Supply Chain Decisions Research Articles

Concentrate or Diversify? Economic Policy Uncertainty and Supplier‐Base Concentration

AbstractRisks of abrupt changes in economic policies not only affect many segments of firms' operations, but also compromise the stability of the supply chain. Based on the data from listed companies in China between 2001 and 2020, this paper examines how firms stabilize their supply chain structure amidst growing economic policy uncertainty. Our findings reveal that, in response to increasing policy uncertainties, companies tend to stabilize supply chains by diversifying their supplier base. This impact is more prominent when firms face greater supply risks and a higher degree of market uncertainty. In addition, we discuss the timeliness and structural changes of firms' supplier‐base in detail. Our results remain consistent across various endogeneity tests, providing empirical evidence for how economic policy uncertainty shapes firms' supply chain decisions. Policymakers can enhance understanding of firms' supply chain adjustment strategies in the face of rising policy uncertainty.

Enhancing Supply Chain Decision-Making through Machine Learning and Mathematical Modelling Approaches

Enhancing Supply Chain Decision-Making through Machine Learning and Mathematical Modelling Approaches

GREEN LOGISTICS AND SUSTAINABLE TRANSPORTATION: AI-BASED ROUTE OPTIMIZATION, CARBON FOOTPRINT REDUCTION, AND THE FUTURE OF ECO-FRIENDLY SUPPLY CHAINS

The rapid expansion of global supply chains has led to increased carbon emissions and environmental concerns, necessitating the adoption of sustainable logistics solutions. This study explores the role of artificial intelligence (AI) in optimizing transportation routes, minimizing fuel consumption, and reducing the carbon footprint of supply chains. AI-powered route optimization integrates real-time traffic data, weather conditions, and vehicle efficiency to enhance last-mile delivery and freight management. Machine learning algorithms further contribute to predictive maintenance, fleet electrification strategies, and demand forecasting, ensuring operational sustainability. This research also examines green logistics practices, including the use of electric and hydrogen-powered vehicles, multimodal transportation networks, and circular economy models to minimize environmental impact. Blockchain-enabled carbon tracking and AI-driven sustainability metrics offer improved transparency in carbon footprint reporting. Additionally, the study highlights regulatory frameworks and industry initiatives promoting low-emission transportation and smart logistics hubs. Findings suggest that AI-driven logistics solutions can significantly improve efficiency while meeting sustainability goals. However, challenges such as high implementation costs, data privacy concerns, and infrastructure limitations must be addressed. Future research should focus on integrating AI with IoT and blockchain for enhanced traceability and decision-making in sustainable supply chains. The study concludes that AI-powered green logistics can revolutionize transportation, offering a viable path toward carbon-neutral and cost-effective global supply chains.

Pricing Analysis of Risk-Averse Supply Chains with Supply Disruption Considering Reference Price Effect

This paper examines the impact of the reference price effect on pricing decisions in a risk-averse supply chain with a dual-sourcing procurement strategy, particularly during single-sourcing supply disruption. To analyze supply chain pricing decisions under non-disrupted and disrupted scenarios, we innovatively use semivariance as a risk measure to effectively avoid the limitations of the traditional variance approach and integrate it into Stackelberg game models. Based on these models, we analyze the impact of the reference price effect, risk aversion, and single-sourcing supply disruption on supply chain members’ pricing decisions. The main findings include the following: the single-sourcing supply disruption degree may increase the price of non-disrupted products and then increase the non-disrupted supplier’s utility; the strength of the reference price effect positively influences retailer utility but negatively impacts product pricing for supply chain members; the pricing decisions and utility of supply chain members are influenced by their risk aversion, and supply chain members with higher risk aversion adopt more conservative pricing strategies and consequently obtain lower utility; and equilibrium decisions generally demonstrate a degree of robustness. These insights may help supply chain managers respond rationally to supply disruptions and properly develop pricing strategies by taking into account the reference price effect.

Decision-Making Research on Supply Chain Quality Inspection Based on Binomial Distribution and Monte Carlo Simulation

In modern industrial production, enterprises face the dual challenge of ensuring supply chain quality reliability while controlling costs. Traditional single-link quality detection methods are inadequate for multi-link collaborative decision-making in supply chains. This study proposes a quality inspection method combining binomial distribution and Monte Carlo simulation for enterprise production processes. Integrating hypothesis testing based on normal distribution determines whether spare part defective rates exceed supplier-declared standards, guiding batch acceptance decisions. The model introduces a random allowable error E to adjust significance levels dynamically and errors, enhancing performance analysis. The study also considers inspection, dismantling costs, and substandard product exchange losses. Using Monte Carlo simulation, a revenue model is constructed for supply chain decision-making, providing accurate success rate solutions for parts, semi-finished, and finished products, aiming to maximize enterprise profit. Results show that precise quality control and cost management significantly improve economic efficiency and market competitiveness. For instance, selling a single product yields maximum profits increased from 9.60 yuan to 19.36 yuan (102% increase), with the second-best option reaching 15.19 yuan, a gap of 4.17 yuan compared to the optimal solution. This study offers robust theoretical guidance for quality control in enterprise production processes.

Blockchain’s Role in Low-Carbon Supply Chain Decisions with Game Model Insights

Blockchain’s Role in Low-Carbon Supply Chain Decisions with Game Model Insights

Green supply chain decision-making considering fair concerns and consumer environmental mindset

Green supply chain decision-making considering fair concerns and consumer environmental mindset

Continuous multimodal data supply chain and expandable clinical decision support for oncology

The study introduces a clinical decision support system (CDSS) developed at a single academic cancer center, integrating real-time clinical, genomic, and imaging data for over 170,000 patients across 11 cancer types. We have developed the Yonsei Cancer Data Library (YCDL) data integration framework to continuously collect and update multimodal datasets comprising over 800 features per case. Quality control measures, using 143 logical comparisons, addressed missing data and outliers, achieving median accuracies of 92.6% for surgical and 98.7% for molecular pathology. An Extract-Transform-Load (ETL) process with natural language processing transformed unstructured data, enabling survival analyses stratified by tumor stage, which revealed significant stage-dependent differences. The CDSS dashboard visualizes patient trajectories and key milestones. User feedback from oncology professionals showed strong acceptance, with satisfaction scores exceeding 4 out of 5. This framework demonstrates the potential of multimodal data integration to enhance clinical decision-making and patient outcomes, with future research needed to validate its generalizability and scalability.

Research on Eco-Product Supply Chain Decision-Making and Coordination Under Different Subsidy Strategies with Ecological Cost-Sharing Contracts

In order to solve the problem of poor market circulation in the process of realizing the value of ecological products, this paper studies a supply chain system composed of a single ecological product supplier, an ecological product distributor, and a consumer. Since government subsidies will greatly affect the operation of the supply chain system, the supply chain decision-making model under three scenarios, of no government subsidies, development and operation subsidies, and consumer subsidies, is constructed with an ecological cost-sharing contract coefficient between suppliers and dealers. Then, the Stackelberg game is used to solve the optimal strategy and maximum profit of all parties in the supply chain under different financial subsidy scenarios in order to form eco-product development suggestions in line with market rules.

Research on the Measurement and Improvement in Fresh Product Supply Chain Resilience Based on Blockchain Technology

Fresh product faces intrinsic vulnerabilities to supply disruptions, given its perishable nature and seasonal dependencies. This underscores the critical importance of supply chain resilience in sustaining a steady and dependable flow of these goods and is a key factor in promoting the sustainable development of the supply chain. By employing well-established resilience metrics, specifically time to recovery, recovery level, and lost performance during recovery (with profit adopted as the performance indicator for this study), this study explores the resilience of the fresh product supply chain and examines the impact of blockchain technology on resilience enhancement. The findings indicate that when resilience is prioritized, optimal recovery efforts and supply recovery levels exceed those targeted solely for profit maximization. A negative correlation emerges between supply chain resilience and recovery delay, while an optimal supply recovery level is positively associated with recovery delay. Numerical evaluations further demonstrate that blockchain technology has a marked effect on elevating supply recovery levels, thus significantly strengthening resilience. Moreover, recovery levels display a positive correlation with the degree of blockchain adoption, while profit loss initially decreases before ascending alongside increased blockchain investments. Resilience also inversely correlates with fresh-keeping efforts, with blockchain applications accelerating this downward trend. Collectively, this study not only provides important references for recovery decision-making in the fresh agricultural product supply chain but also emphasizes the significant potential of blockchain technology (BT) in enhancing supply chain resilience and promoting sustainable development.

Impacts of distributional and peer-induced fairness concerns on a closed-loop supply chain under dual-channel competition

This work explores the impacts of the distributional and peer-induced fairness concerns with both advantageous and disadvantageous inequities on a two-echelon closed-loop supply chain comprising a manufacturer and two downstream competitors who engage in forward and reverse channel competitions. Four complete information Stackelberg game models are formulated for different fairness concern considerations. The equilibrium results of these models are obtained through backward induction, and the impacts of the important parameters are examined through theoretical analyses. Numerical analyses are conducted to verify the theoretical results and sensitivity analyses are performed to verify the impacts of the key parameters. Some important findings are obtained. First, the manufacturer’s and the dominating competitor’s profits increase when considering peer-induced fairness concerns, and the downstream competitors sacrifice their profits to reduce the manufacturer’s profit when considering distributional fairness concerns. Second, the simultaneous consideration of distributional and peer-induced fairness concerns mitigates the downstream competitors’ profit losses under certain conditions. Finally, the peer-induced fairness concern consideration aggravates the reverse channel competition, resulting in the downstream competitors’ profit losses. This study provides insights into the complex influences of fairness concerns on the decisions in closed-loop supply chains.

Decision and Coordination of Sustainable Supply Chain Considering CSR Implementation and Channel Leadership

To achieve the ultimate objective of multi-dimensional sustainable development of the supply chain system, this paper examines the decision and coordination of the sustainable supply chain (SSC) from the dimensions of environment, society, and governance. Based on a dual-channel collecting structure, this study constructs six supply chain decision models considering two corporate social responsibility (CSR) implementers and three channel leadership structures. The findings reveal that in the SSC with a channel leader, it is most beneficial for the game follower to implement CSR from the perspective of maximising the total benefit to the supply chain and social welfare. The selection of the optimal channel leadership hinges on the trade-off between the collecting cooperation fees and the degree of the member's CSR implementation. Finally, this research proposes a revenue-responsibility sharing contract to achieve the perfect coordination of the SSC. Our results can function as guidelines for establishing an environmental-friendly SSC system.

AI and IoT in Supply Chains: Creating Intelligent and Autonomous Business Operations

This research explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in supply chain management, emphasizing their role in enabling intelligent and autonomous business operations. It examines how these technologies enhance efficiency, decision-making, and adaptability in modern supply chains. A systematic review of existing literature, case studies, and industry reports is conducted to analyze the impact of AI and IoT in supply chain processes. The research highlights that AI-driven predictive analytics and IoT-enabled real-time monitoring significantly enhance supply chain visibility, efficiency, and responsiveness. Automation through AI-powered decision-making and IoT-based smart tracking systems reduces operational risks, minimizes costs, and optimizes resource allocation. However, challenges related to cybersecurity, data privacy, and integration complexities remain key concerns. This study contributes to both academic and industry discussions by offering insights into the evolving role of AI and IoT in supply chains. Practically, it provides guidance for businesses seeking to implement intelligent supply chain solutions. Socially, the findings emphasize the need for ethical AI deployment, data security, and workforce upskilling to navigate the shift toward autonomous operations. This research presents a unique synthesis of AI and IoT advancements in supply chain management, highlighting their synergistic impact on creating more adaptive, resilient, and intelligent operations. By addressing both opportunities and challenges, it serves as a valuable resource for researchers, practitioners, and policymakers in the field.

Optimizing Decision-Making in Supply Chain Management Using Machine Learning and Mathematical Modeling Techniques

Supply Chain Management (SCM) is advancing through the incorporation of machine learning and mathematical modeling methods, improving decision-making effectiveness. The aim of this study is to improve the supply chain functions through four algorithms: Long Short Term Memory (LSTM), Support Vector Machine (SVM) Genetic Algorithm (GA), and Reinforcement Learning (RL). A result of the forecasting experimental data shows that LSTM achieved 94.3% accuracy forecast which exceeds SVM’s 87.8%. GA provides 23% improvement in savings with respect to conventional optimization techniques and RL brings a 15% increase in delivery efficiency through real-time decision making. Comparison of these techniques with existing methods reveals the greater efficiency of hybrid models that combine information from AI with more traditional means. In addition, the research evaluates how blockchain and digital twin technologies improve transparency, and security in supply chains. Despite these issues, the results show that supply chain efficiency can be markedly improved using AI powered decision making. As future studies, combined optimization frameworks and supply chain management ways that focus on sustainability should be investigated to have robust and environmentally friendly supply chains. By arguing that smart supply chain decision needs to be based on empirically validated theoretical assumptions, this research sheds important light on how scholars and industry experts can try to improve supply chain performance through smart decision making.

Contract selection decision of hybrid energy power supply chain under cap-and-trade: From the perspective of supply disruption risk

Contract selection decision of hybrid energy power supply chain under cap-and-trade: From the perspective of supply disruption risk

A Study on Enhancing Logistics Efficiency Through Supply Chain Informationization: The Case of Hefei Reying International Trade Co., Ltd.

This paper delves into how Hefei Reying International Trade Co., Ltd. enhances logistics efficiency through supply chain informationization. Firstly, it analyzes the importance of supply chain informationization in e-commerce, highlighting its key role in improving supply chain transparency, response speed, and resource allocation efficiency. Then, it details Hefei Reying’s specific practices in the application of supply chain management software, automation tools, and supply chain data analysis and decision support. Through these informationization measures, the company has successfully automated order processing, refined inventory management, and made decision support more scientific, significantly improving logistics efficiency, reducing operational costs, and enhancing customer service levels. The paper concludes with a summary of the achievements and experiences in Hefei Reying’s supply chain informationization practice, providing valuable references for other e-commerce enterprises.

Introducing Blockchain? Or not? Remanufacturing supply chain decisions that consider environmental taxes and upstream encroachment

Introducing Blockchain? Or not? Remanufacturing supply chain decisions that consider environmental taxes and upstream encroachment

Price and Quality Decisions in a Co-opetitive Supply Chain Considering Two-Dimensional Consumer Preferences

Co-opetition is a crucial strategy in modern supply chains. In this study, we investigate the price and quality decisions in a co-opetitive supply chain considering two-dimensional customer preferences. The supply chain comprises a key component supplier and an OEM, where the supplier decides whether to enter the end market. The Stackelberg game is employed to examine the optimal decisions of supply chain members. Through comparative analysis, we find that when the supplier’s quality cost coefficient is low, a higher proportion of price-sensitive consumers leads to an increase in the OEM’s final product price. In addition, the supplier consistently benefits from entering the end market and forming a co-opetitive relationship with the OEM. However, this market entry benefits the OEM only when the product substitutability is low and the supplier’s quality investment efficiency is relatively low. The results of this study provide guidance for market practices among supply chain participants.

Optimizing the oil and natural gas industry: The role of ai and data analytics in ERP integration

Global energy requirements depend on the oil and natural gas industry which deals with efficiency issues and must meet both regulatory standards and environmental protection needs. Our research shows how adding artificial intelligence and data analysis tools into ERP systems solves industry problems. Enterprise resource planning systems powered by artificial intelligence make better supply chain decisions while helping businesses predict equipment maintenance needs leading to lower costs and more efficient operations. Research findings from case studies and industry data show that businesses reducing maintenance costs by 25% and improving logistics savings by 15% through AI-ERP implementation. While legacy systems, high costs and data security risks pose challenges the positive outcomes of merging AI with ERP systems prove greater in value. The research shows how these systems help energy companies operate more sustainably and emit less carbon in our modern energy environment. The sector will progress further as new developments like generative AI plus blockchain and edge computing enter the market. Companies need to train their teams, work with experts in the field and use clean technology solutions to stay ahead in today's changing market.

A multi-faceted approach for leveraging AI and intellectual capital for enhanced supply chain decision-making

PurposeThis study aims to investigate the mediating role of different types of intellectual capital (human, structural and relational) in the relationship between artificial intelligence-driven supply chain analytics capability (AI-SCAC) and various supply chain decision-making processes, specifically rational, bounded and tacit decision-making.Design/methodology/approachThe study used a quantitative survey strategy to collect the data. A total of 320 valid questionnaires were received from manufacturing companies. The data were analysed using structural equation modeling with partial least squares (PLS-SEM) approach through SmartPLS software.FindingsThe results indicate that human and structural capital significantly mediate the relationship between AI-SCAC and rational and bounded decision-making processes. However, structural capital does not mediate the relationship between AI-SCAC and the tacit decision-making process. Moreover, relational capital does not show a significant mediating effect on all of the decision-making processes. Notably, structural capital has the strongest impact on rational and bounded decision-making, while human capital plays a critical role across all three decision-making processes, including tacit decision-making.Originality/valueThis study contributes to the literature by providing a nuanced understanding of the differentiated impact of intellectual capital components on various decision-making processes within the context of AI-SCAC. While previous studies have broadly acknowledged the role of intellectual capital in decision-making, this research provides more understanding of how specific types of intellectual capital interact with AI to influence distinct decision-making processes. Notably, the differential impact of structural capital on rational and bounded decision-making versus tacit decision-making highlights the need for organisations to adopt a more tailored approach in leveraging their intellectual capital.