How graph databases are improving the resilience of supply chains
Supply chains underpin many aspects of modern daily life. From keeping supermarket shelves stocked to ensuring hospitals have vital drugs, they have become important links that keep the nation functioning.
In recent years, key supply chains have become increasingly digitised, ushering in transformative changes for business and consumers. On one hand, there have been more opportunities for innovation and efficiency improvements. On the other, increasing interconnectedness has left supply chains exposed to new vulnerabilities. The latter leaves both the physical and digital systems in the global supply chain more susceptible to disruptions.
Supply chain attacks
Cybersecurity threats are a clear example of how the stability of supply chains is under fire. In November 2024, hackers executed a ransomware attack1 on supply chain management specialist Blue Yonder and breached the company's managed services environment.
This caused major delays for a number of grocery and retail stores across the United Kingdom, making it difficult for retailers to manage staff payments and schedules, underscoring the severe disruptions such incidents can cause.
There is clearly a necessity to proactively plan for various scenarios to avert crises. If plans to mitigate potential impacts are not mapped out in advance, these events have the potential to completely halt production and impact business revenues.
The efficiency and efficacy of how supply chains run fundamentally rely on how its data is used, but without sufficient safeguarding or optimisation of that data to circumvent real-world disruptions, they remain vulnerable to setbacks.
The data complexity challenge
The inherent complexity of supply chains comes as no surprise, given the web of producers, warehouses, transport, distribution ports, and stores around the world. Any single disruption within this network can trigger a chain reaction throughout the entire system, making visibility critical to preventing subsequent failures.
Unfortunately, extracting meaningful insights from raw supply chain data presents its own set of challenges. Composed of rigid structures of tables, rows, and columns, traditional data models struggle to effectively capture the intricate relationships between data sets.
As a result, the ability to extract valuable data insights that could inform a response to disruptions becomes significantly limited.
The role of graph databases
To address these challenges, graph databases have emerged as an innovative solution. While traditional models embrace a structure that grapples with analysing complex relationships, graph databases are uniquely structured using 'nodes' and 'edges' that capture those important nuances.
In this model, 'nodes' represent entities, like people, products, or locations, while 'edges' represent the relationship between two nodes – for example, how they are connected to one another. These fundamental properties are invaluable for supply-chain professionals looking to visualise their supply chain as the network that it is, in the digital form.
A practical use case of graph databases in action is transport optimisation. A supply chain organisation could, for example, create nodes to represent each wholesaler and connected retailer.
An edge could then be applied to highlight the distances between them. By running the appropriate query or request in the data model, the output should present the analyst with the 'best' (fastest and cheapest) supplier from which goods can be transported and made ready for purchasing.
It's those edges between entities that make graph database technology a powerful tool for uncovering valuable insights, utilising those links to map complex and telling relationships. While the rigid format of traditional data models makes uncovering relationships much harder, supply chains, operating as networked structures, are a more natural fit.
Putting digital twins to work
Beyond the physical realm, supply-chain resilience also means mitigating disruption in the digital realm. Cyberattacks, such as the incident at Blue Yonder, can vastly impact digital operations. Given this, businesses are exploring digital twin technology as a tool for proactively combatting these potential issues.
Organisations are leveraging 'knowledge graphs' to create virtual replicas of their supply chains, enabling them to test various scenarios and predict multiple outcomes related to cyber-security risks.
This task involves creating a connected, virtual model of a supply chain, which produces a holistic and granular view of how systems interact within the network. This network encompasses the users and the groups they belong to, and the permissions granted to each member.
As recurring or interconnected events are continuously captured, the digital twin becomes increasingly accurate, empowering cyber-security and supply chain analysts to respond more swiftly and effectively in the present while shaping their future strategies.
This knowledge is crucial because it clearly signposts when organisations need to explore alternative routes, reassess transit times, and evaluate the cost impacts. By integrating cyber-security modelling with supply chain optimisation, organisations can develop a robust strategy to stay proactive in the face of disruption and re-prioritise resources in quicker succession.
Boosting supply chain resilience
To thrive in an interconnected world, organisations need to move beyond traditional data management methods. By harnessing the power of graph databases, businesses can gain new perspectives on their supply chains, uncover and prepare to tackle vulnerabilities, and develop proactive strategies to stay ahead of disruptions.
Organisations that achieve this will have a real point of difference from their competitors who don't. Disruptions will be reduced and client satisfaction maintained.
