Supply Chain Network Research Articles

Quantifying the reciprocal impacts of capital and logistics networks in the supply chains: A cyber–physical system approach

Capital flow and logistics are closely linked in the supply chain. Current research primarily assesses their independent resilience during disruptions, but this study reveals an iterative bidirectional impact. The interconnection of shareholding, mutual guarantees, and borrowing within the supply chain has created financial relationships that were previously nonexistent. However, the evolution of logistics has not aligned with these financial developments, leading to a growing heterogeneity between capital flow and logistics. While the heterogeneity between capital flow and logistics within supply chains is crucial, previous studies, especially those rooted in complex networks, often overlook its impact. Drawing inspiration from the cyber–physical system, this paper abstracts logistics and capital flow into respective networks. A model of mutual dependence quantifies reciprocal impacts between capital and logistics networks, exploring the consequences of heterogeneity on the supply chain. Simulation results are compared with traditional single-layer network models. Finally, an innovative edge removal method is proposed to enhance supply chain robustness. Our simulation results indicate that the influence of the capital network on the logistics network and their relationship to heterogeneity is nonlinear. The edge removal method enhances supply chain network robustness in a nonlinear manner, with optimal effectiveness achieved at p_l=0.2.

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.

Enhancing supply chain security and transparency with ai and blockchain integration for future-proof solutions

The objective of this review paper is to examine the integration of Artificial Intelligence (AI) and blockchain technology to enhance supply chain security and transparency, providing future-proof solutions for global industries. As the digital landscape evolves, traditional supply chain management methods face increasing vulnerabilities and inefficiencies. This paper reviews existing literature to assess the current challenges and potential benefits of integrating AI and blockchain in supply chains. The review highlights how AI enhances predictive analytics, demand forecasting, and operational efficiency, while blockchain ensures immutable, decentralized ledgers for real-time tracking and verification of goods. The synergy between these technologies offers unprecedented levels of transparency, enabling stakeholders to detect and respond to anomalies swiftly. Case studies across various industries demonstrate significant improvements in reducing fraud, counterfeiting, and human errors, alongside enhanced compliance with regulatory standards. Conclusions drawn from this review underscore the transformative potential of AI and blockchain in creating resilient, transparent, and secure supply chains. The integration of these technologies not only addresses current vulnerabilities but also lays the foundation for adaptive, intelligent supply chain networks capable of withstanding future disruptions. This paper provides actionable insights and practical frameworks for businesses and policymakers aiming to implement AI and blockchain solutions, emphasizing the importance of strategic planning, cross-industry collaboration, and continuous innovation in achieving long-term supply chain sustainability and security.

Overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability factor

At present, the supply chain is multi-layered and complicated, and its failure detection is based solely on the characteristic threshold, which does not lack the ability to describe the complex supply chain. An overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability is proposed. Based on the multi-layer supply chain network including raw material suppliers, manufacturers, distributors and retailers, the overload cascading failure model of the multi-layer supply chain network is constructed through networking, and the overload cascading failure process of the multi-layer supply chain network is described through three aspects: initial load, node capacity and overload node load distribution. By monitoring and analyzing the status update messages of each node in the supply chain network, and considering the delay probability factor, the node and path that may lead to cascading failure are identified by predicting the delay probability of the next message through the historical message delay probability, and the overload cascading failure detection of the multi-layer supply chain network is realized. The experimental results show that the algorithm can effectively detect the failure of each overloaded enterprise node in the multi-layer supply chain network, which is helpful to enhance the early warning and response ability of the cascade failure of the supply chain network. The algorithm can realize the failure detection of supply chain network under different attack conditions and initial failure ratio of nodes, and measure the anti-risk ability of supply chain network. Under deliberate attack and when the initial attack node is a supplier network layer node, it has a greater impact on the multi-layer supply chain network.

Exploring the Drivers, Challenges, and Opportunities of Supply Chain Technology Adoption for Small and Medium Enterprises (SMEs) in Malaysia

Small and Medium Enterprises (SMEs) have a crucial impact on the Malaysian economy, making major contributions to the functioning of the supply chain. Integrating supply chain technology has become a crucial element in enhancing the efficiency and competitiveness of SMEs in Malaysia. During recent years many developing and developed countries have witnessed the expansion of markets and supply chain networks; in the progress of these market and supply systems SMEs find various complexities when they are implementing or improving the technology integration of their supply chain. The current body of literature often addresses the adoption of technology in a general manner or within larger organizations, resulting in a significant gap in study about the specific circumstances and requirements of small and medium-sized enterprises (SMEs). This study employs a rigorous methodology that analyzes relevant papers to examine the drivers, challenges, and opportunities for adopting supply chain technology among SMEs in Malaysia. By implementing supply chain technology, Malaysian SMEs can increase their performance in many areas of key performance indicators. This includes improving efficiency, optimizing inventory management, achieving cost savings, enhancing customer service, and increasing market competitiveness. It is believed that the findings of the study can be useful for managers of SMEs, policymakers, and interested stakeholders. This will enable SMEs to navigate the challenges of adopting technology more effectively and enhance global competitiveness.

Research on Supply Chain Network Optimization of AH Company

Research on Supply Chain Network Optimization of AH Company

Correction: Trade credit and loan in capital‑constrained supply chain network design model

Correction: Trade credit and loan in capital‑constrained supply chain network design model

The Impact of Supply Chain Network Structure on Green Innovation: The Mediating Role of Knowledge Absorption

ABSTRACT Green innovation is key to sustainable development. While the supply chain network affects corporate green innovation, the impact of the network’s structure remains unclear. On the basis of data concerning 737 Shanghai and Shenzhen A-share listed companies in China from 2007 to 2023, this study empirically investigates the impact of supply chain network structure (centrality, structural holes) on green innovation (exploratory, exploitative). The results reveal that centrality inhibits green innovation, whereas structural holes promote such innovation. These findings continue to be supported after robustness and endogeneity tests are performed. An analysis of the mediating mechanism reveals that centrality hinders green innovation by inhibiting knowledge absorption, whereas structural holes promote such innovation by facilitating knowledge absorption. Heterogeneity analysis reveals that high-profit enterprises, nonheavily polluting enterprises and enterprises that face low levels of financing constraints utilize supply chain networks to promote green innovation more effectively. The findings of this research provide both theoretical and empirical guidance for enterprises regarding the ability of supply chain network structure to promote green innovation.

The resilience and determinants of global mineral resource supply chains: a network percolation perspective

Mineral resources are the basic materials for global economic development. Assessing mineral resource supply chain resilience is an important pillar of mineral resource supply chain stability management. The globality, heterogeneity and complexity of supply chain bring challenges to the resilience assessment of global mineral resource supply chain. To solve this problem, a method based on multi-region input-output model, network percolation model and econometric model is proposed, which is able to measure the resilience of global mineral resource supply chain and its influencing factors from the perspective of the whole system. The percolation phase transition is introduced to measure the critical state of global mineral resource supply chain system collapse facing external disruption. Using the proposed method, this paper conducts an empirical study on the evolution of global mineral resource supply chain resilience from 2005 to 2014. The results show that the resilience of global mineral resource supply chain declined by 39.6% in 2005–2014. Most of the critical links that caused the collapse of the global mineral resource supply chain network are the manufacturing sector and its upstream and downstream sectors. The structure of supply chain network plays a key role in network resilience. Increasing the number of linkages in upstream and downstream could improve network resilience, but the increase of linkage strength would deteriorate network resilience.

Peer Effects in Corporate Digital Transformation within Supply Chain Networks

Peer Effects in Corporate Digital Transformation within Supply Chain Networks

Optimizing Supply Chain Design under Demand Uncertainty with Quantity Discount Policy

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits.

Impact of various fairness-based distribution models on supply chain networks from the perspective of customer satisfaction

AbstractThis study investigates the effectiveness of two fairness-based distribution approaches—type I and type II—in enhancing the resilience of supply chain networks (SCNs) during disruptions. The research contributes to the growing body of knowledge on supply chain management by offering insights into how fairness principles can be applied to improve the restoration of disrupted networks. A mixed-integer programming model was developed to simulate these fairness-based distribution strategies, focusing on a water supply chain network of a privet company in Saudi Arabia. The SCN consists of a single supplier and ten demand nodes, each requiring multiple commodities. The model was tested under 100 random disruption scenarios, each reducing the capacity of randomly selected network segments. The performance of each fairness-based distribution approach was evaluated based on how quickly and effectively the SCN returned to its required service levels (SLs) across all demand nodes. Results indicate that Fairness Distribution Type I, which aims to minimize unmet demand across the entire network, generally outperformed Type II in terms of speed and efficiency. Type I was more effective at restoring SLs quickly at most nodes, while Type II showed localized advantages, particularly in restoring SLs for specific commodities at select nodes. The study concludes that while Type I is more suited for overall supply chain recovery, Type II may be beneficial in scenarios requiring focused recovery at specific demand nodes. These findings provide actionable insights for supply chain managers seeking to enhance network resilience through fairness-based distribution strategies, and suggest avenues for future research on hybrid and context-specific approaches.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

Production perspectives of a high-status polychrome jewellery set from the Hunnic period (mid-5th century AD) Carpathian Basin

Fifty years after the discovery of the Regöly grave, the emblematic Hunnic period archaeological assemblage from Hungary, an extensive scientific investigation was performed on the polychrome dress accessories of the high-status woman, often referred to as the “Princess of Regöly”, buried there. The multi-instrumental analyses aimed to characterise material and technological aspects of the gemstone-inlaid artefacts (a set of a pair of bow-brooches and a belt buckle), i.e., the manufacturing and decorative techniques as well as the chemical and mineralogical compositions. The non-destructive and non-invasive gemmological and geochemical analyses applied optical microscopes, handheld XRF, SEM-EDX and Raman microspectrometer. This study provides the first comprehensive examination of the Hunnic period polychrome jewellery, and highlights the potential of combining surface and subsurface analyses to specify garnet provenances. It presents the first evidence of use of antique and early medieval garnet sources during the early Migration period. The findings enhance understanding of the garnet supply chain and trade network, the production organisation and potential workshop connections. Significant differences of the brooches and the buckle reveal varying levels of luxury among the elite, providing insight into the social context of the polychrome jewellery associated with the “Princess of Regöly”.

Optimization Algorithm of Network Node Layout of Fresh Agricultural Products’ Online Consumption Behavior in E-Commerce Environment

In order to improve the efficiency of online consumption and supply chain efficiency of fresh agricultural products in the e-commerce environment, a network node layout optimization algorithm was designed. In the e-commerce environment, a network node deployment model has been constructed for the online consumption behavior of fresh agricultural products. A network transmission supply chain detection model was established through the rotation scheduling method. By combining distributed transformation protocols, the spatial layout characteristics of network nodes have been reorganized. Further introduction of a multi-coverage scheduling model, combined with sensor information fusion tracking technology, accurately detects the layout characteristics of network nodes. Based on these detection data, forwarding and adaptive control protocols were developed. Real-time control using the node traversal method was developed. Under the optimal node supply chain transmission mechanism, the scheduling of the network node layout has been achieved. Meanwhile, convolutional neural networks were utilized to dynamically adjust layout control to ensure convergence and stability. The simulation results show that the node forwarding ability of the network node layout design for online consumption behavior of fresh agricultural products in the e-commerce environment is good, which improves the output balance of the network supply chain transmission of online consumption behavior of fresh agricultural products in the e-commerce environment. In addition, the optimized delivery cost decreased by 20% compared to before optimization, delivery efficiency increased by 30%, and inventory turnover rate increased by 25%, confirming that the proposed method has good optimization performance.

Supply chain network equilibrium with outsourcing for fresh agricultural products under stochastic demands

Abstract Accepted by: M. Zied Babai This paper examines a supply chain network focusing on the distribution of fresh agricultural products, incorporating outsourcing and stochastic demands considerations. Initially, we establish a generalized Nash equilibrium model with stochastic demands among fresh produce firms. Subsequently, we transform this model into a mixed complementarity system using the Karush–Kuhn–Tucker conditions. Utilizing the Fischer–Burmeister function, we further convert the mixed complementarity system into a set of nonlinear equations, amenable to solution via GAMS software. We conduct numerical experiments and perform sensitivity analysis on key parameters. Our findings suggest that higher subsidy rates incentivize firms towards production outsourcing, particularly benefiting low-income farmers, thereby potentially increasing profits. Moreover, market fluctuations play a pivotal role in ensuring the stability and profitability of firms, with moderate fluctuations presenting opportunities for fresh enterprises to adapt, innovate and capitalize on evolving market conditions. These results offer valuable insights for effective management of fresh produce supply chains.

Advanced Supply Chain Management Using Adaptive Serial Cascaded Autoencoder with LSTM and Multi-Layered Perceptron Framework

Supply chain management is essential for businesses to handle uncertainties, maintain efficiency, and stay competitive. Financial risks can arise from various internal and external sources, impacting different supply chain stages. Companies that effectively manage these risks gain a deeper understanding of their procurement activities and implement strategies to mitigate financial threats. This paper explores financial risk assessment in supply chain management using advanced deep learning techniques on big data. The Adaptive Serial Cascaded Autoencoder (ASCA), combined with Long Short-Term Memory (LSTM) and Multi-Layered Perceptron (MLP), is used to evaluate financial risks. A data transformation process is used to clean and prepare financial data for analysis. Additionally, Sandpiper Galactic Swarm Optimization (SGSO) is employed to optimize the deep learning model’s performance. The SGSO-ASCALSMLP-based financial risk prediction model demonstrated superior accuracy compared to traditional methods. It outperformed GRU (gated recurrent unit)-ASCALSMLP by 3.03%, MLP-ASCALSMLP by 7.22%, AE-LSTM-ASCALSMLP by 10.7%, and AE-LSTM-MLP-ASCALSMLP by 10.9% based on F1-score performance. The SGSO-ASCALSMLP model is highly efficient in predicting financial risks, outperforming conventional prediction techniques and heuristic algorithms, making it a promising approach for enhancing financial risk management in supply chain networks.

Digital welfare of the value cycle of agribusiness supply chain network: evidence from China's listed agriculture-affiliated companies

ABSTRACT Using the data of listed agriculture-affiliated companies with customer-supplier association information, we examine the diffusion effect of the digitalisation of listed agricultural companies on the digital transformation of agriculture-affiliated enterprises from the perspective of supply chain. We show that the supply chain diffusion effect of digitalisation mainly exists in ‘customer enterprises-suppliers enterprises’ and ‘leading enterprises-other enterprises’. And the digital transformation of agriculture-affiliated customer enterprises forces suppliers to respond to customer innovation demands, thereby improving the digitalisation level of suppliers, while leading agriculture-affiliated enterprises improve the digitisation level of other agriculture-affiliated enterprises in the supply chain network through knowledge flow. Additionally, the supply chain diffusion effect of digital transformation is stronger under the conditions of smaller ‘digital divide’, lower geographic distance, the downstream consumer end and more government support of agriculture-affiliated enterprises. We provide theoretical and practical implications for promoting the digitalisation of the entire agricultural chain and improving the scale effect of digitalisation.

Optimization modelling for a sustainable closed-loop supply chain network using IoT: multiobjective metaheuristic algorithms

Optimization modelling for a sustainable closed-loop supply chain network using IoT: multiobjective metaheuristic algorithms

Hydrogen for net-zero emissions in ASEAN by 2050

Hydrogen is believed to be a promising decarbonization vector. An optimal hydrogen supply-chain network achieving net-zero emissions in ASEAN by 2050 is determined. A logistic profile for decarbonization and declining costs with technological advances are assumed. It is found that the region has the potential to fully decarbonize its power sector with renewable energy if a regional power grid is installed. A mix of green (from surplus renewable power), blue (from natural gas) or orange (from biomass) hydrogen, the latter two with carbon capture and sequestration, meets industry, transport and power (without a regional power grid) sector demands. Results show that available biomass is insufficient to meet decarbonization targets beyond 2045 without green hydrogen. Prioritizing energy security and sustainability has minimal incremental costs compared to importing liquefied natural gas for blue hydrogen production. Optimal production locations are Myanmar, Cambodia for green hydrogen, and Indonesia, Vietnam for orange and blue hydrogen.