Social Network Analysis in Supply Chain Optimization: Practical Applications and Case Examples

Abstract

In today’s dynamic and globally interconnected markets, the optimization of supply chain networks has become a pivotal determinant of organizational efficiency, resilience, and competitiveness. Traditional models of supply chain analysis often focus on linear workflows and quantitative logistics metrics, overlooking the complex interdependencies and relational structures among entities. This research explores the integration of Social Network Analysis (SNA) as a powerful methodological framework to uncover hidden patterns, central actors, and systemic vulnerabilities within supply chain ecosystems. By conceptualizing the supply chain not merely as a sequence of transactions but as a network of social and organizational interactions, SNA facilitates a deeper understanding of how information, materials, and influence flow across different tiers. The study begins by establishing the theoretical foundation of SNA in the context of supply chain management, introducing key metrics such as degree centrality, betweenness centrality, and network density. These indicators are then applied to real-world supply chain datasets drawn from industries including automotive manufacturing, pharmaceutical distribution, and electronics. Through comparative case analysis, we demonstrate how SNA tools can identify critical suppliers, potential bottlenecks, and risk propagation nodes that are often missed in conventional supply chain models. One notable case example involves a multinational electronics firm whose supply network exhibited high centralization around a single logistics intermediary. SNA revealed that disruptions at this node would have cascading effects across multiple product lines—insight that led the firm to diversify its partnerships and improve network robustness. In another case, a pharmaceutical supply chain demonstrated low clustering among regional distributors, signaling weak coordination and information sharing. Strategic realignment based on SNA insights resulted in improved distribution efficiency and reduced lead times. Furthermore, the paper discusses the practical implications of implementing SNA in enterprise-level decision-making. Emphasis is placed on how supply chain professionals can leverage SNA for supplier selection, risk management, collaboration strategies, and sustainability goals. The research also addresses challenges in data collection, confidentiality, and the dynamic nature of supply networks, offering solutions for the effective integration of SNA tools with enterprise resource planning (ERP) systems and business intelligence platforms. Ultimately, this study establishes Social Network Analysis as a versatile and actionable approach to optimizing supply chains in an era where agility, transparency, and adaptability are paramount. The findings reinforce the value of network thinking in driving strategic transformation across complex and interlinked supply chain environments.