Supply Chain Optimization Research Articles

Unveiling the impact of digital transformation on carbon emissions: evidence from China’s construction enterprises

ABSTRACT This paper examines how digital transformation (DT) impacts carbon emission intensity (CEI) in construction enterprises (CEs) amid global low-carbon development concerns. Using a difference-in-difference method on data from 170 Chinese CEs, the study finds that DT significantly reduces CEI. Labour structure upgrading partially mediates this relationship, while the green innovation and green supply chain optimization do not show mediating impacts. The constraining impact of DT on CEI is significant in state-owned and large enterprises but not in non-state-owned and small ones. These insights help understand DT’s role in promoting environmentally friendly growth in CEs.

From Reality to Virtuality: Revolutionizing Livestock Farming Through Digital Twins

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.

Data-Driven Analysis of Supply Chain Integration’s Impact on Procurement Performance in International EPC Projects

Integrated supply chain management is essential for enhancing procurement efficiency and project outcomes in international engineering, procurement, and construction (EPC) projects. Despite its acknowledged importance, there is a paucity of understanding regarding the precise effects of supply chain integration on procurement processes in this context. This research analyzes 45 projects overseen by prominent Chinese international contractors to bridge this gap. The research employs Pearson correlation analysis to identify robust correlations among critical components of supply chain integration, procurement management, and procurement performance. Essential elements, including operational performance, procurement contract management, and stakeholder engagement, are identified as critical determinants of project success. The study seeks to examine how international EPC contractors incorporate the complete procurement management process via supply chain integration. Contractors can improve operational efficiency and procurement management by forming partnerships with stakeholders to acquire and integrate resources, hence enhancing project outcomes. This ongoing enhancement process improves procurement efficiency and bolsters the competitiveness of Chinese contractors in the global EPC market. The study underscores the efficiency and sustainability of procurement processes, demonstrating how optimized supply chain integration advances broader sustainability objectives by minimizing waste, enhancing resource utilization, and promoting environmentally and socially responsible procurement practices in international projects. The research offers significant empirical data and insights for academic inquiries and actual company implementations.

Integration and Optimization of Route Planning and Inventory Management in Supply Chain Transaction Systems: A Comprehensive Analysis

This comprehensive article analysis explores the integration and optimization of route planning and inventory management within supply chain transaction systems, examining their transformative impact across various industries. The article investigates the evolution of modern logistics operations, emphasizing the crucial role of advanced technologies, blockchain frameworks, and integrated systems in enhancing operational efficiency. Through a detailed examination of core components including algorithmic foundations, real-time tracking systems, demand forecasting mechanisms, and unified data architectures, this article presents a holistic view of contemporary supply chain optimization. The article analysis encompasses multiple sectors, including retail, e-commerce, and manufacturing, demonstrating how integrated systems have revolutionized traditional approaches to inventory management and route optimization. By exploring performance metrics, return on investment, and future trends, this article provides valuable insights into the implementation strategies and scalability considerations essential for modern supply chain systems. The article highlights the significance of cross-functional optimization and cloud-based solutions in achieving operational excellence while maintaining high levels of customer satisfaction and cost efficiency.

IoT Applications in Healthcare and Industry: Current State, Challenges, and Future Perspectives

The Internet of Things (IoT) has revolutionized various domains by enabling seamless communication between devices and systems. The Internet of Things (IoT) has revolutionized various domains by enabling seamless communication between devices and systems. This technological marvel connects the physical and digital worlds, creating smarter environments and driving unprecedented innovation. From healthcare to industry, transportation to agriculture, IoT is reshaping how we interact with technology, making processes faster, more efficient, and remarkably intuitive. In healthcare, IoT empowers patients with remote monitoring, smart devices, and real-time data insights, ensuring better outcomes and accessible care. In industries, IoT drives automation, predictive maintenance, and optimized supply chains, reducing costs and boosting productivity. This paper explores the applications of IoT in healthcare and industry, highlighting its current state, challenges, and future prospects. A comprehensive literature review is conducted to understand the advancements, and potential solutions to key challenges such as security, interoperability, and data management are discussed. Finally, the paper presents a roadmap for future research to maximize IoT's potential in these critical sectors

Harnessing artificial intelligence for business optimization: Enhancing efficiency and sustainability in SMEs

Small and Medium Enterprises (SMEs) play a crucial role in driving economic growth and innovation, yet they often struggle with limited resources, operational inefficiencies, and sustainability challenges. In this context, artificial intelligence (AI) presents a transformative opportunity for optimizing business processes, improving decision-making, and promoting sustainable practices. This review explores the integration of AI technologies to enhance efficiency and sustainability in SMEs, focusing on practical applications such as automation, predictive analytics, and intelligent resource management. By leveraging AI, SMEs can automate repetitive tasks, optimize supply chains, and streamline financial operations, leading to cost reductions and productivity gains. Additionally, AI-powered customer engagement tools, such as chatbots and personalized marketing systems, allow SMEs to better understand customer behavior and enhance service delivery. On the sustainability front, AI enables real-time monitoring of energy consumption, waste management, and resource utilization, helping SMEs align with environmental and regulatory standards. However, the adoption of AI in SMEs faces significant challenges, including data quality issues, limited budgets, and a lack of technical expertise. This review proposes a strategic approach to implementing AI solutions, emphasizing the need for tailored tools, effective data management, and employee training. By examining successful case studies, we highlight how AI-driven optimization has empowered SMEs to overcome operational hurdles and achieve sustainable growth. As the business landscape becomes increasingly competitive and sustainability-focused, integrating AI is not only beneficial but essential for SMEs seeking long-term resilience. This study aims to provide SMEs with actionable insights into adopting AI technologies, ultimately fostering efficiency, innovation, and sustainability in their operations. Keywords: Artificial intelligence, Small and medium enterprises (SMEs), Business Optimization, Review.

Aging Midstream Supply Chain in the Oil and Gas Industry: Issues and AI-Based Solutions

The paper attempts to identify supply chain issues in the midstream sector of the oil and gas industry due to aging infrastructure. We identify four key issues – safety, regulatory compliance, cost, and supply chain optimization. We used extant literature to assess the potential impact of the aging infrastructure on the aforesaid four issues. We investigate the possible solutions based on emerging artificial intelligence technologies and suggest applications of the same. Suggested solutions are based on our investigation into the oil and gas and technology industries, making our research interdisciplinary.

AI for Optimizing Supply Chain Management

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, with supply chain management (SCM) being a notable beneficiary. This research paper explores the impact of AI on optimizing supply chain processes, focusing on its applications in predictive analytics, natural language processing, robotics and automation, blockchain, and the Internet of Things (IoT). Through a mixed-method approach involving quantitative surveys, qualitative interviews, and secondary data analysis, the study evaluates how AI-driven solutions enhance key performance indicators (KPIs) such as inventory turnover, lead time, order accuracy, and cost reduction. The results indicate substantial improvements in these KPIs post-AI implementation, highlighting AI's role in achieving greater operational efficiency, accuracy, and cost-effectiveness in SCM. Challenges associated with AI adoption, including data quality, integration with legacy systems, and skill gaps, are also discussed. The paper provides actionable insights and recommendations for organizations aiming to leverage AI technologies to optimize their supply chain operations. This research contributes to the growing body of knowledge on AI in SCM and offers practical guidance for industry practitioners

The role of artificial intelligence and machine learning in optimizing U.S. healthcare supply chain management

This paper aims to review the role of Artificial Intelligence and Machine Learning in managing the healthcare supply chain in the United States. Healthcare supply chains face several challenges such as fragmentation, lack of real-time visibility, and inventory management issues. However, there are solutions with the help of AI and machine learning, including the availability of predictive analytics to improve demand forecasting, optimization algorithms for inventory and logistics, and automated quality control. Application areas demand forecasting, supplier selection, logistics optimization, quality control, and real-time tracking. The applications of AI in healthcare supply chains have the potential to improve the healthcare supply chain in terms of reduced costs, increased efficiency, optimized decision-making, and better patient outcomes. However, the implementation experience has shown several challenges such as data quality, privacy concerns, regulatory compliance, and workforce adaptation within organizations. Successful implementations in various health organizations in the US give valuable insights into how AI could be well implemented. The future presents several opportunities for supply chain optimization with the rise of blockchain and Internet of Things (IoT) integration. For healthcare supply chains to adopt AI, organizations should have specific AI plans, start with pilot projects in high-impact areas, invest in data infrastructure, and ensure strong leadership support. As AI becomes increasingly critical for competitive advantage, it has the potential to create more resilient, efficient, and patient-centric supply chains in the US healthcare system.

AI-enabled intelligent inventory and supply chain optimization platform for SMEs

An AI-enabled intelligent inventory and supply chain optimization platform offers SMEs a transformative solution to streamline operations, improve forecasting accuracy, and reduce costs. SMEs often face challenges in managing inventory efficiently, forecasting demand, and maintaining strong supplier relationships, largely due to limited resources and fragmented data. This platform integrates data from multiple sources such as sales, ERP, and supplier systems into a centralized system, enabling comprehensive visibility across inventory and supply chain functions. The platform employs machine learning algorithms to analyze historical data, identify trends, and predict demand patterns, resulting in more accurate forecasting and optimized stock levels. With predictive analytics, SMEs can set dynamic reorder points, minimizing overstock and stockouts while maintaining optimal safety stock. Additionally, the platform provides real-time tracking of shipments and supplier performance, allowing SMEs to monitor logistics in real-time and make informed, proactive decisions. Intelligent automation tools further enhance productivity by automating reorder processes, handling order generation, and reducing manual intervention in restocking. Benefits of this AI-driven platform include increased operational efficiency, reduced inventory holding and ordering costs, and enhanced customer satisfaction through timely order fulfillment. By optimizing stock levels and supply chain flows, SMEs can adapt quickly to market fluctuations and demand shifts, gaining a competitive edge. While challenges remain, such as data integration and employee training, the platform’s scalable design allows SMEs to start small and expand as needed. As AI technology advances, SMEs adopting these intelligent systems will position themselves for sustainable growth, improved resilience, and higher customer satisfaction in a rapidly evolving market landscape.

Digitalization and Sustainability of Supply Chains: Assessing the Potential of the DYLLI Application in the Hotel, Restaurant, and Catering Industry

This article investigates the integration of Information and Communication Technologies (ICT) to enhance service quality and sustainability within the Hotel, Restaurant, and Catering (HoReCa) sector. The study aims to explore how improved communication through ICT can optimize supply chain processes and elevate service standards. A usability analysis was conducted via surveys among HoReCa companies to assess perceptions of a demo application designed to facilitate sustainable practices. Additionally, a competitiveness analysis, grounded in Porter’s Five Forces model, examines the dynamics of mobile applications in the HoReCa market. The usability analysis validates business model assumptions while providing insights into market challenges and opportunities. The findings indicate that volatile commodity prices and recruitment difficulties are significant challenges for restaurateurs, highlighting the need for tools that support sustainable operations. The proposed ICT solutions serve as innovative tools that improve communication between HoReCa businesses and their suppliers, thereby directly influencing service quality. By promoting transparency in pricing and collaboration terms, these solutions align with sustainable supply chain principles, addressing environmental concerns while meeting evolving consumer expectations. This research contributes to the literature on competitiveness in the HoReCa sector by emphasizing the pivotal role of technology in fostering both service quality and sustainable development.

AI-Based Supply Chain Risk Management

As the complexity of global supply chains increases, supply chain risk management becomes increasingly important. The development of artificial intelligence (AI) technology provides new tools and methods for managing supply chain risks. This paper aims to review the application of AI in supply chain risk management, exploring current research progress, challenges, and future development trends. Through a systematic literature search and analysis, this paper examines relevant papers from databases such as Google Scholar, Web of Science, EI, and Scopus, discussing the specific applications of machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms in supply chain risk management. The study finds that these AI technologies demonstrate significant advantages in risk prediction, anomaly detection, image recognition, text mining, supply chain optimization, and emergency strategy formulation. However, issues related to data privacy and security, technical complexity, and implementation difficulties remain major challenges in current applications. In the future, as AI technology advances and interdisciplinary integration develops, supply chain risk management will have more opportunities. This study provides specific application suggestions for enterprises and decision-makers and points the way for future research.

Supply Chain Analytics, Definitions, Characteristics and Applications: A Systematic Literature Review

While there is a high uptake of BDA in the realm of supply chain management, in the view of automation supply chains and improving their value proposition by providing more accurate data for demand forecasting. There are material knowledge gaps on the SC-specific analytics applied to match demand, albeit the existing knowledge could be more amorphous. From this backdrop, the study endeavored to analyze extant literature within the ambit of BDA to unpack the current trends and possible future research directions to foster the application of BDA in SC contexts. The study adopted a systematic literature review of the extant literature published between 2014-2023. The study adopted the five-stage iterative procedure used in the systematic review methodology. The review's findings depict extensive use of big data analytics in matching demand and supply and supply chain optimization. The findings of this study adduce almost non-rebuttable evidence that big data analytics can be applied in procurement, inventory control, logistics, and order processing. Under the auspices of BDA is the SCA. Arguably, extant research has demonstrated the capability of SCA in mitigating contemporary SC risks such as mismatches between demand and supply, sub-optimal SCs, and underutilization of the supply chain infrastructures at the cusps of various echelons.

Transformer-based probabilistic demand forecasting with adaptive online learning

Demand forecasting is crucial for the operation and planning of the energy and power industry. Accurate demand forecasting can assist decision-makers in reducing operational or planning costs while optimizing supply chains and resource utilization. Due to various uncertainties and dynamically changing environments, traditional offline deterministic forecasting methods may face difficulty in forecasting demands accurately and be unable to effectively quantify the uncertainties in forecasts. To adapt to dynamic environmental changes, several online learning methods have been proposed, which require large computational resources. To overcome this limitation, this paper proposes a Transformer-based probabilistic demand forecasting with an adaptive online learning algorithm. More specifically, we design a probabilistic decoder for the Transformer to quantify forecast uncertainty. Furthermore, an adaptive online learning algorithm is employed to selectively update parameters based on an adaptive update strategy, reducing computational burden while allowing for timely adaptation to dynamic environmental changes. The performance of the proposed method is evaluated on multiple benchmark datasets and real electricity demand data from fused magnesium furnace (FMF). The results show that the proposed method exhibits superior accuracy in both deterministic and probabilistic forecasting scenarios. Finally, through ablation experiments, the effectiveness of the proposed adaptive online probabilistic forecasting algorithm is validated.

The Impact of Live E-commerce on Supply Chain Management: Challenges, Adaptations, and Strategic Optimizations

The explosive growth of live e-commerce has accelerated the transformation of supply chain management, where the efficiency and flexibility of the supply chain directly influence the success of live e-commerce. Therefore, optimizing supply chain management has become crucial for industry development. Live e-commerce, with its real-time interaction and instant purchasing characteristics, has profoundly impacted supply chain management. The rise of live e-commerce has driven supply chains to become more streamlined, personalized, and intelligent to meet consumer demands for freshness and customization of products. Additionally, the data-driven nature of live e-commerce enables better demand forecasting, reduces inventory backlogs, and enhances overall operational efficiency. This study explores the specific impacts of live e-commerce on supply chain management, including its effects on supply chain structure, logistics efficiency, inventory management, and the application of information technology. The aim is to provide strategic recommendations for supply chain optimization, helping enterprises enhance their competitiveness and promoting the healthy and sustainable development of the live ecommerce industry.

Improving Collaboration and Communication Strategies to Optimise Supply Chain in South Eastern Nigeria in Response to Currency Devaluation: A Holistic Approach

The depreciation of currency presented substantial obstacles for firms operating in South Eastern Nigeria, affecting the price of acquiring goods and services, the dynamics of the supply chain, and the overall ability to compete. To overcome these issues, it was essential to improve the coordination and communication methods among all parties involved, since this was key to maximising the efficiency of supply chains. This study employed a holistic strategy to reduce risks, enhance resilience, and take advantage of opportunities in the face of currency changes. Recent research highlights the crucial importance of cooperation and communication in optimising supply chains, especially in locations that are susceptible to currency depreciation. Efficient cooperation among stakeholders was crucial for actively tackling difficulties that arose from currency depreciation. Clear and open communication promotes confidence, simplifies the exchange of information, and allows stakeholders to synchronise their expectations and priorities. Collaboration and communication played a particularly important role in South Eastern Nigeria, which was known for its varied socioeconomic and political factors. This article examined the difficulties and advantages linked to the depreciation of currency in South Eastern Nigeria. It suggests a complete method to improve cooperation and communication techniques for optimising the supply chain. This article offered practical advice for firms in the area to successfully and sustainably overcome problems posed by currency depreciation. These recommendations were based on insights from current research and real-world experiences. Keywords: Currency depreciation, Teamwork, communication, Supply chain optimisation, South Eastern Nigeria.

CHALLENGES AND OPPORTUNITIES FOR THE DEVELOPMENT OF LOGISTICS HUBS IN UKRAINE

Logistics, as a key element of modern supply chains, is increasingly becoming the subject of deep engineering research and development. The construction of logistics complexes in today's world is a critical aspect for the development and functioning of global economic systems, as it allows optimizing supply chains and organizing logistics processes to meet the high standards of consumers and business partners. Logistics complexes play a crucial role in global trade by ensuring the fast and efficient delivery of goods. The growth of e-commerce volumes enhances their significance, requiring more space for the storage and processing of goods. These complexes are strategically important for enterprises as they contribute to efficiency improvement, delivery time reduction, and cost optimization. Logistic infrastructures provide flexibility to respond to changing market conditions and support competitiveness at the international level. Accordingly, the construction of logistics complexes plays a strategic role in economic development and supports global business. Objective: The work aims to assess, analyze, and justify organizational-technical factors in optimizing the construction of logistics complexes. Research Object: Construction of logistics complexes as part of modern supply chains and their role in global economic systems. Research Subject: Organizational-technical factors influencing the process of construction of logistics complexes, as well as methods for their optimization to enhance the efficiency and competitiveness of logistic systems. Presentation of Material: To achieve the research objective, the following tasks were performed: analyzed the current state of warehouse logistics in Ukraine and the role of logistic hubs; analyzed the impact of innovative and sustainable materials on the construction of logistics complexes; studied the provision of flexibility and adaptability of logistics complexes to changing market conditions; analyzed resource utilization during construction. Research Results: The research results focus on improving organizational and technological aspects of the construction of logistics complexes through effective management of construction processes, implementation of innovative technologies, and optimization of resource utilization. Such measures contribute to increased productivity and competitiveness of logistic systems, as well as sustainable development and enhanced efficiency of the supply chain.

A novel approach for optimisation of bioenergy supply chain: Integrating mathematical programming, Geographic Information System, and Analytic Hierarchy Process

The crucial role of sustainable energy in addressing environmental challenges and using agricultural waste for bioenergy supports a circular bioeconomy. This paper presents a four-stage approach to optimise a biorefinery supply chain for Ireland’s agricultural waste sector. The spatial availability of agricultural waste was assessed, followed by evaluating land suitability for biorefinery development under economic, environmental, and social constraints. A hybrid method using GIS and AHP was employed to rank suitable areas, and a mathematical model was used for supply chain optimisation. Results showed only 12 % of the study area is highly suitable for biorefineries, with an average transportation distance of 39 km, leading to reduced costs and emissions. The framework’s efficiency is highlighted by a transportation cost of only 4 %, compared to 16 % in previous studies. This research fills a gap in bioenergy supply chain management by demonstrating advanced tools for optimising sustainability and site suitability.

Towards circularity in the wind industry: Optimal reverse supply network design under various policy scenarios

Wind energy is key to supply renewable energy. However, the increasing number of end-of-life wind turbines is still predominantly landfilled, while regulatory aspects such as waste shipment and landfilling rules hinder the development and scalable implementation of reverse supply networks.This work aims to understand how EU directives impact the structure and viability of circularity-enabling networks by investigating the optimal reverse supply network design for end-of-life wind turbine blades under different policy scenarios. Three policy scenarios were explored through a Mixed-Integer-Linear-Programming model: (i) ‘as-is’; (ii) ‘EU Proposal 2021/0367′, removing transboundary restrictions on waste shipments; (iii) ‘Landfilling Ban’ enforcing an EU-wide ban on landfilling composites. The optimal reverse supply networks with minimum costs were identified for each scenario, contextually determining location and sizing of recycling facilities and calculating landfilling quota and GHG emissions. The costs and emissions were minimum for the EU Proposal scenario, at 15,706,041€ and 2,081 tCO2e respectively. A sensitivity analysis on landfilling gate fees highlighted that they should be significantly increased to incentivise higher recycling rates and close material loops.This research is the first to evaluate the effects of policy initiatives on the shaping of optimised reverse supply chains through mathematical programming methods. The work contributes to the waste management literature by designing optimal circular supply chain networks for the management of waste from wind turbines decommissioning at the EU-level to improve sustainability of renewable energy installations.

Environmental uncertainty, supply chain, and stability of sustainable green innovation:Based on micro evidence from energy-intensive enterprises

In the context of the low-carbon transformation of energy-intensive enterprises, the stability risks of continuous green innovation and their transmission mechanisms within the supply chain warrant attention. Specifically, will these stability risks be transmitted along the supply chain? If so, how are they transmitted? Therefore, this study focuses on Chinese energy-intensive enterprises and their upstream and downstream companies from 2008 to 2021. It explores the transmission effects of stability risks of continuous green innovation within the supply chain and the role of environmental uncertainty. By quantifying stability risk indicators of continuous green innovation and analyzing supply chain mechanisms, the study reveals the asymmetry of risk transmission and the directional and moderating effects of environmental uncertainty. The results indicate that stability risks of continuous green innovation primarily transmit upstream in the supply chain. Mechanism variables include financing constraints, degree of green transformation, and coordination of supply relationships. Additionally, the study finds that differences in supply structure contribute to heterogeneity in the transmission effect. These findings provide theoretical support for risk management strategies of energy-intensive enterprises in green innovation. It also provides practical guidance on strengthening coordination in green innovation and optimizing supply chain structure to reduce stability risk exposure.