Knowledge graphs and digital twins to optimize supply chains
Article by Neo4j A/NZ Managing Director Peter Philipp.
Organizations increasingly see the potential of digital twins.
CIOs recognize their potential to create virtual, highly detailed, and completely faithful reproductions in software of their real-world assets, such as a factory or complex industrial workflows in real time. And in particular, CIOs are exploring the value of digital twin technologies to deliver supply chain insights. They are using digital twin technology to optimize what, in light of a pandemic and a great European war, are often broken supplier networks.
This move towards digital twin technology is based on the understanding that the technology can have more impact in a supply chain context when used in conjunction with knowledge graphs. These technologies, combined, can provide powerful insights into supply chain optimization.
In 2012, Google announced that it was using a knowledge graph behind its search engine. Since then, the convergence of analytics, data science, machine learning, and AI has sparked an appetite for using knowledge graphs.
Indeed, a knowledge graph data structure has the ability to make smarter and more predictive decisions. A knowledge graph is just an interconnected set of data, albeit very large and complex, enriched with meaning or semantics. It allows users to reason about the underlying data and use it to make complex decisions.
And knowledge graphs become more efficient if they take advantage of a graph schema. The reason is due to the inherent limitations of SQL and relational when it comes to supporting queries of the numeric twin type. It also depends on the specific form of data you want to work with in a supply chain context.
On the other hand, in a graph-based knowledge graph, reading the relationship from storage and querying the graph is straightforward – users simply traverse the graph. And if developers add a third layer in the form of semantics, a working knowledge graph is obtained. The graph can be supplemented with useful graphing algorithms and other tools.
Create a connected virtual supply chain
It’s simple with graph technology to create a rich, responsive representation of complexity, like a supply chain in a digital twin. It is always difficult to have complete visibility into a supply chain because it is a complex and multidimensional connected digital network. The current supply chain disruption has exacerbated this lack of visibility. As such, knowledge graphs are the best tools for connecting all facets of the supply chain, from materials to products, from factories to distribution centers and shipping.
In a graph, decision making also becomes much easier. The knowledge graph provides context so that decisions can be made holistically, considering many interrelated supply chain dependencies.
Graph-based twin digital knowledge graphs that bring data together and create a connected virtual supply chain give brands what they really need right now. Brands get a traceable and very granular picture of all products, suppliers and facilities in this supply chain, as well as the relationships between them. Putting a supply chain on a chart delivers real loyalty across everything from the oil and gas industry to nationwide retail distribution.
To better understand a supply chain via a digital twin, consider modeling it in a graph first, then in a complete knowledge graph. By using a graph, all of that complex supply chain data and deeply hierarchical and even hidden recursive events will become much easier to expose.
A graph-based knowledge graph provides the flexibility, performance, and analytics capabilities CIOs need to create, manage, and query enterprise-scale digital twins. Why not discover a digital twin powered by a knowledge graph?
