Graph Databases in the Supply Chain

Traditional relational databases already reach their limits when merging data. They are not optimal for representing complex relationships and can cause difficulties when querying and analyzing data.

Why traditional databases are not sufficient:

• Rigid structures: relational databases use tables with fixed columns. This makes it difficult to iteratively merge data from multiple sources.
• Scalability: As data volumes grow, relational databases reach their limits when querying across multiple tables.

Graph databases are specifically designed to model relationships between data points. They use graphs (consisting of nodes and edges) to represent these relationships.

Advantages of graph databases:

• Flexibility: graphs make it possible to map complex relationships without rigid structures.
• Scalability: Graph databases are optimized for relationship queries. They enable fast and precise analyses regardless of the overall size of the database.

Graph databases are ideal for merging data from multiple sources with different structures and relating them to each other. The schema-free nature of the database supports the iterative merging of data sources.

Graph traversals and graph algorithms enable a deep, holistic analysis of data that goes far beyond the capabilities of traditional databases. Some of the ways in which graph algorithms help to improve the efficiency and effectiveness of supply chains include

• Identifying bottlenecks in the network: graph algorithms can visualize and analyze complex networks to identify bottlenecks and inefficient segments. This enables companies to make targeted improvements and increase the overall performance of the supply chain.
• Run what-if scenarios: Using graph algorithms, companies can simulate different scenarios and evaluate the impact of changes in the supply chain. This is particularly useful for planning, risk assessment and preparing alternatives when disruptions occur.
• Optimized production planning through better forecasting: Graph algorithms can help predict future demand and supply trends. This enables more accurate production planning and helps companies to avoid over- or underproduction.
• Showing chains of effects: By visualizing and evaluating the relationships between different elements in the supply chain, companies can better understand how changes in one place impact other areas.

Graph databases offer a promising solution for these and other challenges in the supply chain. They enable flexible, powerful and scalable data analysis that traditional relational databases cannot provide. Companies embracing this technology can use their data more effectively, make better decisions and ultimately become more competitive. It's an exciting time for companies that are ready to utlise this advanced technology.