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The rapid growth of electric vehicle (EV) adoption globally underscores the need for sustainable and efficient supply chains, particularly in emerging economies like India. However, India's EV industry faces critical challenges, including dependency on imported raw materials, limited domestic manufacturing, and inadequate charging infrastructure. This study addresses the gap in understanding how to optimize India's EV supply chain to reduce import reliance and align with global best practices. The objective of the research is to develop actionable strategies for enhancing domestic EV production capabilities and sustainability. Using data from credible sources, including government and industry reports, statistical analysis was performed via ANOVA to compare India's EV supply chain metrics with global leaders such as China and Norway. Advanced visualization techniques were also employed to analyze data interactions. The findings reveal that India's domestic manufacturing meets only 20% of demand, while strategic investments in local mining and recycling could mitigate up to 40% of raw material needs by 2030. Furthermore, policy standardization and infrastructure development are critical for closing the gap with global benchmarks. These results highlight the importance of a cohesive national strategy to enhance India's EV ecosystem. Future research should expand the comparative scope to other developing nations and explore innovative recycling technologies. [ABSTRACT FROM AUTHOR]

Utilization of biomass through anaerobic digestion and hydrothermal carbonization is crucial to maximize resource efficiency. At the same time, supply chain integration ensures sustainable feedstock management and minimizes environmental and logistical impacts, enabling a holistic approach to a circular bioeconomy. This study presents an integrated approach to simultaneously optimize the biomass supply chain network and process flowsheet, which includes anaerobic digestion, cogeneration, and hydrothermal carbonization. A three-layer supply chain network superstructure was hence developed to integrate the optimization of process variables with supply chain features such as transportation modes, feedstock supply, plant location, and demand location. A mixed-integer nonlinear programming model aimed at maximizing the economic performance of the system was formulated and applied to a case study of selected regions in Slovenia. The results show a great potential for the utilization of organic biomass with an annual after tax profit of 23.13 million USD per year, with the production of 245.70 GWh/yr of electricity, 298.83 GWh/yr of heat, and 185.08 kt/yr of hydrochar. The optimal configuration of the supply chain network, including the selection of supply zones, plant locations and demand locations, transportation links, and mode of transportation is presented, along with the optimal process variables within the plant. [ABSTRACT FROM AUTHOR]

Featured Application: Rural communities and energy planners can directly apply the methodology developed in this study to optimize biogas production from locally available biomass resources. By implementing optimization strategies for biomass input and reactor sizing, communities can enhance the efficiency of their hybrid renewable energy systems, ensuring a stable and reliable energy supply. In response to the growing demand for sustainable energy and the environmental impacts of fossil fuels, renewable sources like biomass have become crucial, especially in regions rich in agricultural and animal waste. This study focuses on a real-life project in Aras de los Olmos, Spain, where solar, wind, and biogas from biomass serve as primary energy sources, supplemented by a hydro-based storage system to stabilize supply. Central to the research is optimizing biomass inflow to the biogas reactor—the primary controllable variable—to effectively manage the supply chain, maximize energy output, and minimize logistical costs. The study addresses practical challenges by utilizing real data on demand, truck capacities, and costs and employing robust optimization tools like Gurobi. It demonstrates how optimized biomass flow can secure energy needs during high demand or when other renewables are unavailable. Integrating technical and economic aspects, it offers a comprehensive and practical model for sustainable and economically viable energy production in rural communities. It provides a foundational framework for future renewable energy and optimized energy storage system studies. [ABSTRACT FROM AUTHOR]

ABSTRACT Globalization and growing business dynamics lead to weakly harmonized supply chain (SC) systems. While smart technology offers innovation opportunities, supply chains often lack the integration needed to fully leverage resources and collaboration. A comprehensive systems engineering (SE)‐driven model for integrated innovation and optimization of smart SC business models is still missing. This study, through case research at SAP SE's Industry 4.0 division and three automotive companies, identifies key digital transformation objectives and interoperability gaps hindering smart opportunities. Systems engineering, supply chain management (SCM), and artificial intelligence (AI) methods were synthesized into a holistic SE‐driven model for transforming and optimizing SC business models. This model integrates management concepts like the theory of ambidexterity and dynamic capabilities, with SE methods capability engineering and complex adaptive systems, and semantic web concepts. Key SE contributions include meta‐modeling multi‐tier SC architectures, ensuring performance and resilience via simulations, and balancing value exploration and exploitation. Moreover, semantic harmonized and profit‐optimized SC ecosystems enable collaborative innovation for flexible, efficient manufacturing—a core Industry 4.0 principle. This SE‐driven model, validated by experts, provides a concise view of digital SC business models and a driver of generative design. [ABSTRACT FROM AUTHOR]

Abstract: Inventory supply chain planning involves determining the quantity of products to be transported among entities within a specified planning horizon. Often, inventory levels are reviewed at set intervals: (1) Background: In this paper, periodic review (s,S) policy is used to optimize inventories from an integrated perspective of inventory management across the supply chain. The decision to place an order and the order quantity are based on the inventory level at the review time. If the inventory falls below a certain level (s), an order is placed to replenish it to a target level (S); (2) Methods: The planning model is implemented using a mixed integer linear programming model. It determines the inventory levels, supply levels and the (s) and (S) levels for each entity, as well as the flow of products between them. To test the model, a case study is conducted to demonstrate its applicability; (3) Results: The experimental data confirm the model's validity, as its behavior aligns with the expectations for a periodic review (s,S) policy; (4) Conclusions: Since a fixed replenishment frequency is mandatory, with no continuous inventory review required, this policy offers simplicity and ease of implementation, making it a practical choice for certain inventory management. [ABSTRACT FROM AUTHOR]

The supply chain network design (SCND) problem is a typical optimization problem that determines the structure of a supply chain and affects its costs and operational performance. SCND deals with various decisions, such as determining the number, size, and location of facilities and the optimal material and product flows of the entire supply chain network. Therefore, SCND is one of the most crucial planning problems in supply chain management. In this paper, we present a practical approach in which we adopt a mixed-integer programming (MIP) mathematical model to solve a real industry SCND problem for McDonald's China. As a result of this project, McDonald's China has saved millions of dollars in logistics costs and reduced CO2 emissions by more than 10%. In our approach, size-reduction techniques were successfully applied to deal with a large-scale model, making it possible to analyze hundreds of scenarios before coming to a consensus. [ABSTRACT FROM AUTHOR]

This paper explores how AI drives GCC sector retail towards the fulfillment of the UN SDGs. Analyzing a survey conducted on 410 retail executives, using PLS-SEM, this study underlines the role of AI in promoting operational efficiency, waste reduction, and consumer engagement with greener products. Key highlights include that AI-enabled marketing strategies improve the adoption of sustainable practices among consumers; AI-powered smart distribution channels enhance supply chain efficiency, reduce carbon emissions, and optimize logistics. For a retailer, practical applications of AI include the use of AI in demand forecasting to potentially reduce waste, personalized marketing to efficiently promote sustainable products, and deploying smart systems that reduce energy consumption. While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. The following study provides actionable insights for GCC retailers on how to align AI adoption with sustainability goals, fostering competitive advantages and environmental responsibility. [ABSTRACT FROM AUTHOR]

The impacts of climate change on agricultural production are becoming more severe, leading to increased food insecurity. Adopting more progressive methodologies, like smart farming instead of conventional methods, is essential for enhancing production. Consequently, livestock production is swiftly evolving towards smart farming systems, propelled by rapid advancements in technology such as cloud computing, the Internet of Things, big data, machine learning, augmented reality, and robotics. A Digital Twin (DT), an aspect of cutting-edge digital agriculture technology, represents a virtual replica or model of any physical entity (physical twin) linked through real-time data exchange. A DT conceptually mirrors the state of its physical counterpart in real time and vice versa. DT adoption in the livestock sector remains in its early stages, revealing a knowledge gap in fully implementing DTs within livestock systems. DTs in livestock hold considerable promise for improving animal health, welfare, and productivity. This research provides an overview of the current landscape of digital transformation in the livestock sector, emphasizing applications in animal monitoring, environmental management, precision agriculture, and supply chain optimization. Our findings highlight the need for high-quality data, comprehensive data privacy measures, and integration across varied data sources to ensure accurate and effective DT implementation. Similarly, the study outlines their possible applications and effects on livestock and the challenges and limitations, including concerns about data privacy, the necessity for high-quality data to ensure accurate simulations and predictions, and the intricacies involved in integrating various data sources. Finally, the paper delves into the possibilities of digital twins in livestock, emphasizing potential paths for future research and progress. [ABSTRACT FROM AUTHOR]

Background: This study explores the use of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a crucial resource for sustainable energy and material production. Efficient biomass logistics play a key role in supporting circular bioeconomy principles by improving resource utilization and reducing operational costs. Methods: Two optimization approaches are evaluated: a base MILP model designed for scenarios with single processing points and an advanced model that incorporates intermediate processing steps to enhance logistical efficiency. The models were tested using synthetic datasets simulating vineyard regions to assess their performance. Results: The models demonstrated significant improvements, achieving cost reductions of up to 30% while enhancing operational efficiency and resource utilization. The study highlights the scalability and real-world applicability of the proposed models. Conclusions: The findings underscore the potential of MILP models in optimizing biomass supply chains and advancing circular bioeconomy goals. However, key limitations, such as computational complexity and adaptability to dynamic environments, are noted. Future research should focus on real-time data integration, dynamic updates, and multi-objective optimization to improve model robustness and applicability across diverse supply chain scenarios. [ABSTRACT FROM AUTHOR]

This study presents the development and application of a mathematical optimization model to improve decision-making in the supply chain for high-energy-density pellet (HEDP) production and commercialization. Focused on the Metropolitan Region of Chile, the research involved a detailed analysis of key supply chain components, including identifying landfills and controlled dumps, waste volume assessments, plant location analysis, technology evaluation, and market potential exploration. The model revealed that the available raw material in the region was sufficient to meet 100% of HEDP demand, with a surplus of 2,161,952 tons remaining after satisfying maximum demand. An optimization analysis of potential plant locations identified Santa Marta as the optimal choice, resulting in annual cost savings of USD 100,000 compared to other sites. This work underscores the role of mathematical optimization in enhancing supply chain efficiency for biomass-based energy products, offering valuable insights for strategic decision-making in similar contexts. [ABSTRACT FROM AUTHOR]

This research introduces a groundbreaking approach to supply chain optimization and management, termed as Deep Reinforcement Learning based Supply Chain Optimization and Management (DRL-SCOM). At the core of this approach is the utilization of advancements in Deep Reinforcement Learning (DRL), specifically through the integration of Randomized Ensembled Double Q-learning (REDQ) and Trust Region Policy Optimization (TRPO). DRL-SCOM is designed to effectively tackle the inherent complexities and dynamic challenges that are characteristic of supply chain management. One of the key strengths of DRL-SCOM lies in its use of REDQ, which plays a crucial role in mitigating the overestimation bias commonly associated with traditional Q-learning methods. This results in more accurate value estimation and policy improvement, a critical factor in the effective management of supply chains. Additionally, the integration of TRPO into the framework brings the advantage of safe and stable policy updates. Such stability is vital for maintaining the robustness required in the fluctuating environment of supply chain operations. The combination of REDQ and TRPO in DRL-SCOM creates a powerful synergy. REDQ's ensembled learning approach, when fused with TRPO's trust-region method, enables the framework to efficiently navigate the complex and high-dimensional decision space typical of supply chains. This allows for real-time optimization of decisions while staying within operational constraints. The DRL-SCOM methodology shows significant potential in addressing various aspects of supply chain management, from demand forecasting and inventory management to logistics, adeptly handling the nonlinearities and uncertainties that are prevalent in these areas. Thus, the DRL-SCOM framework emerges as an innovative solution, pushing the frontiers of traditional supply chain management. It paves the way for a more agile, responsive, and intelligent system, equipped to adapt to changing market demands and operational challenges. This approach represents a significant stride towards transforming supply chain management into a more advanced, data-driven, and adaptive field. [ABSTRACT FROM AUTHOR]

Abstract The rapid growth of electric vehicle (EV) adoption globally underscores the need for sustainable and efficient supply chains, particularly in emerging economies like India. However, India’s EV industry faces critical challenges, including dependency on imported raw materials, limited domestic manufacturing, and inadequate charging infrastructure. This study addresses the gap in understanding how to optimize India’s EV supply chain to reduce import reliance and align with global best practices. The objective of the research is to develop actionable strategies for enhancing domestic EV production capabilities and sustainability. Using data from credible sources, including government and industry reports, statistical analysis was performed via ANOVA to compare India’s EV supply chain metrics with global leaders such as China and Norway. Advanced visualization techniques were also employed to analyze data interactions. The findings reveal that India’s domestic manufacturing meets only 20% of demand, while strategic investments in local mining and recycling could mitigate up to 40% of raw material needs by 2030. Furthermore, policy standardization and infrastructure development are critical for closing the gap with global benchmarks. These results highlight the importance of a cohesive national strategy to enhance India’s EV ecosystem. Future research should expand the comparative scope to other developing nations and explore innovative recycling technologies.

In the contemporary supply chain management landscape, the intricacies of managing a single vendor-multi-buyer network amidst stochastic demand pose significant challenges. This paper delves into optimizing such supply chains, emphasizing resilience in the face of uncertain demand scenarios. Leveraging the NSGA-II (Non-dominated Sorting Genetic Algorithm II), a powerful evolutionary optimization technique, we explore the multifaceted dimensions of supply chain optimization. The proposed framework aims to enhance the robustness and adaptability of supply chain networks by simultaneously addressing two key objectives: minimizing costs and maximizing service levels. By considering stochastic demand patterns, inherent uncertainties are meticulously accounted for, ensuring that the optimized solutions are efficient and resilient to unforeseen fluctuations in demand. This study comprehensively evaluates the single vendor-multi buyer supply chain model and highlights the efficacy of the NSGA-II algorithm in navigating the complex trade-offs inherent in supply chain optimization. By generating diverse Pareto-optimal solutions, the algorithm empowers decision-makers with actionable insights, enabling them to make informed choices that balance cost-effectiveness with service quality. Furthermore, this paper contributes to the evolving discourse on supply chain resilience by integrating advanced optimization methodologies with real-world supply chain dynamics. The findings underscore the importance of proactive optimization strategies in building resilient supply chain networks capable of withstanding the volatility of today's global marketplace. In conclusion, this research illuminates the path towards catalyzing resilience in single vendor-multi buyer supply chains, offering a nuanced understanding of the interplay between optimization algorithms, stochastic demand, and supply chain performance. Organizations can fortify their supply chain architectures through continuous refinement and adaptation, fostering agility and competitiveness in an ever-evolving business landscape. [ABSTRACT FROM AUTHOR]

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies. [ABSTRACT FROM AUTHOR]

This paper aims to discuss the concept of modeling and simulation for addressing transportation challenges in Malaysia's agriculture industry, focusing on the capabilities of distribution centers (DC) and collection centers (CC). To identify the optimal solution for the single-period vehicle routing problem (SP-VRP), we developed an optimization model that accurately represents the real-world problem, accompanied by a transportation problem simulation. The challenge involves determining the collection quantities, times, and routes to the CCs within the SP-VRP system. Consequently, we constructed a linear mixed-integer program to solve the SP-VRP. A comprehensive analysis of a sample problem demonstrates how our proposed approach is integrated. The results indicate that vehicle capacity is optimized efficiently, with an average capacity utilization of 100% across all tours and solving optimal routes allows for an evaluation of how well the model achieves this objective, as well as how different routes affect logistics performance. [ABSTRACT FROM AUTHOR]

Food waste is a serious worldwide issue that has an impact on the environment, society, and economy. This comprehensive review provides a detailed description of methods and approaches for reducing food waste, emphasizing the necessity of comprehensive strategies to tackle its intricate relationship with environmental sustainability, social equity, and economic prosperity. By scrutinizing the extent and impact of food waste, from initial production stages to final disposal, this comprehensive review underlines the urgent need for integrated solutions that include technological advancements, behavioral interventions, regulatory frameworks, and collaborative endeavors. Environmental assessments highlight the significant contribution of food waste to greenhouse gas emissions, land degradation, water scarcity, and energy inefficiency, thereby emphasizing the importance of curtailing its environmental impact. Concurrently, the social and economic consequences of food waste, such as food insecurity, economic losses, and disparities in food access, underscore the imperative for coordinated action across multiple sectors. Food waste can also be effectively reduced by various innovative approaches, such as technological waste reduction solutions, supply chain optimization strategies, consumer behavior‐focused initiatives, and waste recovery and recycling techniques. Furthermore, in order to foster an environment that encourages the reduction of food waste and facilitates the transition to a circular economy, legislative changes and regulatory actions are essential. By embracing these multifaceted strategies and approaches, stakeholders can unite to confront the global food waste crisis, thereby fostering resilience, sustainability, and social equity within our food systems. [ABSTRACT FROM AUTHOR]

Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. Results: The application of these ML models resulted in significant improvements across key operational areas. We achieved a 15% increase in demand forecasting accuracy, a 10% reduction in overstock and stockouts, and a 95% accuracy in predicting order fulfillment timelines. Additionally, the approach identified at-risk shipments and enabled customer segmentation based on delivery preferences, leading to more personalized service offerings. Conclusions: Our evaluation demonstrates the transforma-tive potential of ML in making supply chain operations more responsive and data-driven. The study underscores the importance of adopting advanced technologies to enhance deci-sion-making, evidenced by a 12% improvement in lead time efficiency, a silhouette coefficient of 0.75 for clustering, and an 8% reduction in replenishment errors. [ABSTRACT FROM AUTHOR]

In this work, we address a nationwide tactical planning for industrial gas supply chains, particularly argon. The proposed approaches follow as extensions of our previous work (Comp. & Chem. Eng., 161 (2022) 107778) in which a regional argon supply chain problem is addressed; in that work, both production and distribution could be represented in detail. Two different types of deliveries from the Air Separating Units (ASU) to customers, which involve single driver deliveries for short distance trips and sleeper team that require multiple days. The nationwide problem requires simplifications to keep the problem mathematically tractable, primarily the representation of production sites with different tier costs and the aggregation of customers in clusters. The regional problem addressed in our previous work is used as a benchmark case study for benchmarking. We then focus on a real-world problem that represents a nationwide argon supply chain. Despite the size of the models, near optimal solutions could be found in reasonable times. Finally, we highlight important features of the proposed approaches.

Industrial 4.0 (I4.0) is believed to revolutionize supply chain (SC) management and the articles in this domain have experienced remarkable increments in recent years. However, the existing insights are scattered over different sub-topics and most of the existing review papers have ignored the underground decision-making process using OR methods. This paper aims to depict the current state of the art of the articles on SC optimization in I4.0 and identify the frontiers and limitations as well as the promising research avenue in this arena. In this study, the systematic literature review methodology combined with the content analysis is adopted to survey the literature between 2013 and 2022. It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. Scholars can take this investigation as a means to ignite collaborative research that tackles the emerging problems in business, whereas practitioners can glean a better understanding of how to employ their OR experts to support digital SC decision-making. [ABSTRACT FROM AUTHOR]

This study investigates the impact of AI-driven demand forecasting on supply chain optimization in the retail sector. With the increasing complexity and competition in retail, accurate demand forecasting is crucial for operational efficiency. Traditional forecasting methods often fall short in addressing the dynamic nature of demand, leading to inefficiencies in inventory management and order fulfillment. This research explores the application of advanced AI models--Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Random Forest--in improving forecast accuracy and overall supply chain performance. Utilizing a quantitative approach, the study analyzes real-world data from various retail industries to evaluate the effectiveness of these AI models. The results reveal significant improvements in forecast accuracy, inventory management, and order fulfillment rates post-AI implementation. Specifically, the LSTM model demonstrated the highest accuracy with the lowest forecast error, leading to notable reductions in inventory discrepancies and enhanced order fulfillment rates. The study also identifies key performance indicators (KPIs) such as sales volume, inventory turnover rate, and stockout occurrences, all of which showed marked improvement following AI adoption. These findings underscore the potential of AI to address traditional forecasting limitations, offering substantial cost savings, operational efficiency, and a competitive advantage for retail businesses. The study concludes with recommendations for future research, including the exploration of AI's role in other supply chain functions and across diverse retail sectors. This research contributes to the growing body of knowledge on AI in supply chain management and provides actionable insights for enhancing retail operations through advanced forecasting techniques. [ABSTRACT FROM AUTHOR]

The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce. This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation. Objectives include investigating the transformative potential of PCEPs such as personalized product recommendations, dynamic pricing strategies, enhanced fraud detection, and optimized supply chain management; critically analyzing the ethical considerations of AI in e-commerce, including data privacy, algorithmic bias, and job displacement; and proposing comprehensive and practical strategies for responsible PCEP development and implementation, focusing on transparency, fairness, and accountability. Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner. [ABSTRACT FROM AUTHOR]

Nowadays, Energy has become one of the most important issues. Negative environmental effects of fossil fuels lead countries to use renewable and sustainable energy sources day by day. In addition, energy supply and security have been an motivator factor in this field. This study focus on biomass that renewable and sustainable energy source. According to studies in literature, energy production from biomass resource less than other energy sources. The most important reason for this is the logistics costs. Therefore, biomass supply chain optimization is an important issue. In this study, mathematical models of biomass supply chain are reviewed. When the studies are evaluated, there are three approaches in modeling biomass supply chains. These approaches are as follows: 1) Collection and Distirubiton, 2) Selection, 3) Clustering. In additon, researchers generally focus on single-aim mixed integer mathematical models. However, recently, it is seen that there are multi-aim models in the literature for minimizing emissions as well as supply chain costs. In this study, general information about biomass and biomass supply chain, studies of in this area, mathematical models, details of published papers are given. Objective functions, cost/income items, methods of these models are discussed in detail. [ABSTRACT FROM AUTHOR]

Energy storage is a foundational clean energy technology that can enable transformative technologies and lower carbon emissions, especially when paired with renewable energy. However, clean energy transition technologies need completely different supply chains than our current fuel-based supply chains. These technologies will instead require a material-based supply chain that extracts and processes massive amounts of minerals, especially critical minerals, which are classified by how essential they are for the modern economy. In order to develop, operate, and optimize the new material-based supply chain, new decision-making frameworks and tools are needed to design and navigate this new supply chain and ensure we have the materials we need to build the energy system of tomorrow. This work creates a flexible mathematical optimization framework for critical mineral supply chain analysis that, once provided with exogenously supplied projections for parameters such as demand, cost, and carbon intensity, can provide an efficient analysis of a mineral or critical mineral supply chain. To illustrate the capability of the framework, this work also conducts a case study investigating the global lithium supply chain needed for energy storage technologies like electric vehicles (EVs). The case study model explores the investment and operational decisions that a global central planner would consider in order to meet projected lithium demand in one scenario where the objective is to minimize cost and another scenario where the objective is to minimize CO 2 emissions. The case study shows there is a 6% cost premium to reduce CO 2 emissions by 2%. Furthermore, the CO 2 Objective scenario invested in recycling capacity to reduce emissions, while the Cost Objective scenario did not. Lastly, this case study shows that even with a deterministic model and a global central planner, asset utilization is not perfect, and there is a substantial tradeoff between cost and emissions. Therefore, this framework—when expanded to less-idealized scenarios, like those focused on individual countries or regions or scenarios that optimize other important evaluation metrics—would yield even more impactful insights. However, even in its simplest form, as presented in this work, the framework illustrates its power to model, optimize, and illustrate the material-based supply chains needed for the clean energy technologies of tomorrow. [ABSTRACT FROM AUTHOR]

Harnessing the power of data science is revolutionizing supply chain management (SCM) practices, enabling businesses to optimize operations, enhance efficiency, and reduce costs. This paper delves into the transformative impact of data science on SCM, exploring its diverse applications, challenges, and ethical considerations. Through comprehensive analysis and real-world examples, we elucidate the benefits of implementing data science in SCM. These benefits encompass enhanced demand forecasting, optimized inventory management, predictive maintenance, supply chain risk management, and improved customer experience. Each application is discussed in detail, showcasing how data science empowers businesses to make informed decisions, optimize resource allocation, and achieve operational excellence. However, integrating data science into SCM is not without challenges. Data availability and quality, technical expertise, integration and adaptability, explainability and transparency, and ethical concerns pose significant hurdles. To overcome these challenges, we provide strategies that address data issues, build technical expertise, facilitate integration and adaptability, enhance transparency and explainability, and address ethical considerations. By adopting data science in a strategic and thoughtful manner, businesses can unlock the significant benefits that data-driven supply chain optimization offers. Data science holds the potential to transform supply chains into more efficient, responsive, sustainable, and ethical operations, leading to increased competitiveness and enhanced customer satisfaction. [ABSTRACT FROM AUTHOR]

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits. [ABSTRACT FROM AUTHOR]

Implementing reverse logistics in the construction industry is considered a crucial method to achieve a circular economy. Despite a wealth of research focusing on improving reverse logistics systems, businesses still encounter challenges during the implementation process. Therefore, this study conducted a systematic literature review utilizing bibliometric methods to analyze 623 articles on reverse logistics in the construction industry published on Web of Science from 1995 to 2023. Additionally, a comprehensive review of 56 high-quality literature on obstacles to implementing reverse logistics in the construction industry and optimizing reverse supply chains was conducted. This review uncovered the current status and challenges of implementing reverse logistics in the construction industry and proposed potential solutions to address these issues. The main findings of this study include: (1) increasing academic interest in construction waste reverse logistics, with Chinese scholars leading the way and publications predominantly in environmental and construction journals, with limited coverage in logistics journals; (2) the primary obstacles to implementing reverse logistics in the construction industry lie in supply chain management, such as lacking deconstruction designs, incomplete recycling markets, difficulties in evaluating the quality of secondary materials, and insufficient supply chain integration; (3) proposing a framework for a construction industry reverse logistics supply chain ecosystem, aiming to establish a platform to facilitate online collection of construction waste, online transactions of secondary materials, end-to-end monitoring, and data analytics for consultation. [ABSTRACT FROM AUTHOR]

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