The last decade of engineering and manufacturing business development demonstrated that data is increasingly important for modern manufacturing businesses as it forms a major part of the key resource that drives innovation. In the past, data was seen as an asset that companies need to manage to keep track of what they do (eg. engineering data management or PDM to manage CAD files) and it was a typical “expense” that added to engineering operation. Not anymore.
Today, data is a strategic asset that can be leveraged to build better products, enhance operational efficiency, and increase customer satisfaction. Data provides insights into customer needs and preferences, enabling manufacturers to develop more relevant products and services. It also helps in predicting customer behavior and segmenting markets more effectively, thus allowing manufacturers to reach more customers with targeted messages.
From Data Control to Data Intelligence
Check my five-year-old presentation at the PI PLMx event in Hamburg, which speaks about the PLM innovation path from data control to intelligence. As we move forward, past the pandemic, and explore a variety of options on how manufacturing companies can innovate in the modern digital world, it is becoming obvious that “networks” and “data intelligence” are among the two most powerful paradigms that are taking the manufacturing industry by storm.
In the past manufacturing companies were focused on applications that can help them to operate and run business processes. While it is still true that companies need to have enterprise software to operate, it is quickly becoming obvious that data is a new platform and in the extremely connected manufacturing world, focus on data, data availability, data quality, and the ability to use data for making important decisions – those are things that will define the future of data in PLM.
The most interesting question that PLM system vendors will have to answer in the next few years is what data model and technology can be used to deliver rich data management and data intelligent options. Many existing PLM solutions (eg. Teamcenter, Windchill, MatrixOne, Arena Solutions, Aras) are relying on SQL databases, we built OpenBOM product lifecycle management and product data management capabilities using polyglot persistence multi-tenant data management architecture, which allows using OpenBOM with multiple companies and shares data instantly for the supply chain management. However, a very special aspect of the OpenBOM data model and infrastructure is the use of graphs to manage relationships and data semantics. Let’s talk more about why graphs are becoming so important these days.
Catch The Knowledge Graph Train
If you never heard the word “knowledge graph”, I recommend you to check this article. It will give you some technical and business background. In a nutshell, any graph is built from nodes and A knowledge graph represents a network of real-world entities — i.e. objects, events, situations, or concepts — and illustrates the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph”. A knowledge graph is made up of two main components: nodes and edges. Any object, place, or person can be a node. An edge defines the relationship between the nodes.
There are two types of graphs – label property and RDF. Here is an example of LPG graph (which is simpler to understand).
The strategic perspective of knowledge graph usage is to build an overlay data model capable to hold information from multiple systems and multiple data objects.
Jumping from geeking about technology back to business, I found the following passage extremely important to understand.
By 2025, per Gartner, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision-making across the enterprise. For more examples check this article – Are organizations finally understanding the capabilities of graph technologies?
OpenBOM Roadmap For Graphs
At OpenBOM, we are bringing graphs as a solution for painful problems – to build relationships between assemblies and components, to analyze the impact of changes, to analyze supplier dependencies, and many other characteristics. More sophisticated semantic capabilities of OpenBOM will allow you to manage the similarity of data properties to run an overall cost estimation for complex assemblies delivered by many organizations.
Heads up – if you read about OpenBOM 2023 roadmap, you probably noticed OpenBOM intelligent web services. The infrastructure for OpenBOM services will include graph navigation components that will be used for impact analysis together with OpenBOM BOM compare function.
Where are you with your knowledge graph journey? Is it part of your product lifecycle management strategy? Are you looking at how to build your company’s competitive advantage based on connecting computer-aided design, product development process, and enterprise resource planning together? It can be a powerful combo. Are you ready to start one? Do you have a strategy to build a knowledge graph for product data in your organization? This is where the digital thread begins. It is time product lifecycle management solutions step beyond old and limited relationship databases and start building dynamic knowledge graphs to describe product data and all dependencies with scalable and modern technologies.
Interested to learn more about what OpenBOM is building for knowledge graphs and data analytics? Check with us – contact us via email (oleg @ openbom dot com) and join the discussion about the future of graphs in manufacturing.
In the meantime, REGISTER FOR FREE to check how OpenBOM can help you today.