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With rising environmental concerns, recent years have witnessed a significant surge of academic and corporate interest in green supply chain coordination (GSCC). This is evident from the rise in channel coordination literature focused toward the elimination of sub-optimal in the green supply chain (GSC). This paper seeks to summarize the model-based research on coordination in GSCs with the help of a framework developed specifically for this paper. The purpose of this paper is to present an in-depth analysis of the widely used models in the area.

A review of literature is presented in this paper to examine the underlying concepts peculiar to GSCC. A classification framework is developed to present an exhaustive survey of commonly used concepts.

Around 90 percent of the papers on GSCC come from game theory (GT) application, which explicitly utilizes coordination through contracts. The review concludes prospective area of research in GSCC. The study posits that there exists a potential of creating a more rational and efficient coordination strategies to improve GSC’s operational performance, with the view of the optimum distribution of resources and better environmental management.

To the best of authors’ knowledge, this is the first state-of-the-art review of GSCC literature focused primarily on mathematical model-based literature. This review identifies various methodological and content-oriented characteristics of GSCC. The paper also opens avenues of future research.

In the recent years, the two increasingly popular topics, namely, Industry 4.0 and circular economy (CE) have attracted the attention of many academicians and practitioners. However, the connection between CE and Industry 4.0 has not been much investigated in the literature. Motivated by this gap, the purpose of this paper is to integrate these two streams and attempt to understand the new paradigm of Industry 4.0 for resolving the issues pertaining to CE principles.

The study uses situation, actor, process, learning, action, performance (SAP-LAP) linkages framework to analyze the applications of Industry 4.0 mechanisms in realizing the issues of current CE business models. This is done through the interpretation of the cross-interaction and self-interaction relationships among the different interfaces/elements of SAP-LAP.

The results suggest that top managers are the most essential actors for integrating the use of Industry 4.0 to achieve sustainability, in the light of CE. In addition, advanced technologies such as Internet of Things and cyber-physical systems are the most important Industry 4.0 actions that help in improving the CE performance parameters.

This qualitative study is an attempt to analyze and assess the strategic issues pertaining to Industry 4.0 standards in CE. The study identifies learnings (challenges/opportunities) and the corrective actions which are imperative toward achieving CE principles. This study will guide managers and policymakers to understand the importance of implementing Industry 4.0 for accomplishing CE principles.

This study integrates two important streams of literature – Industry 4.0 and CE. Thus, this paper offers insights about the importance of Industry 4.0 standards in achieving CE principles.

This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise.

A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose.

SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature.

The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors’ knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better.

The purpose of this paper is to explore the drivers of healthcare waste management from literature review and field survey and model these drivers for understanding the inter-relationships among the drivers to enhance healthcare waste management in the Indian context.

In view of the need of the study, the interpretive structural modelling (ISM) method has been applied to model the drivers. The ISM method helps in depicting the relationships among the drivers and filtration of drivers on the basis of their driving and dependence power.

The findings of the study reveal that the type of a healthcare facility and its management structure, size of a healthcare facility, human resource management of a healthcare facility, healthcare facility’s management monitoring and control, and the effective re-enforcement of government regulation and policy implementation in a healthcare facility play a vital role in the enhancement of HCWM.

The application of the findings of this study would enhance the hospital’s waste management by ultimately leading to a good ambience and satisfied patients and personnels. Additionally, the study would aid in the policy formulation by government and decision making of medical facilities, thereby strengthening HCWM scenario in the country.

The drivers filtered in this study would be useful for ranking the hospitals’ healthcare waste management in a region/country. This ranking may play a vital role in earmarking the hospitals which are managing their healthcare waste according to the guidelines of Central Pollution Control Board (CPCB) and Ministry of Environment and Forest (MoEF) of a country. With the help of this study, the problem of inadequate human resource can be effectively addressed for CPCB and MoEF, in India.

Healthcare waste management is a vital issue which needs attention from the management perspective in India. Therefore, an interpretive structural model, i.e. ISM digraph, has been developed which would help in the filtration of drivers and attaining the better healthcare waste management in an economically and timely manner.

The purpose of this paper is to assess how the emergent theme of Industry 4.0 is considered in the context of supply chain management (SCM) and to identify important areas for future research.

A systematic literature review of 334 studies is carried out. The papers have been analyzed by having a focus on the unit of analysis, research methods used, the key themes covered and how the debate has evolved over a period of time.

Findings of the study reveal that scholars have so far focused on principles and practices for digitized SCM, identified enablers and barriers, and considered the Industry 4.0 at various levels, particularly at the firm level. Majority of studies focus on manufacturing from the perspective of the focal firm.

Limitation of this study is its unit of analysis where only peer-reviewed journal and conference articles published in the English language have been considered.

This review identifies the important themes and areas for practitioners to consider vis-à-vis the implementation of Industry 4.0 standards in supply chains. The review provides insights into under-researched areas and highlights the need for future researchers to develop more practical models for guiding the implementation of Industry 4.0.

This paper is one among the few studies that assess the Industry 4.0 implementation in the context of SCM and provides insights and implications for further research.

The purpose of this paper is to give an up-to-date and structured insight into the literature published during the past decade on sustainable green supply chain management. It also suggests trends for future research based on the research issues identified through systematic and comprehensive analysis of previous studies in the area of green and sustainable supply chain management.

A state-of-the-art literature review is carried out by systematically collecting the existing literature over a period of 10 years (2005-2014) and categorizing it on the basis of attributes such as stages in supply chain, methodology and the industries/sectors under consideration. The classification of literature is also done according to the geographic region and year of publication.

There has been an increased interest among researchers and practitioners in the area of sustainable green supply chain management in the past decade. A need for achieving sustainability through adoption of greener practices has been universally felt, owing to an increasing environmental and ecological complexity. The review reveals that there exists a need to address behavioural issues like human resource management and supply chain partner relationship management. Moreover, reverse logistics, closed-loop supply chain management and waste management are areas that need special focus to achieve environmental sustainability.

The current review focuses on research trends in the past 10 years only. Moreover, papers from only good quality, peer-reviewed journals are considered in the study.

Most of the previous reviews have either focused on specific issues related to sustainable supply chains only or green supply chains. The present study collectively takes into consideration papers both from green supply chain management as well as from sustainable supply chain literature that have a prime focus on environmental sustainability.

This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items directly relates to cost and quality of raw materials purchased from the supplier. In an increasingly competitive environment, firms are paying more attention to selecting the right suppliers for procurement of raw materials and component parts for their products. The present research work focuses on this issue of supply chain management.

This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

The supply chain network is witnessing a changing business environment due to government policies aimed at promoting new small manufacturing enterprises (small and medium-sized enterprises) for intermediate parts and components. Hence, the managers have an option to select a new group of suppliers and allocate the optimal multi-period demand among the new group of suppliers to maximize their purchase value. In this context, the proposed hybrid model would be beneficial for the managers to operate their supply chain effectively and efficiently. The present research work will be helpful for the managers who are interested to reconfigure their supply chain under the failure of any supply chain partner or in a changing business environment. The model provides flexibility to the managers for evaluation of the different available alternatives to take a decision of optimal demand allocation among the suppliers. The proposed hybrid (fuzzy, TOPSIS and MILP) model provides more objective information for supplier evaluation and demand allocation among suppliers in a supply chain. The managers can use the proposed model to the analysis of other management decision-making problems.

This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. This hybrid algorithm prioritizes the suppliers and then allocates the multi-period demand among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

The purpose of this paper is to analyze the literature that is currently available and take a glance at minimum quantity lubrication (MQL) with nanofluids (NFs) as viable candidates to improve the efficiency of various milling operations on challenging materials.

The extensive literature review is carried through the existing literature, which shows the effect of various process parameters in the milling operation of challenging materials under NF-MQL conditions. The manuscript also deals with identifying the inferences and research gaps from the literature review. The role and potential of NF-MQL in milling challenging materials are identified in this work.

The conclusion has also derived some recommendations for future study from the prior research, which will be helpful for any further research in this area.

This research work is limited to milling operations in challenging materials.

NF-MQL applications in milling operations are comparatively underexplored and merit considerable research. The amount of effort industry practitioners put into sustainable manufacturing will surely be greatly reduced by thorough research on the milling of challenging materials under NF-MQL settings.

MQL system has a great potential to perform well in the experimental endeavor. Despite that fact, majority of the small and medium scale manufacturing industries are still using the conventional flood system for the machining of the workpieces because of the unaffordable initial cost and requirement of expertise involved as compared to the flooded lubrication. This issue might be solved when more works will be accomplished in industries for small as well as medium scale production.

These are novel study approaches because there are so many variables that affect cutting efficiency; therefore, more research is required to assess and provide direction for the advancement of hard milling technology.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2023-0010/