Supply Chain Optimization Research Articles

The Impact of Supply Chain Concentration on Firm Value - Based on the Mediating Effect of Green Technology Innovation

This paper constructs a fixed-effects panel model based on data from listed com-panies in heavy pollution industries in Shanghai and Shenzhen A-shares from 2013 to 2023, and empirically analyzes the impact of supply chain concen-tration on enterprise value and the intermediary role of green technology in-novation. The results show that, on the one hand, supply chain concentration significantly suppresses enterprise value, possibly due to the increased inter-nalization costs of environmental risks caused by excessive control over up-stream and downstream activities. On the other hand, green technology innova-tion plays a partial inter-mediary role between supply chain concentration and enterprise value, reducing costs through technological spillover and collabora-tive R&D, alleviating the neg-ative impact of concentration on enterprise value. Heterogeneity analysis reveals that non-state-owned enterprises are more sig-nificantly affected by supply chain concentration, while equity-independent firms face greater negative impacts, and non-equity-independent firms experi-ence smaller and insignificant effects. When optimizing supply chain concen-tration, companies should balance concentration with investment in green in-novation to enhance long-term competitiveness and sustainability

Optimizing Supply Chain Management with ChatGPT: An Analytical and Empirical Multi-Methodological Study

This work explores how a leading language model, ChatGPT, can improve every aspect of supply chain management (SCM) by using a multi-methodological approach: quantitative analysis, qualitative case studies, and simulation models, to set goals that delve into the efficiency of ChatGPT in enhancing demand forecasting accuracy, improving decision-making processes, and highlighting the best practices for its deployment across different SCM tasks. Empirical results indicate that ChatGPT significantly increases the accuracy of the forecast, and the efficiency of decision-making compared to traditional methods. Qualitative insights reflect positive feedback from supply chain professionals, and best practices identified in areas such as predictive maintenance, and automation of customer service. The key findings have a great number of implications for SCM practitioners, theorists, and policymakers, indicating the potential of the model for transforming supply chain operations while pointing at avenues for future research on AI integration and its impact assessment.

Optimal capacitated multi-product robust cement supply chain Network design considering carbon emission policies

Optimal capacitated multi-product robust cement supply chain Network design considering carbon emission policies

A study on the impact of artificial intelligence on demand forecasting in food industries

This study examines how artificial intelligence (AI) is transforming the food supply chain by improving forecasting, demand planning, and operational efficiency. AI technologies like predictive analytics and machine learning enhance accuracy and responsiveness but face challenges such as data quality, system integration, and high costs. By surveying 370 food industry professionals, the research explores the benefits and barriers of AI adoption, providing actionable insights to optimize supply chain processes. The findings aim to support businesses and policymakers in leveraging AI strategically for competitive advantage and supply chain resilience.

Research on the evaluation of China's Supply Chain Finance policy based on text mining.

Supply Chain Finance (SCF) aims to manage the capital flow, logistics flow, and information flow of small and medium-sized enterprises (SMEs) in the upstream and downstream of the supply chain while optimizing supply chain risk control. Like other types of financial services, the development of SCF is highly influenced by policy factors; however, related research remains relatively limited. This study aims to explore the current state of SCF policymaking in China and provide scientific recommendations for the development of SCF from a policy perspective. First, using the BERTopic model, 3,439 SCF-related academic papers and 181 central-level SCF policy texts from the CNKI database were analyzed for thematic clustering. Then, by comprehensively considering the thematic distribution of SCF research and the operational characteristics of SCF, the Policy Modeling Consistency (PMC) Index model was constructed to evaluate SCF policy texts. The findings reveal several issues in China's SCF policymaking: limited thematic focus, fluctuating levels of policy formulation, and significant homogenization of policy content. The study proposes several optimization recommendations for SCF policies, including expanding the scope of policy focus, fostering synergy among different types of policies, diversifying the use of policy tools, and broadening the range of target groups addressed by policies.

Research on the Performance Evaluation of Green Supply Chain of Papermaking Enterprises based on AHP

In the context of increasingly stringent global environmental regulations, paper enterprises need to optimize supply chain management and improve green supply chain performance to achieve a balance between economic benefits and environmental sustainability. This paper builds a green supply chain performance evaluation system based on the analytic hierarchy process (AHP), selects three dimensions of economic performance, environmental performance, and social performance, and calculates the index weight through expert scoring and consistency tests. The results show that economic performance (0.56) is the dominant factor in enterprise decision-making, and the energy consumption reduction rate (0.45) and production cost reduction rate (0.30) are the core factors. The environmental performance (0.29) is followed by the carbon emission reduction rate (0.50), which indicates that enterprises have invested in energy conservation and emission reduction but still need to optimize; Social performance (0.15) has the lowest weight, and supply chain transparency and social responsibility implementation still need to be strengthened. The final performance score of 76.9 indicates that the enterprise has achieved some results in green supply chain management, but there is still room for improvement in green product promotion, carbon footprint optimization, social responsibility fulfillment, and other aspects. In this paper, short-term optimization strategies (energy saving, consumption reduction, recycling efficiency improvement) and long-term optimization strategies (intelligent green supply chain, carbon neutral supply chain construction) are proposed to provide decision support for the green transformation of paper enterprises.

AI Transformation in JD Edwards EnterpriseOne: A Technical Deep Dive

The integration of Artificial Intelligence (AI) with JD Edwards EnterpriseOne (JDE E1) represents a transformative evolution in Enterprise Resource Planning (ERP) systems, fundamentally changing how organizations manage their business processes. This technical article explores the comprehensive impact of AI integration across various functional domains of JDE E1, examining both the technological requirements and organizational implications. It investigates core AI technologies enhancing JDE E1, including predictive analytics and machine learning implementations while analyzing the architectural considerations necessary for successful integration. The article delves into practical applications across financial operations, supply chain optimization, and manufacturing intelligence, providing insights into how AI transforms these critical business functions. It also addresses implementation considerations, emphasizing technical prerequisites and integration strategies essential for successful AI adoption. Furthermore, the article examines future trends in natural language processing, advanced automation, and cognitive services, offering organizations a roadmap for leveraging AI capabilities within their JDE E1 environments to achieve improved operational efficiency and competitive advantage.

Optimizing Supply Chain Networks for Quality Compliance: A Comprehensive Analysis

This article examines the critical challenges and opportunities in optimizing supply chain networks for quality compliance in today's globalized business environment. It explores how organizations navigate complex quality management requirements across diverse geographical locations while integrating advanced technologies for enhanced performance. The article investigates multi-criteria decision frameworks, predictive analytics, IoT-enabled monitoring, and blockchain-based traceability solutions through comprehensive case studies in the pharmaceutical and food & beverage industries. Through the evaluation of implementation strategies and risk mitigation approaches, the study provides insights into successful quality management system deployment and offers practical recommendations for organizations seeking to enhance their supply chain quality capabilities.

Lagrangian relaxation-based heuristics for solving uncertain supply chain network design problem addressing supplier’s ESG factor

Purpose The uncertain supply chain network design (SCND) problem, considering suppliers’ environmental, social and governance (ESG) ratings, has been infrequently addressed in the literature. Looking at the importance of ESG ratings in achieving supply chain sustainability, this study aims to fill the gap by incorporating supplier ESG factors into SCND within an uncertain environment. Design/methodology/approach This paper presents a multi-period, multi product SCND model that integrates ESG factors and accounts for uncertainties in supply and production capacities. The model seeks to minimize total operational costs by determining the optimal selection of plant and warehouse locations across multiple time periods. Uncertainties in supply and production capacities are managed through a chance-constrained programming approach with right-hand side stochasticity. A Lagrangian relaxation-based heuristic method is applied to address the NP-hard nature of the problem. Findings The efficacy of the proposed model is illustrated through a numerical example, demonstrating its capability to optimize material flows across the supply chain under uncertain conditions. The model simultaneously considers economic and ESG factors in procurement decisions. A sensitivity analysis is conducted to examine different operational scenarios and their implications on the model’s outcomes. Originality/value To the best of the authors’ knowledge, this study is one of the first to integrate ESG factors into SCND under uncertainty. The proposed model provides a robust framework for decision-makers to optimize supply chain operations while considering both economic and ESG objectives in an uncertain environment.

Can smart manufacturing benchmark the complex landscape of global success?

PurposeThe globalization of markets poses great challenges, and thus, the manufacturing businesses trying to expand their operations to cater to a global audience have to undergo significant transformations. Therefore, this research aims to identify key challenges and elucidate the critical success factors (CSFs) required for the global growth of manufacturing companies on a worldwide scale.Design/methodology/approachA range of interval-valued spherical fuzzy sets (IVSFs) and flexible methodologies such as the analytic hierarchy process (AHP) and data envelopment analysis (DEA) have been employed to evaluate the issues in detail. It calculates the effectiveness delivered by each critical success factor (CSF) and identifies the factors acting as a barrier to global market penetration.FindingsThis research highlights the transformative potential of smart manufacturing in developing economies, identifying CSFs such as government support, cost optimization and resilient supply chain management as essential for overcoming obstacles like over-reliance on foreign technologies, regulatory rigidity and skill gaps. The integration of IVSFS with AHP and DEA models offers actionable insights to foster localized innovation, reduce foreign dependencies and promote user-centric designs, aligning with the United Nations Sustainable Development Goals.Originality/valueThis study shows that IVSFs, AHP and DEA can be used together to estimate the global challenges of manufacturing firms in developing markets. The combination of efficient decision-making and these strategies is novel as it provides ways in which businesses in developing countries can deal with their obstacles and improve their competitiveness on the global stage.

Sustainable supply chain in green hydrogen industry in India using SAP-LAP and paradox framework

Purpose This study aims to delve into how predictive maintenance (PM) strategies and supply chain optimization (SCO) can effectively integrate within the green hydrogen sector, enhancing operational efficiency and sustainability. Design/methodology/approach This study uses the situation, actor, process-learning, action, performance model of inquiry, enriched by insights from the paradox framework using expert opinions. The study is grounded in an empirical methodology, using in-depth interviews and discussions with industry experts to elucidate the complex dynamics. Findings Key findings from the expert interviews reveal a rich tapestry of challenges and opportunities. This study emphasizes the criticality of PM in reducing downtime and increasing the lifespan of key equipment in hydrogen production. Simultaneously, it underscores the pivotal role of SCO in ensuring the seamless flow of materials and energy, which is vital for the industry’s scalability and economic viability. Originality/value It is noted that this study contributes a detailed perspective to the existing body of literature, particularly focusing on the nuanced roles of PM and SCO in the green hydrogen value chain. This approach is distinct from prior studies that primarily concentrated on the hydrogen value chain, which have traditionally delved into the drivers, challenges and enablers of these areas.

Optimizing Supply Chain Management: A Decision-Making Approach for Performance Enhancement

Introduction: Supply chains have become indispensable elements in the process of conducting modern businesses, continuously adapting to changes in markets and demands from clients. Effective Supply Chain Management (SCM) is essential for achieving success and holding a competitive advantage, with many challenges ahead: encouraging collaboration, new technologies, and best practices. Objectives: This work develops the application of artificial neural networks (ANNs) within SCM based on the Supply Chain Operations Reference (SCOR) model. The SCOR model has been extensively applied because of its great capacity to detect issues within the supply chain; however, it does not indicate the best way to address these issues. Methods: A diagnostic approach based on the SCOR model is proposed in a hybrid manner where ANNs are exploited to provide recommendations regarding the issues detected by the SCOR model. The integration of ANNs allows for data-driven insights to enhance the decision-making process and optimize supply chain operations. Results: The new strategy aims to improve decision-making processes, modernize operations, reduce redundant tasks, and be flexible enough to adapt to future market changes by determining activities that could create greater impact within its supply chain. Conclusions: Furthermore, the proposed methodology should be adaptable to various industries and is expected to optimize the efficiency of SCM, providing a scalable solution to enhance supply chain performance across different business sectors.

Leveraging Analytics to Drive Human Performance

Recent audit reports by the Auditor General noted the potential impact to Canadian Armed Forces readiness due to sub optimal Defence Supply Chain performance. To identify the root cause of performance deficiencies and assess the adequacy of existing training, a systematic approach was employed to identify the knowledge and skills required by each Supply Chain role to perform their share of tasks across 39 Processes. Based on the Department of National Defence Supply Administration Manual (SAM), the project team mapped Processes and Tasks to all applicable Supply Chain Phases. Processes and Tasks were also mapped to each role and the training priority was determined based on Difficulty, Importance and Frequency (DIF) analysis. We then mapped topics/teaching points from relevant course to existing processes and Tasks; and generated a list of processes and Tasks with “adequate”, “limited” or “no” curriculum to support the acquisition of requisite knowledge and skills for each role. The analysis revealed: • All roles contribute heavily to the overall success of the Supply Chain in an integrated work environment – necessitating an understanding of the impact of their work on others. • Developing curriculum incrementally over the years based on specific, sometimes narrow needs/performance and without a comprehensive map as outlined above yielded inefficient learning solutions. • Developing role-based solutions in parallel with process-based curriculum resulted in gaps and duplication of effort. This paper reaffirms the need for “getting back to basics”. A thorough analysis and mapping of actual work/role requirements based on an authoritative reference, using a systematic process enabled by a leading-edge Training Management System, will provide a robust analysis framework. Training gaps and overlaps will become evident, and a blueprint for a comprehensive re-organization of the curriculum will naturally emerge.

A Process Systems Engineering Approach to Decision‐Making in the Chemical Industry

AbstractA variety of optimization tools are used in the chemical industry, but they are often embedded in inflexible, isolated, manual, and complex decision‐making processes. This work proposes a decision‐making framework that integrates models, data, and decision‐makers into data‐connected decision cells, organized within a decision graph. The framework formalizes decision processes and enables systematic and adaptive workflows across decision layers. The framework can be applied to various use cases, including supply chain optimization and process design, to address the evolving challenges of the chemical industry. It enables a shift from component‐level optimizations toward system‐wide optimization strategies that account for the interconnected nature of complex production systems.

Impact of technological innovation on the performance of Chinese listed seed companies: The moderating role of supply chain concentration

This study examines the impact of technological innovation on the performance of Chinese seed firms, as well as the moderating effect of supply chain concentration, aiming to provide actionable strategies for seed firms to enhance their sustainable development. Analyzing panel data from Chinese listed seed firms (2013–2022) through lagged regression models, results show that both R&D intensity and patents have a significant negative impact on ROE in the current period, likely due to short-term cost pressures. However, R&D intensity exhibits a significant positive relationship with ROE from a lag of two periods, indicating long-term benefits. Patents show a positive but insignificant relationship from a lag of three periods. Supply chain concentration weakens the negative relationship between R&D intensity and ROE, suggesting that optimizing supply chain concentration can moderate short-term costs and enhance long-term performance. Given the unique industry characteristics of seed companies, strategic supply chain management is crucial for leveraging innovation investments. This study highlights the importance of balancing short-term costs with long-term benefits in driving sustainable competitiveness.

The Metaverse Revolution: Transforming the Future of Retail

This comprehensive article explores the transformative impact of the Metaverse on the retail industry, examining how this immersive digital realm is revolutionizing traditional commerce paradigms. It investigates the convergence of cutting-edge technologies with innovative retail strategies, highlighting the evolution from static retail spaces to dynamic, AI-driven environments. It delves into real-time customer intelligence systems, adaptive retail environments, and sophisticated technical infrastructure components, including edge computing, blockchain, and artificial intelligence. The article examines practical applications such as virtual showrooms, AI-powered shopping assistants, hyper-personalized marketing, and supply chain optimization. Through extensive analysis of current industry research and market trends, this article demonstrates how the Metaverse is fundamentally reshaping customer interactions, operational efficiency, and business models in the retail sector. It reveals the critical role of emerging technologies in creating seamless, personalized shopping experiences that bridge physical and digital retail environments, while also addressing the challenges and opportunities presented by this digital transformation.

The Role of Artificial Intelligence in Transforming Physical and Online Fashion Retail: Enhancing Experiences, Driving Sustainability, and Fostering Innovation

This study examinesexplores the transformative role of artificial intelligence (AI) in revolutionizing the fashion industry, focusingwith a focus on enhancing consumer experiences, fosteringpromoting sustainability, and driving innovation in retail. AI’sIt examines AI applications in personalized recommendations, virtual try-ons, and supply chain optimization are explored alongside its, while also addressing societal implications. The integration of sustainabilitySustainability is a key focuscentral theme, highlighting how AI reducesminimizes overproduction, facilitatesenables circular fashion, and fostersencourages conscious consumerism. Case studies, such as Nike’s AI-powered retail stores and the Lynn University’s Surreal Fashion Show illustrate, demonstrate practical applications and innovations during the COVID-19 pandemic. This research synthesizes insights from  reports by The Business of Fashion  and McKinsey and& Company reports, emphasizing the trends and challenges shapinginfluencing AI’s integration into the future of fashion retail.

Optimizing Supply Chain Financial Strategies Based on Data Elements in the China’s Retail Industry: Towards Sustainable Development

China’s retail industry faces unique challenges in supply chain financing, particularly for small and medium-sized enterprises (SMEs) that often struggle to secure loans due to insufficient credit ratings and collateral in the business environment of China. This paper presents a groundbreaking approach that integrates real-time data elements into financing models, addressing the critical issue of information asymmetry between financial institutions and retail SMEs. By leveraging dynamic data such as orders, receivables, and project progress, our novel framework moves beyond the limitations of traditional asset-based lending, employing advanced data analytics for enhanced credit assessment and risk management. Applying the Stackelberg game theory, we explore the strategic interactions between suppliers and purchasers in the retail supply chain, identifying optimal financing strategies that improve capital flow efficiency and reduce overall costs. Our comprehensive data-driven model incorporates various scenarios, including the traditional supply chain financing model (Model T) and the innovative data-element secured financing model (Model G). The latter further considers risk assessment, risk appetite, volume, and schedule factors, providing a holistic approach to financial decision-making. Through rigorous mathematical modeling and numerical analysis, we demonstrate the effectiveness of our proposed framework in optimizing supply chain financing strategies. The results highlight the potential for data-driven approaches to unlock new financing opportunities for SMEs, fostering a more collaborative and efficient ecosystem within the retail industry. This study presents comprehensive data-driven strategies that unlock new financing opportunities for SMEs, providing a practical roadmap for stakeholders to foster a more collaborative and efficient supply chain financing ecosystem. The significance of studying supply chain finance for small and medium-sized enterprises (SMEs) lies in optimizing financing models to address the financing difficulties faced by SMEs. This helps improve their market competitiveness and promotes resource sharing and collaboration among all parties in the supply chain, thereby achieving sustainable economic development.

AI-Driven Next-Gen U.S. Retail: An Empirical Study on Optimizing Supply Chains by leveraging Artificial Intelligence, Business Intelligence, and Machine Learning.

Business Intelligence (BI), Artificial Intelligence (AI), and Machine Learnings (ML) have been playing an important role for optimizing Supply Chain Management (SCM) in the U.S. retail industry. The integration of these innovative and cutting-edge technologies into SCM has transformed the efficiency, agility, and profitability of retail businesses across the nation. It’s important to know how these advanced technologies transformed the supply chain process for optimizing inventory level to avoid any bottleneck, overstocking or stockout situation. This research examines how the integration of these modern technologies transformed the supply chain process and enabled retailers in optimizing their supply chain management. In this research work, we have used extensive knowledgebase on Business Intelligence, Artificial Intelligence, Machine Learning, the U.S. retail industry, and the Supply Chain Management, and later we applied this knowledgebase in the U.S retail domain to see how retailers integrate these technologies into their supply chain management process. We also used secondary information available online from reliable sources to make it more realistic. The U.S retail sales revenue was reported at US$7.6 trillion in Y2024 with an expected growth of CAGR of 3.2% over the last five years (Y2019-Y2024). We see a steady growth in the retail sector after the COVID-19 pandemic. Therefore, there is a growing demand for integrating these technologies into the retailers’ SCM so that they can predict consumer demand more accurately and maximize their sales revenue. These technologies serve retailers with greater benefits like forecasting product demand, optimizing inventory level, data-driven decision making, cost reductions by avoiding overstocking, increasing efficiency etc. Though these modern technologies enable retailers with supply chain optimization, there are still some downsides, which include high initial payouts, data silos, resistance to adoption of new technology, consistent and quality dataflow, data integration from various sources etc.

Optimization of three-echelon logistics supply chain considering emergency scenarios under resilience Strategy: A case study in power metering industry

Optimization of three-echelon logistics supply chain considering emergency scenarios under resilience Strategy: A case study in power metering industry