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Knowledge graphs and digital twins to optimise supply chains

By Contributor
Fri 3 Jun 2022

Article by Neo4j A/NZ general manager Peter Philipp.

Organisations are increasingly seeing the potential in digital twins.

CIOs are recognising their potential for creating virtual, highly detailed, and totally faithful reproductions in software of their real world assets, like a factory or complex, real-time industrial workflows. And in particular, CIOs are exploring the value of digital twin technologies to provide insights into the supply chain. They are using digital twin technology to optimise what, in the light of a pandemic and a major European war, are often broken supplier networks. 

Informing this move to digital twin technology is the understanding that the technology can have more impact in a supply chain context when employed in conjunction with knowledge graphs. These technologies, in combination, can provide powerful insights into supply chain optimisation. 

In 2012, Google announced 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 the use of knowledge graphs. 

This is because a knowledge graph data structure has the capability to make smarter, more predictive decision-making. A knowledge graph is just an interconnected, albeit very large and complex dataset enriched with meaning or semantics. It allows users to reason about the underlying data and use it for complex decision-making. 

And knowledge graphs get more effective if they take advantage of a graph schema. The reason is attributable to the inherent limitations of SQL and relational when it comes to supporting digital twin-style queries. It’s also down to the specific shape of the 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 just traverse the graph. And if developers add a third layer in the form of semantics, a working knowledge graph is achieved. The graph can be augmented by useful graph algorithms and other tools. 

Creating a connected virtual supply chain

It’s simple with graph technology to create a rich, reactive representation of complexity, like a supply chain in a digital twin. It is always hard to gain complete visibility into a supply chain because they are a complex, multi-dimensional connected digital network. The current supply chain upheaval has exacerbated this lack of visibility. As such, knowledge graphs are the best tools to connect all facets of the supply chain, from materials to products, plants to distribution centres, and shipping. 

In a graph, decisioning gets a whole lot easier, too. The knowledge graph provides context so decisions can be made holistically, taking many interlocking supply chain-arena dependencies into consideration. 

Graph-based digital twin knowledge graphs that bring data together and create a connected virtual supply chain give brands what they really need right now. Brands get a trackable, highly granular picture of all the products, suppliers and facilities in that supply chain, and the relationships between them. Putting a supply chain into a graph provides real-world fidelity in everything from the oil and gas sector to nationwide retail distribution. 

To get a better handle on a supply chain via a digital twin, think about modelling it in a graph initially and then as a full knowledge graph. Using a graph, all that complex supply chain data and deeply hierarchical and recursive events, even hidden, will become far easier to expose. 

A graph-based knowledge graph provides the flexibility, performance, and analytical capabilities CIOs need to build, manage and query digital twins on an enterprise scale. Why not check out a digital twin fuelled by a knowledge graph?

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