This study provides a current and complete picture of dividend policy's available theories and empirical determinants. Its significant contribution is identifying some of the more consistently essential determinants of dividend policy while proposing a holistic model to address the prevailing dividend dilemma.

The requirement of high-quality government-supported healthcare services has necessitated the significance of recognizing new management practices to enhance patient satisfaction. Hence, the purpose of this study is to address the patient's enhanced custom needs through the implementation of supply chain value stream mapping (SCVSM) in government-supported drug distribution system (DDS) for enhanced patient's satisfaction.

This study elucidates the role of one popular emerging management technique (i.e. SCVSM) in the healthcare sector by an investigative case study. The DDS in Rajasthan (India) was selected for this study. The data for this analysis were gathered in three ways (i.e. direct observation, documentary analysis and semi-structured interviews).

The outcome of this current study reveals that it is possible to apply the tool (SCVSM) to investigate the wastes in DDS to deliver the medicines at right time, right quantity and right quality. The application of SCVSM concluded that the various Kaizens (areas needed to improve) in lead time; transportation and routing should be adopted. The study further implemented kaizen on the current SCVSM and developed future SCVSM.

Although various stages and functions exist in the healthcare supply chain, the current study is focused on the distribution system of drugs. The proposed approach provides a platform for both researchers and academicians to understand the existing DDS and to implement the SCVSM approach in the healthcare environment. The results show that the proposed SCVSM model is able to identify some operational bottlenecks and wastes which interfere in DDS.

It was observed that limited literature related to lean implementation on DDS and implementation of SCVSM on the healthcare environment in general and government-supported or public in specific are available. The current study on the application of SCVSM in DDS is unique in nature and will definitely add value to the existing literature of the application of value stream mapping (VSM) on the healthcare supply chain management field.

Proper prediction of productivity can enable the enhanced estimation, realistic scheduling, and accurate cost forecasting of construction processes. Due to the existence of different labor sources (unionized and non-unionized), the prediction of productivity is still a significant problem in India. Moreover, the construction procurement processes and on-site performance are the predominant elements that can result in improved project outcomes. Thereby, the consideration of labor constraints and site conditions will play an important role in productivity improvement.

This study investigates the factors affecting construction site productivity. A total of 28 factors are grouped under 7 categories as follows: labor constraints, safety and quality procurements, material and equipment (ME), site management, project working condition, delay controls, construction methods and techniques, and external factors. Furthermore, by involving these factors, the questionnaire survey was conducted among Indian construction practitioners. As a result, 204 responses were received and the data were analyzed using a reliability test, relative importance index (RII), and analysis of variance (ANOVA).

The result of this study highlighted the importance of strategic construction management activities in terms of effective planning of ME, planning and realistic scheduling of construction activities, proper communication, information sharing, etc. Thus, this study provides a clear insight to the Indian construction practitioners in determining the effect of these site factors on the successful execution of their projects.

In this paper, the problem of construction productivity in India and its causes are explained effectively. This study examines the preference of labor contract, labor source, and most importantly, the factors affecting site productivity. Moreover, the other lagging issues regarding the management of construction activities are also described in detail.

Drug warehouses (DWs) play a crucial role in drug distribution of government-supported healthcare supply chain as it controls both the cost and responsiveness of the logistics activities. The current study proposes a methodology using data envelopment analysis (DEA) to estimate the performance along different dimensions and was applied to 30 government-supported DWs.

This study employs DEA to evaluate the performance and relative technical efficiency of DWs. In this research, four inputs and six outputs are identified based on intensive literature review and discussion with all stakeholders of DWs. The inputs are warehouse storage capacity, temperature-controlled storage capacity, number of skilled employees and operational cost, while the outputs are fill rate, number of generic drugs, volume of drugs, consumption points, inventory turns ratio and time efficiency.

Results show that 30% DWs operate at the most productive scale size with 100% efficiency level while 47% DWs have a significant possibility for further enhancement in productive efficiency and 23% DWs should diminish their operational size to increase their productivity level. It was also found that achieving 100% operational productivity along warehouse space capacity needs significant effort, whereas other three inputs, namely temperature-controlled capacity, number of skilled employees and operational cost, require comparatively less effort. Similarly, it was observed that the performance along the fill rate and time efficiency is satisfactory, whereas the performance along other fours output variables (i.e. number of generic drugs, volume of drugs, consumption points and inventory turns ratio) needs to be improved.

The findings offer insights on the inputs and outputs that significantly contribute to efficiencies so that inefficient DWs can focus on these factors.

Although many issues related to DEA have been widely researched and reported, but no literature has been found for analysis of DWs in general and government-supported DWs specifically to find out efficiencies for supply chain performance improvement.

A MEMS process is described to control diaphragm thickness with an integrated provision for back to front alignment in the fabrication of a polysilicon piezoresistive pressure sensor. The end point detection for the diaphragm etching is suitably incorporated in the process so that it is also used for the back‐to‐front alignment. The proposed process is cost‐effective and suitable for the batch fabrication of the pressure sensor.