Home Digital Graph-Based Digital Twin Will Move Well Beyond Manufacturing in 2023
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Graph-Based Digital Twin Will Move Well Beyond Manufacturing in 2023

by uma


Solution architect Michael Moore on the coming synergy of three powerful techniques: Graph technology, knowledge graph and digital twins

One of the biggest trends we see is the increased use of knowledge graphs and graphs for digital tins. Why? Just as the popularity of digital twins grows, so does the complexity of the use cases people are attempting. Digital twins used to be about connecting just one single asset, for instance, but now we want to model a system of assets or even a digital twin of an entire organization. 

The Knowledge Graph-Digital Twin combination

Knowledge graphs capture the real-world complexity and volume of the digital twins today’s enterprises demand. Why? Because a knowledge graph excels at harmonizing complex data and flexibly, modeling massive real-world structures and their business logic.

With a modern native graph database at the foundation that can capture many billions of relationships, organizations can manifest a digital twin in any structure or process within any industry, leading to a wide variety of use cases. A graph data platform also provides the flexibility, performance, and analytical capabilities needed to build, manage, and query digital twins on an enterprise scale economically, unifying data across myriad sources to provide maximum business value.

In addition, knowledge graphs are able to bring the most advanced analytics to digital twins, as they support powerful queries, as well as data science and machine learning techniques from algorithms to embeddings. Increasingly, it will become more common for digital twins to replicate entire processes digitally within an organisation through their power.

Digital twins used to only be deployed by manufacturers, but they’re no longer limited to that industry. Indeed, there’s been a 10-fold increase in the number of digital twin implementations across various industries. CIOs now see the value of a digital twin in any structure or process within any industry, and as result, there are a growing variety of use cases. In 2023, we can expect to see a major uptick in the use of digital twins in a number of sectors.

Multiple sector digital twin use cases are emerging

For supply chain, that might look like gaining complete visibility into complex networks, connecting diverse product validation life cycle data for automotive, and mirroring very complex production lines (as in, life sciences manufacturing). In the context of cybersecurity, digital twins will improve risk assessment and evaluation of vulnerabilities in live production systems, network environments, or cloud instances to proactively prevent cyberattacks.

For construction, digital twins will improve the analytical capabilities of building information modeling (BIM) and provide real-time status of inventory, working conditions, and resources for more efficient cost estimation, contractor financing, better material management, better tendering (bidding), etc. For transportation, digital twins will perform analyses and scenarios of multiple interconnected transportation modes (bus, rail, metro, etc.) digitally before they are applied in the real environment. This will almost certainly be part of creating smart cities, e.g. digital networking of road users, intelligent traffic control, etc.

Finally, for customer experience/CX, digital twins will simulate and anticipate customer behaviour by getting a deeper understanding of the customer. In parallel, organisations will continue to explore how to leverage knowledge graphs for truly responsible AI. They provide the context such systems need to convince users and regulators. Context enhances the accuracy of ethical decision-making, improves explainability by providing provenance of data, and mitigates the fear of decisions about me by biased artificial intelligence by offering new avenues of analysis. 

As a result, as responsible AI takes off, there will be a huge increase in demand for knowledge graphs to make it real. The time to start getting this right yourself is now.

About Author:


The author is Principal, Partner Solutions and Technology at the world’s biggest native graph database company, Neo4j


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