A Review of Supply Chain Research Based on Complex Network Theory

Abstract

The 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 for enhancing an 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, the application of 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 and network model-based approaches. In empirical data-based research, scholars use common supply chain databases or integrate multiple data sources to identify supply chain participants and clarify their attributes, behaviors, and interactions. Alternatively, 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 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, key nodes within the supply chain network are identified. Community detection algorithms are employed 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, game theory has been applied to investigate cooperative competition, resource allocation, and strategy selection among enterprises within the supply chain network. This paper reviews the research content 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 and discusses key challenges, such as how to obtain accurate, comprehensive, and timely supply chain network data, propose standardized data processing methods, and determine the attributes of supply chain network nodes and the strength of their relationships. Furthermore, research on supply chain network structure has not yet fully captured the unique characteristics of supply chain networks. Existing models and methods for vulnerability assessment often fail to account for the dynamic and nonlinear features 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 within supply chain networks, along with multiple sources of uncertainty, further complicate the prediction and analysis of multi-party decisionmaking behavior. The paper also outlines several key directions for future research. One direction involves applying 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 longterm dependencies in time-series data could enhance the analysis of network structure evolution and improve the prediction of future states. The application of reinforcement learning algorithms could also enable adaptive adjustments to network structures and strategies in response to changing conditions and demand, thereby enhancing the adaptability and response speed of supply chain networks during emergencies. This paper aims to contribute valuable insights to supply chain research and to promote the development and application of complex network methods in this field.