In the ever-evolving landscape of modern supply chains, comprehensive lifecycle orchestration has become essential. One of the major challenges faced is the integration of upstream and downstream partners, as different management systems often fail to communicate effectively. This lack of dynamic visibility makes it difficult to trace the impact of supply disruptions from one end to the other. To meet the demands of today’s complex supply chains, a system enabling dynamic visibility is crucial.
Traditional supply chain management has relied on relational database management systems (RDBMS) and structured query language (SQL) for visibility. However, these traditional tools have struggled to keep up with increasing complexity. Modern supply chains require real-time insights, predictive risk analysis, and effective mitigation strategies. This has given rise to the need for intelligent visibility, which encompasses both structural and dynamic aspects.
While significant advances have been made, only 3% of companies have achieved autonomous execution, with many still relying on predictive or prescriptive use of data (67% and 40%, respectively). Even with application programming interface (API) integration, the limitations of RDBMS have highlighted the demand for solutions that offer quick insights into today’s non-linear supply chains.
Transitioning from structural to dynamic visibility is paramount for companies seeking a competitive edge in their supply chains, and this is where graph databases come into play. Unlike traditional databases that use tables for data storage, graph databases represent data as nodes and edges, making them ideal for modeling complex networks like supply chains.
Graph databases excel in querying connected data, allowing for rapid identification of relationships and tracing second-tier suppliers for specific product variants. They offer several advantages over relational databases, including improved scalability for large datasets and flexibility in handling various data types—structured, semi-structured, and unstructured. Consequently, graph technology, initially popular in social networks, has become indispensable for navigating the intricacies of modern supply chains.
Utilizing data from enterprise resource planning (ERP) systems, quality and compliance documents, export data, and more, graph databases create 360-degree supply chain visualizations. Key capabilities that enhance supply chain visibility include:
- Betweenness centrality: Identifying potential bottlenecks by measuring a node’s frequency on the shortest paths within the network.
- Degree centrality: Assessing a node’s load and dependency within the network, such as the significance of a particular warehouse in the supply network.
- Regional dependence: Analyzing natural clusters within the network to highlight areas of high interdependency, such as the proximity of vendors.
These metrics empower supply chain leaders to identify and mitigate risks and vulnerabilities. During disruptions like natural disasters, graph databases can swiftly simulate impacts across the supply chain, allowing for the evaluation of alternate scenarios and the implementation of effective mitigation strategies.
Graph technology is experiencing growing adoption across various sectors and is expected to play a central role in climate change response and fraud detection, with 80% of data and analytics innovations adopting this technology, according to Gartner. Major firms are increasingly turning to graph technology, including hyperscalers and consultancies. In addition to supply chain visibility, it facilitates data integration across silos, quick simulations, risk mitigation, and scalability for complex data. Moreover, it enhances transparency, trust, and ethical sourcing monitoring.
The future of supply chain management is intertwined with the advancements in graph technology, empowering enterprises to analyze, understand, and respond to complex data landscapes. Those equipped with a robust graph database tech stack will lead the race towards agile and efficient supply chains.
