This study addresses the intersection of renewable energy and the circular economy by exploring an underutilized resource, namely, biohydrogen production from agricultural residues. In transitioning to sustainable resource utilization and carbon neutrality, existing gaps include inefficient supply chains and limited technological adoption. Our research adopts a digital twin framework to optimize supply chain logistics for biohydrogen production, supported by greenfield analysis, Monte Carlo simulations, and network optimization. The results point to significant socio-economic and environmental benefits, including an estimated 34.54 billion dollars in annual social savings. The findings offer actionable insights for policymakers, technology developers, and investors aiming to foster industrial decarbonization within circular supply chains.

PurposeAs e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.Design/methodology/approachThe authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.FindingsThe authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.Originality/valueThis is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.

Effective contractor selection is crucial for successful execution of construction projects. In contrast to the conventional lowest-bid approach prevalent in the public sector, this study focuses on developing a framework that minimizes time and cost overruns by considering diverse criteria for contractor selection. A variety of machine learning models, including multi-linear regression, random forest, Support Vector Machine, and Artificial Neural Network, have been employed, with multi-linear regression proving to be the most effective, achieving the lowest Mean Squared Error of 0.00003366. To determine the final order allocation, a multi-objective mathematical model was utilized to optimize conflicting criteria, such as time and cost overruns, sustainability, risk, and safety aspects related to shortlisted contractors. The findings highlight the significance of specific selection criteria, such as turnover, experience in similar projects, qualification of staff, technology utilization, client satisfaction, accident records, available bid capacity, and socioeconomic factors. This study emphasizes a three-phase decision-making framework for contractor selection and order allocation, particularly in public construction projects, with a focus on sustainability. By adopting this approach, government agencies can enhance infrastructure projects and minimize overruns through optimization and analytical tools, which aligns with the Gati-Shakti scheme of the Indian government. It is recommended that clients embrace a holistic approach to contractor selection, considering both technical and non-technical factors, to ensure successful project outcomes.

Increasing pollution is causing adverse environmental effects, leading to increased interest in combating this issue. There has been a significant interest in minimizing the pollution caused by combustion engine vehicles, with high research and development investments in hybrid and electric vehicle (EV) batteries. The innovations in EVs have a high potential to contribute to an optimized transportation sector while also playing a crucial role in reducing greenhouse gas emissions. This study contributes to the EV industry by precisely predicting the power demand at a particular charging station and identifying the optimal charging station characteristics. We proposed a modified business process based on digital technologies to maximize customer engagement and operational efficiency. Our research has incorporated technologies like artificial intelligence (AI) and machine learning (ML). This study addresses the issues of EV infrastructure facilities, the issues raised by the lack of service features for EVs, and the optimal power requirement for charging stations. The proposed framework has managerial and technological implications, suggesting that the system must promptly receive, store, and analyze substantial volumes of data and demonstrate adaptability in response to environmental factors, such as the availability of EVs and the utilization of renewable energy sources. Despite the challenges, there is potential promise in developing decision assistance systems for electric vehicle power demands based on AI and ML.

The global shipping industry is the cornerstone of contemporary culture and the economy. Swift trade facilitated by efficient and dependable shipping services forms the backbone of the rapid exchange of goods and ideas, making the availability of certain now-ubiquitous products possible. Maritime cargo strategies enable businesses to seamlessly and expeditiously transport their goods across nations and borders. Integrating Internet of Things (IoT) technology is a promising solution to enhance these operations. IoT refers to a network of interconnected devices, objects, or “things” that communicate and share data with each other over the internet. The primary purpose of IoT is to enable these devices to collect, exchange, and analyze information, creating a seamless and intelligent network. This paper addresses the barriers that organizations may face when contemplating the implementation of IoT in maritime freight operations. To identify and prioritize these challenges, a multi-criteria decision-making approach has been employed specifically the Fuzzy Analytical Hierarchy Process (AHP) method Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to rank these factors in descending order of their significance.

Optimizing costs and profits while meeting customer demand is a critical challenge in the development of perishable supply chains. Customer-centric demand forecasting addresses this challenge by considering customer characteristics when determining inventory levels. This study proposes a solution framework comprising two steps: (a) segmentation using customer characteristics and (b) demand forecasting for each segment using transparent and responsible artificial intelligence techniques. We employed k-means, hierarchical clustering, and explainable AI (XAI) to segment, model, and compare several machine-learning techniques for demand forecasting. The results showed that support vector regression outperformed the autoregressive models. The results also showed that the two-step segmentation and demand forecasting process using hierarchical clustering and LSTM outperforms (Weighted average RMSE across segments = 61.57) the conventional single-step unsegmented forecasting process (RMSE overall data = 238.18). The main implication of this study is the demonstration of XAI in enhancing transparency in machine learning and an improved method for reducing forecasting errors in practice, which can strengthen the supply chain resilience for perishable products.

The primary objective of the research study is to investigate the influence of innovative digital tools and technologies in attaining the fundamental aspects of Industry 4.0, including automation, integration, traceability, flexibility, safety, and security. This study utilizes a systematic literature review, employing keyword search criteria to examine 187 relevant articles and evaluate the implementation and consequences of technologies, such as artificial intelligence, big data, cyber-physical systems, cloud computing, and digital twin, across a variety of Indian economic sectors, including retail, healthcare, manufacturing, agriculture, entertainment, and e-commerce. The study's results indicate that digital technologies can improve parameters such as automation, transparency, integration, traceability, flexibility, safety, and security, while promoting sustainable development through reduced waste, operational costs, and time, and optimum resource utilization. The insights from this study will be helpful for future inquiries and economic initiatives, encouraging the adoption of digital innovations in fields such as women's empowerment, public administration, and skill development.

Consumers' dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry.

The frequency and intensity of global disasters, including the COVID-19 pandemic, and natural disasters such as earthquakes, floods, and wildfires, are increasing, necessitating effective emergency logistics management. Climate change significantly contributes to these events, emphasizing the importance of limiting human and environmental impacts. The transportation sector, particularly the automobile industry, ranks second in global carbon emissions, highlighting the need to adopt electric vehicles (EVs) to reduce emissions and minimize the impact of climate change. However, this has led to an increase in demand for lithium-ion batteries. During emergencies, end-of-life (EOL) battery management through reverse logistics is essential because recycling EOL batteries can recover valuable raw materials, decrease landfill waste and costs, and support environmental sustainability. This study proposed a two-phase method for intelligent emergency EV battery reverse logistics management. The first phase employed machine learning to address unpredictable battery demands, whereas the second phase proposed a multi-objective model to minimize carbon emissions through efficient order allocation during uncertain emergencies. The model considers carbon emissions and defect rates as sources of uncertainty, current regulations, and customer environmental awareness. The model is solved using the weighted sum and epsilon-constraint methods, resulting in non-dominant solutions. The findings indicate that combining the selection of third-party reverse logistics providers (3PRLPs) with optimal order allocation for recycling old batteries during emergencies effectively minimizes environmental impacts and combats climate change.

As industrialization continues, the world faces issues related to increased energy consumption and massive carbon emissions. Given the growing complexity of maritime transport operations, whose role in the global trading system has grown significantly over the last few decades, this has resulted in an increase in carbon emissions from vessels and quay cranes alike. The efficiency of maritime transport operations is critical, and proposing a prescriptive analytics-based comprehensive approach to improving it is vital. The expansion of container terminals has resulted in increased global container traffic, which has increased carbon emissions from both vessels and quay cranes. This paper aims to provide a comprehensive, prescriptive, analytics-based approach to the integrated planning decision of the dynamic berth allocation problem (DBAP) while reducing carbon emissions. We investigate the implications of consecutive berths and yard assignments for carbon emissions during port handling and sea operations. We created a mathematical model to reduce handling operations, anchorage waiting time costs, demurrage costs, costs incurred due to deviations from vessels' preferred positions, and carbon tax costs. We propose two kinds of solutions: (i) ILOG CPLEX Solver and (ii) meta-heuristics i.e., particle swarm optimization (PSO), advanced PSO (APSO), shuffled complex evolution (SCE), and the shuffled frog leaping algorithm (SFLA). The SFLA performs better than other meta-heuristics Algorithm in all the instances.

There have been more studies on the circular economy (CE) and the internet of things (IoT) in recent years, but limited studies have examined how IoT can aid the CE. This study will use bibliometric tools to examine the current literature on IoT-CE and contribute to the expansion of knowledge in this field. The R package Bibliometrics was used to look at a set of more than 2,000 scientific papers from the Web of Science database about how IoT-based solutions can help improve the circular economy. This analysis helps us figure out how many papers are published each year by country, author, journal, and institution. With the number of publications around the world going up, the United States was the country with the most publications and citations, followed by the United Kingdom. Also shown in the results is how well journals, authors, and institutions did. The keyword analysis shows that research in these fields is getting more and more popular. The content analysis shows that IoT is mostly used in six areas: internet traceability and e-commerce, IoT adoption barriers in the supply chain, digitization in the supply chain, the effects of big data on the economy, the role of information technology in the circular economy, and supply chain performance. Therefore, these results can aid future research in this field by providing a global perspective of scientific research on IoT-based innovative solutions along-with the role of various digital technologies in achieving circular economy characteristics or dimensions.

The COVID-19 pandemic has increased the demand for life-saving devices known as 'ventilators,' which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equipment, many Indian nonmedical equipment companies have risen to meet this demand. This unexpected demand for ventilators during the COVID-19 pandemic, similar to that for other EOL electronic medical devices, has become a severe problem for the nation. Consequently, the healthcare industry must efficiently handle EOL ventilators, which can be outsourced to 3PRLPs. 3PRLPs play a vital role in a company's reverse logistics activities. This study emphasises the 3PRLP selection process as a complex decision-making problem and the optimisation of order allocation to qualified 3PRLPs. As a result, this study proposes a two-phase hybrid decision-making problem. First phase combines the two multi-attribute decision-making methods to select 3PRLPs based on their assessed SPS and Second phase, the evaluated SPS was utilised as one of the objectives of a multi-objective linear programming model to allocate orders to the selected 3PRLPs. To solve the proposed model, both classical and modern approaches were used. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry.

The goal of this paper is to explain why digital innovation is so important in business organizations in order to survive in Industry 4.0. The study helps to understand the new era of Industry 4.0 and the importance of introducing digital innovation into organizations. A systematic review of the literature and studies on Industry 4.0 and digital innovation were synthesized to find answers to the research questions. To improve their manufacturing industry, organizations have implemented digital technologies such as augmented reality, robotic sensing, artificial intelligence, cloud computing, cyber physical systems, and remote sensing technologies. These technologies focused on automating logistics and supply chain systems, improving manufacturing system performance, and simplifying automated production systems. Because digital innovations save time and energy, employees can devote more time and energy to creative and innovative activities. Organizations should plan to implement digital technology in order to keep the environment healthy and sustainable while meeting the demands of customers, consumers, and the Industry 4.0 dimension.

Industry 4.0 is a growing trend in the field of project management in the modern era. As a result, this study identifies emerging and growing trends in project management as they relate to Industry 4.0. Before now, no research has been conducted to examine existing themes and research trends in this expanding field. To fill these gaps, this paper seeks to comprehend current academic research relating to Industry 4.0 and smart manufacturing in project management. We conducted a literature search, content review, and exploratory study on the subject area, and we used Scopus as the main database to identify related articles. We used cluster analysis, network analysis, citation analysis, and co-citation analysis to identify the primary vehicle of publications, research centres, and researchers. The key finding of our research is that Industry 4.0 is attracting international researchers' attention; thus, we aim to identify the new and emerging research area in Industry 4.0 and improve its productivity. The term Industry 4.0 first appeared in the market in 2012. Following that, researchers have thoroughly investigated the implications of this newly emerging field and concluded that various technologies, such as the Internet of Things, big data analytics, and artificial intelligence, play an important role in transforming organisations by improving their productivity. Countries such as Australia, Brazil, Canada, China, and other European countries expressed interest in this field and quickly joined the collaboration. Industry 4.0 is now regarded as a popular research topic in the field of project management.

Purpose This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities. Design/methodology/approach The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance. Findings The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs. Research limitations/implications This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities. Practical implications The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings. Originality/value This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

COVID-19's aftereffects have had a significant impact on our daily lives. The recent pandemic caused by the new coronavirus epidemic has increased the production of infectious medical waste (IMW) and demand for medical care and protective equipment. Although national and local initiatives are primarily concerned with saving lives and bolstering local economies, hazardous waste management is essential for reducing long-term human and environmental health threats. In this situation, establishing a dependable and efficient reverse logistics network of IMW can prevent the spread of viruses. Few studies have been conducted on this topic and those that have rarely considered how to operate a network of multiple medical waste generation centres (MWGCs) cost-effectively and risk-averse. This study proposes a framework for reducing the accumulation of IMW products using reverse logistics in the context of medical waste management. The optimal values of the multiple objective functions were determined using a multi-objective optimization model. Our proposed framework considers four objective functions and their respective constraints while using data-driven digital transformation in reverse logistics energy optimization for managing single-use medical waste.

The global-local supply chains are affected by the forward and downward propagation of COVID-19. The pandemic disruption is a low-frequency and high-impact (black swan) event. Adapting to the "New Normal" situation requires adequate risk mitigation strategies. This study proposes a methodology to implement a risk mitigation strategy during supply chain disruptions. Random demand accumulation strategies are considered to identify the disruption-driven challenges under different pre and post-disruption scenarios. The best mitigation strategy and the optimal location of distribution centers to maximize the overall profit were determined using simulation-based optimization, greenfield analysis, and network optimization techniques. The proposed model is then evaluated and validated using appropriate sensitivity analysis. The main contribution of the study is to (i) perform cluster-based supply chain disruption analysis, (ii) propose a resilient and flexible model to illustrate the proactive and reactive measures for the ripple effect, (iii) prepare the supply chain for future pandemic-like crises, and (v) reveal the relationship between the pandemic impact and supply chain resilience. A case study of an ice cream manufacturer is used to demonstrate the proposed model.

A sustainable and robust supply chain is becoming increasingly important to meet customers’ never-ending needs. Numerous supply chain challenges such as forecasting issues, maintaining traditional inventories, distribution delays, delayed digital transformation, counterfeit consumer goods, excessive carbon gas emissions, and hazardous waste have necessitated improvements in supply chain management. These difficulties can only be addressed by modernizing the embedded technology of the supply chain. According to current assessments, organizations should prioritize sustainable development and use innovations such as the “Internet of Things (IoT) and big data” to create smart and resilient supply chains for sustainable performance and Industry 4.0. Industry 4.0, ushers in a sustainable and digital supply chain revolution through the application of cutting-edge technologies. The 19 papers in this special issue aim to contribute to the development of research topics on optimizing the IoT and big data-embedded smart supply chains for sustainable performance. Three major macro-topics arose: (i) Implication of IoT in Supply Chain, (ii) impact of big data in the supply chain, and (iii) Use of Blockchain in Supply Chain Analysis. Novel evidence-based frameworks and approaches have been proposed, as well as some real-life situations, in the articles featured in this special issue collection, supporting meaningful developments in the twin subject of IoT and Big data-embedded smart supply chains for sustainable performance. Nonetheless, an examination of the papers in the special issue reveals interesting research prospects required to analyze and explore this developing topic in terms of its various features. More evidence and application-based research are required to better understand the dynamics of Smart Supply Chains for Sustainable Performance ecosystems and Industrial Symbiosis networks. Finally, specialized IoT and Big data are being developed. Embedded solutions based on intelligent architectures and data-driven techniques can help firms embrace Smart Supply Chains for Sustainable Performance not only within their own walls, but also in the supply chain and societal dimensions.

This research intends to examine the Service Quality (SQ) factors in mail service operations conducted at National Sorting Hub (NSH), Mangalore, Karnataka state, Southern India. In the postal service industry, measuring SQ performance in mail service operations is a major challenge. So, this paper attempts to explore the positive effect of postal SQ factors on Customer Satisfaction (CS) with the data collected from employees (n = 148) to the Indian postal service. Further, to quantify the significance of SQ factors in gaining customer satisfaction, this study has used Graph-Theoretic approach technique. Results established same priority index to the SQ factors such as Human service delivery (Rank 1), Core service (Rank 2), and Systemization (Rank 3). The results indicate that the postal service industry should concentrate more on these factors to enhance their customer satisfaction. Further, the study employs the operating empirical model which is sparsely used in Indian domain. Furthermore, this research aids the SQ in designing and developing the necessary aspects to improve the CS in various service sectors.