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Network analysis: definition & important aspects for buyers
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Network analysis in a nutshell:
Network analysis is a systematic method for examining and visualizing relationships and dependencies between different actors or elements in a system. In purchasing, it enables the strategic evaluation of supplier-customer relationships and the identification of risks and optimization potential in the supply chain.
Example: A car manufacturer conducts a network analysis of its 250 main suppliers and identifies 3 critical Tier 2 suppliers that influence 65% of its just-in-time production, whereupon targeted risk management measures are implemented.
Contents
What is network analysis?
Network analysis is a methodical approach to analyzing relationships and interactions within complex procurement networks. It views supplier management, customers, products and other players as nodes in a network and analyzes the connections between them. This analysis enables companies to identify patterns, increase efficiency and make more informed strategic decisions.
Core elements of network analysis
Importance of network analysis in purchasing
In the area of procurement, network analysis enables an in-depth understanding of supplier relationships and supply chain structures. It helps to identify critical suppliers, recognize risk management at an early stage and manage relationships strategically. By analyzing network dynamics, buyers can develop more effective negotiation strategies and improve the efficiency of procurement processes.
Network analysis: from manual mapping to data science
Building on the theoretical understanding of network analysis, it is crucial in practice to effectively manage supplier relationships within the supply chain. Traditional manual mapping of supplier networks is reaching its limits in an increasingly complex and globalized economy. Therefore, there is a need to introduce modern approaches that enable the analysis of large amounts of data and provide deeper insights into hidden network structures.
Old: Manual network display
Traditional approach: In the past, network analysis was often carried out by manually recording and presenting the relationships between supplier management, customers and internal departments. This was usually done using spreadsheets or simple diagrams. This approach was characterized by time-consuming data collection processes and limited timeliness of information. The main difficulties lay in the maintenance of the data, the lack of dynamism in the event of changes and the limited ability to capture complex relationships.
New: Data-Driven Network Analysis
Network analytics: The modern approach uses advanced data science methods and big data technologies to make network analysis more efficient and effective. By integrating real-time data from ERP systems, CRM platforms and external data sources, companies can gain detailed insights into their networks. Risk management and machine learning enable the identification of patterns that remain hidden in manual analyses. This leads to improved decision-making, the ability to identify risks at an early stage and process optimization within the supply chain.
Practical example: Optimizing supplier relationships in the automotive industry
A leading automotive manufacturer implemented a data-driven network analysis to optimize its complex supply chain. By collecting and analyzing data from over 500 suppliers, the company was able to identify critical nodes. This enabled a 30% reduction in supply bottlenecks and a 20% improvement in delivery reliability. The use of network analytics also led to cost savings of over 15 million euros per year thanks to more efficient logistics processes and better negotiation potential with strategic suppliers.
Conclusion on the network analysis
Network analysis is an indispensable tool in modern purchasing that enables strategic decisions to be made by systematically analyzing supplier relationships. It helps to identify critical suppliers, minimize risks and optimize procurement processes. Despite challenges such as data availability and complexity, it offers enormous potential for effective supply chain management, especially in combination with new technologies such as AI and blockchain.
