The supply chain is a complex network of suppliers, manufacturers, retailers and logistics service providers that aims to ensure the smooth flow of goods and information. The modern supply chain faces numerous challenges:
Datum
23.07.2024
content.autor.writtenBy
Supply chains are often globally interconnected and involve a large number of players, products and processes. Companies have to deal with a variety of data sources and need to use them efficiently in order to make well-founded decisions
The use of real-time data enables precise coordination of transportation and logistics processes. Automation technologies reduce human error and speed up processes. However, companies must ensure that their systems are reliable and guarantee data integrity.
The digital supply chain offers the opportunity to identify risks at an early stage and respond to them. However, companies must be able to analyze the right data and take appropriate action. A strong supply chain resists disruption through data analysis, holistic risk management and strategic measures.
Complete traceability is crucial, both in the event of recalls or quality problems and when fulfilling obligations under the Supply Chain Act.
The supply chain is the backbone of many companies and influences their efficiency, profitability and competitiveness. From small retailers to multinational corporations, all companies rely on smooth supply chains to deliver their products and services. The different parts of the supply chain (such as indirect purchasing, direct purchasing, logistics, etc.) are represented to varying degrees in companies. Regardless of the perspective from which the supply chain is viewed, all areas share a common theme: data-based decision-making. The existence of data is usually not the main problem. Rather, the data is often distributed across several IT systems. This makes it difficult to merge the data into a single source of truth. However, only a holistic view of all available data enables actual data-driven decisions. But even once the data is in one place, the challenge of deriving concrete insights and recommendations for action from the overwhelming amount of information remains.
Data consolidation
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.
Data analysis
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.
About the authors: Elena Kohlwey & Matthias Bauer
Elena Kohlwey has been a Data Scientist and Data Engineer at X-INTEGRATE (part of TIMETOACT GROUP) since 2024 and brings more than 5 years of expertise as a graph database expert. Her mission is to model networked data as a graph and use graph queries and algorithms to bring deeply hidden insights to the surface. Elena has been very active in the Neo4j (graph database provider) community for years. She regularly speaks at conferences on graph topics and is also one of the approximately 100 active Neo4j Ninjas worldwide.
Matthias Bauer has been Teamlead Data Science at X-INTEGRATE (part of TIMETOACT GROUP) since 2020 and brings more than 15 years of expertise as a Solution Architect. Using data to create great things and achieve added value - in his words: data thinking - is his passion. Matthias is experienced in artificial intelligence, data science and data management, covering a wide range of data-related issues from data warehousing to data virtualization.
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