Supply Chain Optimization Research Articles

Supply Chain Management for the Engineering Procurement and Construction (EPC) Model: A Review and Bibliometric Analysis

China’s “Going Out” strategy and the “Belt and Road” initiative have significantly propelled the expansion of overseas contractual engagements, with a pronounced impact in the Engineering, Procurement, and Construction (EPC) sector. EPC contracting is increasingly recognized for its superior resource allocation and integration efficiency. Within the EPC framework, procurement is pivotal, exerting a direct influence on project cost, schedule, and quality parameters. The biblio-metric analysis of this paper reveals the research trend of EPC projects in the past decade, especially in supply chain integration, procurement management, and stakeholder management. This indicates an increasing interest in efficient project management and supply chain optimization in international engineering EPC projects. Although the application of supply chain integration in the manufacturing industry has been a successful experience, its practical application in construction and EPC projects is still insufficient. Integration strategies mentioned in the literature, such as achieving supply chain transparency through digital platforms, reducing operating costs, and optimizing delivery time, are directions that need to be further explored in this field. Effective supply chain integration and increased maturity of procurement management in EPC projects are still less involved areas. In addition, there is also a lack of research on how to manage high-risk and unpredictable risks in large international engineering projects. This article suggests that future research should focus on developing flexible supply chain management tools and practices to adapt to a project-oriented supply chain environment.

Research on Emission Reduction Decisions in Supply Chains Considering Vertical Spillover Effects and Low-Carbon Preferences

The growth in carbon emissions is increasingly exacerbating global warming. As the principal source of carbon emissions, companies can effectively enhance their emission reduction levels through the vertical spillover of emission reduction technologies to investigate the impact of vertical spillover rates, consumers’ low-carbon preference coefficients, emission reduction cost coefficients on optimal supply chain decisions, and profits in centralized and decentralized decision-making environments. Considering consumers’ preferences for low-carbon options, this paper constructs a Stackelberg game model under centralized and decentralized supply chain decision-making scenarios. It examines the effects of considering the vertical spillover effects of emission reduction versus not taking them into account. Using backward induction, this study optimizes the emission reduction levels of leading suppliers and manufacturers. The results indicate that an increase in consumers’ low-carbon preference levels and vertical spillover rate not only enhances the emission reduction levels of suppliers and manufacturers but also increases their profits. Conversely, an increase in emission reduction cost coefficients impedes emission reductions, with a more pronounced effect under vertical spillover conditions. In centralized decision-making, increases in vertical spillover rates, consumers’ low-carbon preferences, and decreases in emission reduction cost coefficients create a synergistic effect, resulting in greater increases in emission reduction levels and profits for suppliers and manufacturers compared to the sum of the effects of changes in individual coefficients. This finding provides new insights for governments in formulating relevant policies to promote corporate emission reductions.

RETRACTION: Auxiliary Cognition System‐Based Management Strategy Optimization of Supply Chain of New Energy in Oil–Gas Enterprises

<scp>RETRACTION</scp>: Auxiliary Cognition System‐Based Management Strategy Optimization of Supply Chain of New Energy in Oil–Gas Enterprises

Transformative Impact of AI and Cloud Technologies: A Comparative Analysis across Healthcare, Retail, and Mobile Financial Services

This article examines the transformative impact of artificial intelligence (AI) and cloud technologies across healthcare, retail, and mobile financial services sectors. Through a comprehensive analysis of industry-specific applications, we explore how these technologies are revolutionizing operational efficiency, customer experiences, and innovation in each domain. In healthcare, we investigate the role of CI/CD automation and scalable cloud infrastructure in improving system reliability and patient care. The retail sector analysis focuses on AI-driven customer insights and supply chain optimization. For mobile financial services, we examine AI applications in fraud detection and real-time analytics. The findings reveal significant improvements in operational efficiency, decision-making processes, and customer satisfaction across all three industries. However, we also identify unique challenges and opportunities specific to each sector. This article contributes to the growing body of literature on digital transformation and provides valuable insights for industry professionals and policymakers navigating the integration of AI and cloud technologies in their respective fields.

Research on supply chain efficiency optimization algorithm based on reinforcement learning

Supply chain efficiency is critical to enterprises and can affect their competitiveness. The supply chain faces an uncertain and complex external market environment, facing the problem of supply chain efficiency optimization; the traditional optimization method is ineffective, which can better face the current environment and deal with problems. It has advantages in optimizing supply chain efficiency and has been widely used. This paper first expounds on the importance of supply chain management status, the limitations of traditional supply chain management methods, and reinforcement learning in the application of supply chain optimization. Then, through experiments, reinforcement learning, supply chain optimization problems, and the analysis of related algorithm design, the optimal algorithm focuses on inventory management optimization. Finally, this paper points out the future research directions and development trend of the supply chain efficiency optimization algorithm based on reinforcement learning.

Optimizing last-mile delivery of essential medicines in Nigeria: Insights from Niger State’s health-care supply chain

Last-mile delivery of health commodities is a critical component of efficient supply chains, and third-party logistics (3PL) providers are pivotal in ensuring cost-effective and reliable product distribution. This study explores the implementation of last-mile delivery outsourcing by government-operated warehouses and drug management agencies (DMAs). This research focuses on the relationship between 3PL utilization and key performance metrics, such as cost reduction, timely delivery, and end-user satisfaction. This study combines a literature review, surveys, and interviews conducted with employees of the DMAs in Minna, Niger State, to establish significant correlations. The findings show that using 3PLs significantly reduced operational costs, improved the frequency of timely deliveries, and enhanced end-user satisfaction. These findings underscore the need for continued engagement with 3PL providers to optimize supply chain performance and improve service delivery outcomes. Managers are recommended to maintain and expand such collaborations to sustain these benefits.

Supply Chain Optimization Decisions for Environmentally Conscious Products with Preservation Investment and Carbon Tax Policy

Supply Chain Optimization Decisions for Environmentally Conscious Products with Preservation Investment and Carbon Tax Policy

Optimization of a sustainable supply chain for medical device industry under uncertainty and COVID-19 pandemic

Optimization of a sustainable supply chain for medical device industry under uncertainty and COVID-19 pandemic

FORMATION OF STRATEGIC DIRECTIONS FOR THE USE OF ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE TO ACHIEVE THE GOALS OF SUSTAINABLE DEVELOPMENT

Rapid artificial intelligence (AI) development opens new opportunities for enterprises to achieve sustainable development goals. This article summarizes the research results of applying AI to optimize production processes, reduce environmental impact, increase productivity, and strengthen social responsibility. The article aims to highlight the potential of AI for achieving enterprises' sustainable development goals and identify effective strategies for its application to increase economic efficiency, social responsibility, and environmental sustainability. A qualitative analysis of statistical data, official regulatory documents, books, and articles from accredited journals was used to achieve the goal. The obtained results showed that AI could be used to analyze production processes and forecast the demand for raw materials, optimize supply chains and reduce energy consumption, identify potential sustainability risks and develop strategies to prevent them, identify defects in production and control product quality in real-time, create personalized services and products for customers. The study highlights the importance of AI's ethical and safe use to ensure its positive impact on society and the environment. The practical value of the article lies in the systematic review of studies on the effects of AI on the sustainable development of enterprises. The obtained results can be used to improve production processes, increase enterprises' competitiveness, and promote sustainable development.

A systematic review on exploring the influence of Industry 4.0 technologies to enhance supply chain visibility and operational efficiency

This systematic review investigates the implications of Industry 4.0 technologies on supply chain visibility and operational efficiency. The primary aim is to discern the impact of technological integration on contemporary supply chain dynamics. Methods: A comprehensive search strategy identified 65 pertinent studies published between 2015 and 2023. The review adheres to systematic methodologies, employing the Critical Appraisal Skills Programme framework for quality assessment. Data synthesis incorporates qualitative and quantitative analyses to distill key themes and patterns. Results: The review unveils the pivotal role of information visibility in fortifying supply chain outcomes, emphasizing the need for a dual investment strategy encompassing technological solutions and a collaborative organizational culture. Regional variations in supply chain practices, insights from humanitarian supply chains, and the influence of environmental factors on agility broaden the understanding of Industry 4.0 implications. Organizations are urged to adopt a context-specific, adaptive approach, recognizing the significance of intangible assets and tailoring strategies to local contexts for optimal supply chain performance. This systematic review contributes a nuanced understanding of Industry 4.0’s transformative potential in supply chain management, emphasizing the interplay between technology, organizational culture, and regional contexts.

The Effect of ERP System Utilization, Inventory Management, and Interdivisional Collaboration on Supply Chain Operational Efficiency in the Automotive Industry

This study examines the impact of ERP system utilization, inventory management practices, and interdivisional collaboration on supply chain operational efficiency in Indonesia’s automotive industry. Using a quantitative approach, data were collected from 120 respondents and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The results indicate that ERP system utilization, inventory management, and interdivisional collaboration contribute to supply chain efficiency, with a combined effect explaining 67% of the variance in operational efficiency. Findings highlight ERP systems’ role in enhancing real-time data sharing, inventory management’s impact on stock optimization, and collaboration’s ability to streamline cross-functional processes. This study provides insights for industry practitioners seeking to optimize supply chain performance through technology adoption, robust inventory practices, and cohesive interdepartmental collaboration.

Data-driven digital marketing and battery supply chain optimization in the battery-powered aircraft industry through case studies of Rolls-Royce’s ACCEL and Airbus's E-Fan X Projects

This paper investigates the interplay between data-driven digital marketing strategies and battery supply chain optimization in the battery-powered aircraft industry, with a focus on Rolls-Royce's ACCEL and Airbus's E-Fan X projects. As the electric aircraft market emerges, these companies face the dual challenge of promoting technological advancements to a skeptical audience while navigating complex supply chain requirements. The study explores how data analytics and targeted content marketing are employed to enhance stakeholder engagement, build consumer trust, and communicate the benefits of electric aviation. By leveraging digital platforms, Rolls-Royce and Airbus effectively highlight key aspects such as battery performance, sustainability, and the safety of electric propulsion, addressing both technical and consumer concerns. Concurrently, the research delves into the intricacies of battery supply chain optimization, examining the processes involved in sourcing high-energy-density batteries, ensuring quality control, and adhering to regulatory standards. Advanced supply chain management techniques, including predictive analytics and real-time monitoring, are analyzed for their role in streamlining procurement and manufacturing processes. The integration of these strategies within digital marketing campaigns demonstrates how each company positions itself as a leader in the industry, emphasizing technological innovation and reliability. This study aims to provide a comprehensive understanding of how digital marketing and supply chain optimization are not only critical for promoting the adoption of electric aircraft but also for overcoming market skepticism and ensuring operational efficiency. By presenting case studies of two pioneering projects, ACCEL and E-Fan X, the research highlights best practices and strategic approaches that can guide other companies in the industry. The findings underscore the importance of a synergistic approach where digital marketing is informed by supply chain realities, creating a transparent and trustworthy narrative for consumers and stakeholders. Ultimately, this paper contributes to the discourse on how emerging technologies in aviation can be successfully marketed and operationalized, offering insights into the effective use of digital marketing and supply chain management in the rapidly evolving electric aircraft sector.

Combating food waste in the agricultural supply chain: A systematic review of supply chain optimization strategies and their sustainability benefits

Food waste represents a significant global challenge, with approximately one-third of all food produced for human consumption lost or wasted annually, leading to severe environmental, economic, and social consequences. The issue not only exacerbates food insecurity but also contributes to greenhouse gas emissions, waste of resources, and economic losses estimated at over $1 trillion each year. This paper aims to systematically review the causes of food waste across agricultural supply chains and explore effective supply chain optimization strategies to mitigate these losses. Through a comprehensive analysis, the paper identifies critical drivers of food waste, including post-harvest losses, inefficient processing, and aesthetic standards, while proposing technological innovations such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) as solutions to enhance supply chain efficiency. Furthermore, it evaluates policy frameworks that support waste reduction, highlighting the role of government incentives and regulations in promoting sustainable practices. The findings underscore the importance of reducing food waste not only for environmental sustainability but also for economic profitability, as optimizing supply chains can lead to substantial cost savings and increased revenue for businesses. Ultimately, the paper emphasizes the necessity for collaboration among stakeholders—including governments, industries, and non-profit organizations—to create systemic change and effectively combat food waste on a global scale. By fostering innovative practices and policies, we can pave the way for a more sustainable agricultural future, ultimately contributing to the alleviation of food insecurity and the enhancement of economic resilience.

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

Exploring the critical factors in pharmaceutical supply chains revealed during COVID-19 and addressing missing links

The COVID-19 pandemic revealed profound weaknesses in global pharmaceutical supply chains, resulting in widespread shortages of critical medical supplies, delayed drug manufacturing, and disruptions in vaccine distribution. These supply chain vulnerabilities were exacerbated by several key factors, including an overreliance on a limited number of countries for active pharmaceutical ingredients (APIs), insufficient local manufacturing capacity, and logistical constraints in transportation and distribution networks. The pandemic also exposed inefficiencies in regulatory frameworks, leading to delays in approvals and limited agility in responding to rapidly changing demands. Furthermore, the lack of transparency and real-time monitoring across global supply chains hindered effective decision-making and coordination, aggravating the crisis. This paper provides an in-depth analysis of the critical factors that contributed to pharmaceutical supply chain disruptions during COVID-19, identifying missing links such as the need for diversification of supply sources, enhanced flexibility in manufacturing processes, and the integration of advanced technologies like artificial intelligence (AI), blockchain, and automation for supply chain optimization. The analysis highlights how supply chain visibility, digital innovation, and risk management strategies could improve resilience in the face of future global health crises. In addition to identifying these challenges, the paper proposes solutions to address the missing links in pharmaceutical supply chains, including the decentralization of production, the development of regional manufacturing hubs, and the creation of public-private partnerships to bolster local production. The role of government policies in facilitating faster regulatory approvals, fostering international cooperation, and incentivizing innovation is also explored. Case studies on successful post-COVID-19 adaptations, such as the rapid scale-up of vaccine production and the reconfiguration of personal protective equipment (PPE) supply chains, offer insights into effective strategies for building more resilient systems. Ultimately, this paper underscores the importance of a collaborative, multi-stakeholder approach to ensuring the stability and security of pharmaceutical supply chains. It calls for proactive measures to strengthen global supply chain infrastructures, improve regulatory agility, and embrace emerging technologies that can predict and respond to disruptions. These steps are crucial to safeguarding the continuous delivery of essential pharmaceuticals during future pandemics or other large-scale disruptions, ensuring global health security in an increasingly interconnected world.

Sustainability Service Chain Capabilities in the Oil and Gas Industry: A Fuzzy Hybrid Approach SWARA-MABAC

This study aims to redefine the capabilities of the service supply chain in the Iranian oil and gas industry, where the concept of service chains has remained underdeveloped due to a traditional focus on conventional service chains. It seeks to compare the capabilities of the oil and gas service supply chain to those in other sectors and explore whether applied research is necessary to improve service chain performance. The study was conducted in three stages, involving ten petroleum and natural gas experts. The first stage focused on identifying key capabilities, yielding seven significant capabilities and 28 critical sub-capabilities. In the second stage, the SWARA fuzzy hybrid approach was employed to weigh and prioritize these capabilities, while the fuzzy MABAC methodology was used for strategic decision-making. Finally, the model’s sensitivity was assessed using fuzzy methods to validate the findings. The results highlight the highest priority capability for service providers: the ability to utilize information for updating information processing capacity in making decisions within the service supply chain. The selected location, G1, emerged as a key area of focus. This approach presents a novel method for optimizing service supply chain locations within the oil and gas sector. This paper introduces a unique approach to selecting optimal service supply chain locations in the oil and gas industry, addressing critical gaps in previous research. Employing advanced fuzzy hybrid methodologies provides valuable insights into improving service chain capabilities, offering a competitive advantage for companies operating in this sector.

Strategical and tactical supply chain optimisation for smart production planning and control 4.0

Industry 4.0 (I4.0) technologies generate new opportunities for developing smart models, algorithms and tools to support supply chain (SC) production planning and control (PPC), or smart PPC (SPPC 4.0). Paired with opportunities, challenges arise in integrating sustainability and resilience in SC network design (SCND) according under I4.0. The main novelty of this paper is to propose two multi-objective models that integrate strategical and tactical PPC decisions into a sustainable-resilient SC under I4.0 towards SPPC 4.0. Sustainability is incorporated in terms of reducing costs and CO2 emissions, and a job creation factor. Resilience is incorporated in terms of contracting support suppliers. To solve the multi-objective models, the augmented epsilon constraint method (AUGMECON) was used. This algorithm minimises a target objective function while it treats the others as constraints. Our experiments indicate that AUGMECON is sensitive to the choice of epsilon values. Furthermore, a synthetic data generation tool called SR1-SR2_SynthDataGenerator was developed. This tool generates input datasets to validate and evaluate our models against benchmarks. A stochastic model (SR2) is also proposed that, with small datasets, takes 8.39 seconds to solve. The proposed models can be useful for industries that seek sustainable and resilient SCs to adapt to changing environments.

Research on the Development Status, Influencing Factors, and Countermeasures of Digital Transformation of China’s Manufacturing Enterprises

The manufacturing enterprises’ digital transformation is a complex and long-term process that requires the joint efforts of the government, enterprises, and society. By strengthening digital technology capacity building, promoting business model innovation, optimizing supply chain management, strengthening data sharing and circulation, strengthening policy support, and promoting industry-university-research cooperation, manufacturing enterprises can effectively adapt to the opportunities and challenges of the digital economy era and attain sustainable growth. This paper seeks to investigate the development status, key influencing factors, and corresponding countermeasures of manufacturing enterprise’s digital transformation. With the rapid growth of the digital economy, the manufacturing enterprise’s digital transformation has become a prominent way to enhance competitiveness, optimize production processes, and promote product innovation.

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.

Digital Transformation Governance Model and Implications for Cosmetic Companies: A Case Study of LOreal

Abstract: As digital technology advances, more companies are undergoing digital transformation, leading to benefits and profits. The cosmetics industry is also following this trend. This study aims to explore the governance model of cosmetic companies during digital transformation and its impact on the development of the industry, using LOreal as a case study for in-depth analysis. By examining LOreals digital transformation case study, this paper reveals the key initiatives and impacts of digital transformation in the cosmetics industry. Specifically, the paper examines LOreals digital transformation initiatives in virtual reality applications, supply chain and operations optimisation, e-commerce platforms and AI product innovation, and analyses the positive impact of these initiatives on the company's business, customer experience and market performance. The study also offers some recommendations for cosmetic companies to address the challenges and risks that may arise during the digital transformation process. By analysing LOreals digital transformation experience in depth, this paper provides a valuable reference for the cosmetics industry to guide other companies to succeed in digital transformation and enhance the overall competitiveness of the industry.