Today, all leading PLM software vendors are selling solutions they call “digital”. I find this interesting because most of these platforms created 20–30 years ago as product data management tools. How all these solutions overnight became “digital” and what does it mean? Don’t misunderstand me – those are good systems built to support the product development process in the way we understood it decades ago.
My point is that all these solutions are built around a specific paradigm: a central database with an object-oriented CRUD model, managing versions (mostly files) and workflow-like approval processes. This is 90% of what PLM systems do today and what they’ve done for decades. The core of process management back in those days was “to make a design” and “through it over the wall of manufacturing”. The rest will be done by manufacturing business processes and supply chain management.
What makes solutions truly digital? In my view, it’s about changing the way we interact with data and how we trigger events based on that data.
Future Memos
In my daily work, I practice methods that help me get the right information at the right time, in a contextual and time-sensitive form. For example, I send emails to myself with ideas tagged with specific labels or set them to appear at specific times. The logic of this approach is to use present experiences to guide future decisions. It’s a practical “hack”: creating reminders based on current thinking and scheduling them for future moments when they might influence decision-making. By documenting and revisiting these insights, I can make choices aligned with past experiences, whether related to health, work-life balance, or personal motivation. The idea is to actively shape the future by anchoring it to meaningful moments in the present.
Now, let me translate this idea into PLM and product development. The current PLM paradigm revolves around storing files and data to create a “single source of truth.” There’s nothing wrong with that—it gives me a history of files and data for reference. However, the problem is that we don’t store or organize this data in a way that’s useful for future decision-making.
Making Data To Work for Us (In a Digital Way)
We need to move from thinking about document versions to thinking about data, enabling it to drive decision-making and improve decision effectiveness. For example:
- How can I remind myself that the last time we ordered components from a specific supplier, the order was delayed, and there were quality issues?
- How can I know that a component used in one product is not used anywhere else and might need to be replaced with a more standard alternative?
These scenarios become possible when we shift from the “document management store” mentality of PLM to a focus on data intelligence. The question then becomes: How do we capture and organize engineering and related information to make these outcomes possible?
Over the past 20 years, data management technology has gone through a period of explosive growth. Now, with AI, we’re doubling down on how to make data more actionable.
Rethinking how we deal with data and creating digital models to describe products is essential. These models must make data accessible to teams and enable them to work differently. For example, imagine actionable reminders for the future that help teams make better decisions.
To achieve this, we need models capable of producing results like:
- Identifying items not used in any current products.
- Highlighting suppliers with the most problem reports associated with their parts over the past year.
- Identifying the vendor with the highest payment totals, supplying most of the components for your products.
Now, imagine these insights showing up instantly—right when engineers are working on a new design or procurement teams are deciding whether to approve a vendor.
Knowledge Graphs and PLM modeling
Knowledge graphs can change Product Lifecycle Management (PLM) by creating intelligent, interconnected data networks that transform how organizations understand and utilize product information. By mapping complex relationships between design, manufacturing, and performance data, knowledge graphs enable more dynamic, context-aware decision-making across the entire product development lifecycle.
The semantic intelligence of knowledge graphs allows companies to move beyond traditional data silos, creating a unified digital thread that connects information, predicts potential issues, and accelerates innovation through enhanced data visibility and insights.
Getting Into Agent Era?
I found it interesting to learn that ChatGPT is moving into an agentic mode by introducing ‘Tasks’ in beta. This further confirms the idea that ‘data disconnected from applications‘ is trending and will soon become a standard way to manage our activities—both in business and everyday life (Stay tuned, I’m planning to talk more about it later this week).
What is my conclusion?
Rethinking how we work with data is a fundamental challenge for manufacturing companies. We must consider an entire lifecycle and include all criteria founded with product data – product quality, development cost, customer feedback, supply chain, and many others that can be found in business systems. This is where I believe the most innovative engineering and manufacturing organizations will focus when developing future digital systems. Whoever gets the data right will win the future.
Just my thoughts…
Best, Oleg
I am the co-founder and CEO of OpenBOM, a digital-thread platform that offers cloud-native collaborative services, including PDM, PLM, and ERP capabilities. With extensive experience in federated CAD-PDM and PLM architecture, I advocate for agile approaches, open product models, and the adoption of cloud technologies in manufacturing. Please note that my opinions may reflect my work at OpenBOM and could be unintentionally biased.
