Supply chain research based on complex network theory

Cui Jun-Ying; Xu Shu-Qi; Na Xu; Pan Li-Ming; Lü Lin-Yuan

doi:10.7498/aps.73.20240702

Abstract

Supply chain is a chain structure formed by the sequential processes of production and distribution, spanning from raw material suppliers to end customers. An efficient and reliable supply chain is of great significance in enhancing enterprise’s market competitiveness and promoting sustainable social and economic development. The supply chain includes the interconnected flows of materials, resources, capital, and information across various stages, including procurement, production, warehousing, distribution, customer service, information management, and financial management. By representing the various participants in the supply chain as nodes and their interactions—such as the logistics, capital flow, information flow, and other interactions—as edges, the supply chain can be described and characterized as a complex network. In recent years, using complex network theory and methods to model and analyze supply chains has attracted increasing attention from researchers. This paper systematically reviews the supply chain research based on complex network theory, providing an in-depth analysis of supply chain networks in terms of network construction, structural properties, and management characteristics. First, this paper reviews two kinds of approaches to constructing supply chain network: empirical data-based approach and network model-based approach. In the empirical data-based research, scholars use common supply chain databases or integrate multiple data sources to identify the supply chain participants and clarify their attributes, behaviors, and interactions. Alternatively, the research based on network models employs the Barabási–Albert (BA) model, incorporating factors such as node distance, fitness, and edge weights, or uses hypergraph models to construct supply chain networks. Next, this paper summarizes the research on the structural properties of supply chain networks, focusing on their topological structure, key node identification, community detection, and vulnerability analysis. Relevant studies explore the topological structure of supply chain networks, uncovering the connections between nodes, hierarchical structures, and information flow paths between nodes. By analyzing factors such as node centrality, connection strength, and flow paths, the key nodes within the supply chain network are identified. Community detection algorithms are used to investigate the relationships between different structural parts and to analyze the positional structure, cooperative relationships, and interaction modes. Furthermore, quantitative evaluation indicators and management strategies are proposed for the robustness and resilience of supply chain networks. Further research has explored the management characteristics of supply chain networks, including risk propagation and competition game. Relevant studies have employed three main methods—epidemic model, cascading failure model, and agent-based model—to construct risk propagation models, simulate the spread of disruption risks, and analyze the mechanisms, paths, and extent of risk propagation within supply chain networks. These studies provide valuable insights for developing risk prevention and mitigation strategies. In addition, the game theory has been used to investigate the cooperative competition, resource allocation, and strategy selection among enterprises within the supply chain network. This paper reviews the research contents and emerging trends in supply chain studies based on complex network methods. It demonstrates the effectiveness and applicability of complex network theory in supply chain network research, discusses key challenges, such as how to obtain accurate, comprehensive, and timely supply chain network data, proposes standardized data processing methods, and determines the attributes of supply chain network nodes and the strength of their relationships. Furthermore, research on the structure of supply chain network has not yet fully captured the unique characteristics of supply chain networks. Existing models and methods for vulnerability assessment often fail to consider the dynamic and nonlinear characteristics of supply chain networks. Research on risk propagation in supply chains has not sufficiently integrated empirical data, overlooking the diversity of risk sources and the complexity of propagation paths. The asymmetry and incompleteness of information in supply chain networks, as well as multiple sources of uncertainty, make the prediction and analysis of multi-party decision-making behavior more complex. This paper also outlines several key directions for future research. One direction involves using high-order network theory to model interactions among multiple nodes and to describe the dynamics of multi-agent interactions within supply chain networks. Furthermore, integrating long short-term memory (LSTM) methods to process long-term dependence in time-series data can enhance the analysis of network structure evolution and improve the prediction of future states. The application of reinforcement learning algorithms can also adaptively adjust network structures and strategies according to changing conditions and demands, thereby improving the adaptability and response speed of supply chain networks in emergency situations. This paper aims to provide valuable insights for supplying chain research and promoting the development and application of complex network methods in this field.

Keywords:

Authors and contacts

1.
Department of Physics, University of Fribourg, Fribourg CH-1700, Switzerland
2.
Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China
3.
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
4.
School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China

Corresponding author: Lü Lin-Yuan, linyuan.lv@ustc.edu.cn

Funds: Project supported by the Major Program of the National Natural Science Foundation of China (Grant No. T2293771), the National Science Fund for Distinguished Young Scholars of China (Grant No. 62306191), and the China Scholarship Council of China (Grant No. 202108410127).

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图 1 供应链网络的典型结构^[26]

Figure 1. Typical structure of supply chain networks^[26].

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图 2 (a) 供应商网络; (b) 产品网络, 节点颜色代表节点所属社团^[27]

Figure 2. (a) Supplier network; (b) product network, nodes are colour-coded according to communities^[27].

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图 3 全球电动汽车锂电池供应链网络, 粉色节点代表供应核心部件的公司, 绿色节点代表供应关键矿物的公司, 橙色节点代表供应汽车的公司, 蓝色节点代表电池制造商, 节点大小与企业规模成正比^[34]

Figure 3. Visualization of the Electric Vehicle Lithium-ion Battery supply chain network, pink nodes represent supplying core components of firms, green nodes represent supplying critical minerals of firms, orange nodes represent supplying auto of firms, blue nodes represent battery manufacturer, where node size is proportional to company volume^[34].

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图 4 全球电动汽车锂离子电池上下游产业链相关的供应链网络^[35]

Figure 4. Supply chain network related to the upstream and downstream industrial chain of global electric vehicle lithium-ion batteries^[35]

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图 5 基于超图模型构建的供应链网络的演化机制　(a)玫红色圆代表现存节点, 绿色实线代表初始超边; (b) 添加新超边, 在现存节点之间添加新超边, 橙色虚线代表新超边; (c) 重组超边, 删除初始超边, 添加新超边, 蓝色虚线代表重组之后的新超边; (d) 链接新节点, 新增节点根据超边优先链接机制与现存节点连接形成新超边, 天蓝色圆代表新节点, 紫色虚线代表链接新节点的超边^[44]

Figure 5. Evolution mechanisms of supply chain networks constructed based on hypergraph models: (a) Magenta circles represent existing nodes and green solid lines represent original hyperedges; (b) add new hyperedges, add new hyperedges between existing nodes, orange dashed lines represent new hyperedges; (c) rewire hyperedges, delete original hyperedges and add new hyperedges, blue dashed lines represent new hyperedges by rewired; (d) link new nodes, link newly-added nodes with previously existing nodes according to their hyperedge preferential mechanism, sky blue circles represent new nodes, purple dashed lines represent hyperedges linking the new nodes^[44].

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Figure 6. Global crude oil trade network exhibits a core-edge structure in representative years, where dark red and light red nodes represent core and sub-core countries, respectively, and white nodes represent marginal countries, the larger node and darker color illustrate the greater the trade influence^[57]: (a) 2007; (b) 2010; (c) 2020.

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图 7 2000, 2002, 2004, 2009, 2018和2020年全球高岭土贸易网络的社团演变, 不同的颜色代表不同的社团, 节点的大小代表贸易国家的加权度^[94]

Figure 7. Evolution of communities in the global kaolin trade network in 2000, 2002, 2004, 2009, 2018, and 2020, different colours represent different associations, size of the nodes in the network represents the weighted degree of the trading country^[94].

DownLoad: Full-Size Img PowerPoint

图 8 供应链网络的脆弱性随时间的状态转换^[95], 其中TIME =0 时, y₀表示中断事件的初始影响, TIME = t^max时, yt^max表示中断事件的全部影响, T表示网络从中断中恢复的时间速度. y₀和yt^max 刻画了网络的鲁棒性, 而T则描述了网络的可恢复能力

Figure 8. State transitions of supply chain network vulnerability over time^[95], where TIME = 0, y₀ represents the initial impact of the disruption event, TIME = t^max, yt^max represents the complete impact of the disruption event, and T denotes the speed at which the network recovers from the disruption. y₀ and yt^max characterize the robustness of the network, while T describes the network's recoverability.

DownLoad: Full-Size Img PowerPoint

图 9 节点中断条件下铜贸易多层网络风险传播模型, 网络中的节点代表国家, 边代表贸易关系, 红色节点代表受风险冲击影响的国家, 红色边代表受风险冲击的边, 虚线表示产业链的层间关系^[132]

Figure 9. Risk propagation model of copper trade multilayer networks under node interruption conditions, nodes in the network represent countries, the edges represent trade relations, red nodes represent countries affected by the shock, the red edge indicates the edge affected by the shock, the dotted line represents the inter-layer relationship of the industrial chain^[132].

DownLoad: Full-Size Img PowerPoint

表 1 常见的供应链网络类型

Table 1. Common types of supply chain networks.

供应链网络类型	内容
物流网络^[17]	实际货物的运输、仓储、配送等
信息流网络^[18]	订单、库存信息、生产计划等的传递和共享
资金流网络^[19,20]	货币和财务信息的流动, 包括支付、结算、融资和资金管理等.
价值链网络^[21]	价值在供应链中的创造和传递, 涉及生产、分销、销售等环节
合作网络^[22]	各参与方之间的合作和协同关系, 包括供应商、制造商、分销商等
数字化网络^[23]	数字技术在供应链中的应用, 如物联网、大数据、人工智能等
全球供应链网络^[24]	跨国和全球范围内的供应链关系, 包括国际贸易、全球物流等
生态系统网络^[25]	将供应链视为生态系统, 关注各组成部分之间的相互关系和可持续性

DownLoad: CSV

map

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Vol. 73, Issue 19 - 2024-10-05

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