Together with Håkan Kårdén, we had the pleasure of bringing together 32 passionate professionals on November 4th to explore the future of PLM (Product Lifecycle Management) and ALM (Asset Lifecycle Management), inspired by insights from four leading thinkers in the field. Please, click on the image for more details.
The meeting had two primary purposes.
- Firstly, we aimed to create an environment where these concepts could be discussed and presented to a broader audience, comprising academics, industrial professionals, and software developers. The group’s feedback could serve as a benchmark for them.
- The second goal was to bring people together and create a networking opportunity, either during the PLM Roadmap/PDT Europe conference, the day after, or through meetings established after this workshop.
Personally, it was a great pleasure to meet some people in person whose LinkedIn articles I had admired and read.
The meeting was sponsored by the Arrowhead fPVN project, a project I discussed in a previous blog post related to the PLM Roadmap/PDT Europe 2024 conference last year. Together with the speakers, we have begun working on a more in-depth paper that describes the similarities and the lessons learned that are relevant. This activity will take some time.
Therefore, this post only includes the abstracts from the speakers and links to their presentations. It concludes with a few observations from some attendees.
Reasoning Machines: Semantic Integration in Cyber-Physical Environments
Torbjörn Holm / Jan van Deventer: The presentation discussed the transition from requirements to handover and operations, emphasizing the role of knowledge graphs in unifying standards and technologies for a flexible product value network
The presentation outlines the phases of the product and production lifecycle, including requirements, specification, design, build-up, handover, and operations. It raises a question about unifying these phases and their associated technologies and standards, emphasizing that the most extended phase, which involves operation, maintenance, failure, and evolution until retirement, should be the primary focus.
It also discusses seamless integration, outlining a partial list of standards and technologies categorized into three sections: “Modelling & Representation Standards,” “Communication & Integration Protocols,” and “Architectural & Security Standards.” Each section contains a table listing various technology standards, their purposes, and references. Additionally, the presentation includes a “Conceptual Layer Mapping” table that details the different layers (Knowledge, Service, Communication, Security, and Data), along with examples, functions, and references.
The presentation outlines an approach for utilizing semantic technologies to ensure interoperability across heterogeneous datasets throughout a product’s lifecycle. Key strategies include using OWL 2 DL for semantic consistency, aligning domain-specific knowledge graphs with ISO 23726-3, applying W3C Alignment techniques, and leveraging Arrowhead’s microservice-based architecture and Framework Ontology for scalable and interoperable system integration.
The utilized software architecture system, including three main sections: “Functional Requirements,” “Physical Twin,” and “Digital Twin,” each containing various interconnected components, will be presented. The Architecture includes today several Knowledge Graphs (KG): A DEXPI KG, A STEP (ISO 10303) KG, An Arrowhead Framework KG and under work the CFIHOS Semantics Ontology, all aligned.
👉The presentation: W3C Major standard interoperability_Paris
Beyond Handover: Building Lifecycle-Ready Semantic Interoperability
Jean-Charles Leclerc argued that Industrial data standards must evolve beyond the narrow scope of handover and static interoperability. To truly support digital transformation, they must embrace lifecycle semantics or, at the very least, be designed for future extensibility.
This shift enables technical objects and models to be reused, orchestrated, and enriched across internal and external processes, unlocking value for all stakeholders and managing the temporal evolution of properties throughout the lifecycle. A key enabler is the “pattern of change”, a dynamic framework that connects data, knowledge, and processes over time. It allows semantic models to reflect how things evolve, not just how they are delivered.
By grounding semantic knowledge graphs (SKGs) in such rigorous logic and aligning them with W3C standards, we ensure they are both robust and adaptable. This approach supports sustainable knowledge management across domains and disciplines, bridging engineering, operations, and applications.
Ultimately, it’s not just about technology; it’s about governance.
Being Sustainab’OWL (Web Ontology Language) by Design! means building semantic ecosystems that are reliable, scalable, and lifecycle-ready by nature.
Additional Insight: From Static Models to Living Knowledge
To transition from static information to living knowledge, organizations must reassess how they model and manage technical data. Lifecycle-ready interoperability means enabling continuous alignment between evolving assets, processes, and systems. This requires not only semantic precision but also a governance framework that supports change, traceability, and reuse, turning standards into operational levers rather than compliance checkboxes.
👉The presentation: Beyond Handover – Building Lifecycle Ready Semantic Interoperability
The first two presentations had a lot in common as they both come from the Asset Lifecycle Management domain and focus on an infrastructure to support assets over a long lifetime. This is particularly visible in the usage and references to standards such as DEXPI, STEP, and CFIHOS, which are typical for this domain.
How can we achieve our vision of PLM – the Single Source of Truth?
Martin Eigner stated that Product Lifecycle Management (PLM) has long promised to serve as the Single Source of Truth for organizations striving to manage product data, processes, and knowledge across their entire value chain. Yet, realizing this vision remains a complex challenge.
Achieving a unified PLM environment requires more than just implementing advanced software systems—it demands cultural alignment, organizational commitment, and seamless integration of diverse technologies. Central to this vision is data consistency: ensuring that stakeholders across engineering, manufacturing, supply chain, and service have access to accurate, up-to-date, and contextualized information along the Product Lifecycle. This involves breaking down silos, harmonizing data models, and establishing governance frameworks that enforce standards without limiting flexibility.
Emerging technologies and methodologies, such as Extended Digital Thread, Digital Twins, cloud-based platforms, and Artificial Intelligence, offer new opportunities to enhance collaboration and integrated data management.
However, their success depends on strong change management and a shared understanding of PLM as a strategic enabler rather than a purely technical solution. By fostering cross-functional collaboration, investing in interoperability, and adopting scalable architectures, organizations can move closer to a trustworthy single source of truth. Ultimately, realizing the vision of PLM requires striking a balance between innovation and discipline—ensuring trust in data while empowering agility in product development and lifecycle management.
👉The presentation: Martin – Workshop PLM Future 04_10_25
The Future is Data-Centric, Semantic, and Federated … Is your organization ready?
Yousef Hooshmand, who is currently working at NIO as PLM & R&D Toolchain Lead Architect, discussed the must-have relations between a data-centric approach, semantic models and a federated environment as the image below illustrates:
Why This Matters for the Future?
- Engineering is under unprecedented pressure: products are becoming increasingly complex, customers are demanding personalization, and development cycles must be accelerated to meet these demands. Traditional, siloed methods can no longer keep up.
- The way forward is a data-centric, semantic, and federated approach that transforms overwhelming complexity into actionable insights, reduces weeks of impact analysis to minutes, and connects fragmented silos to create a resilient ecosystem.
- This is not just an evolution, but a fundamental shift that will define the future of systems engineering. Is your organization ready to embrace it?
👉The presentation: The Future is Data-Centric, Semantic, and Federated.
Some of first impressions
👉 Bhanu Prakash Ila from Tata Consultancy Services– you can find his original comment in this LinkedIn post
Key points:
- Traditional PLM architectures struggle with the fundamental challenge of managing increasingly complex relationships between product data, process information, and enterprise systems.
- Ontology-Based Semantic Models – The Way Forward for PLM Digital Thread Integration: Ontology-based semantic models address this by providing explicit, machine-interpretable representations of domain knowledge that capture both concepts and their relationships. These lay the foundations for AI-related capabilities.
It’s clear that as AI, semantic technologies, and data intelligence mature, the way we think and talk about PLM must evolve too – from system-centric to value-driven, from managing data to enabling knowledge and decisions.
A quick & temporary conclusion
Typically, I conclude my blog posts with a summary. However, this time the conclusion is not there yet. There is work to be done to align concepts and understand for which industry they are most applicable. Using standards or avoiding standards as they move too slowly for the business is a point of ongoing discussion. The takeaway for everyone in the workshop was that data without context has no value. Ontologies, semantic models and domain-specific methodologies are mandatory for modern data-driven enterprises. You cannot avoid this learning path by just installing a graph database.
With all these upcoming events, I did not have the time to focus on a new blog post; however, luckily, in the 
As the year ends, I decided to take my crystal ball to see what would happen with PLM in the future. It felt like a virtual experience, and this is what I saw:
Over the last month, I have been actively engaged in the field; however, unfortunately, I have not been able to respond to all the interesting and sometimes humorous posts in my LinkedIn stream.
Initially, the Bill of Materials (BOM) existed only in ERP systems to support manufacturing. Together with the Bill of Process (BOP), it formed the heart of production execution. Without a BOM in ERP, product delivery would fail.
However, is the sBOM the real solution or only a theme pushed by BOM/PLM vendors to keep everything within their system? So far, this represents a linear hardware delivery model, with BOM structures tied to local ERP systems.
As I mentioned earlier, during the Dutch PLM platform discussion, we had an interesting debate that began with the question of how to manage and service a product during operation. Here, we reach a new level of PLM – not only delivering products as efficiently as possible, but also maintaining them in the field – often for many years.
Although scientists engaged in a discussion about the scientific evidence, there were no significant economic forces behind the scenes influencing the scientific research.
We look forward to having 
The tools for generative design, life cycle assessment, and, of course, digital twins for the various lifecycle phases can help companies to develop and manufacture more sustainable products.
Part of this challenge is the lack of education among top management, who are primarily focused on efficiency gains rather than adopting new approaches or mitigating risk.
However, first and foremost, the most critical factor in driving sustainability within organizations is the people. Where companies are challenged in creating a green image, including the introduction of the Chief Sustainability Officer (CSO), there has always been resistance from existing business leaders, who prioritize money and profitability.
And recently, we saw the 
Tempted by LinkedIn posts, I noticed the summer was full of memories, with 
The expansion of capabilities was also the moment when the confusion about the term PLM reached its peak: a PLM strategy or a PLM system?
With the availability of cloud solutions that support real-time interactions between stakeholders, either within an enterprise or in a value chain, a new paradigm has emerged: the connected enterprise.
An open SaaS infrastructure enables a company to let data flow almost in real-time. There is a lot of discussion related to data quality and governance, and if you have missed it, please read these three articles I created together with 
As technology has become more accessible than before, you no longer need an IT department to establish a PLM infrastructure. And then indeed, the people and process side needs and deserves much more attention..
In the past three weeks, between some short holidays, I had a discussion with 
Rob, I was curious whether there were any interesting comments from the readers that enhanced your understanding. For me, 
It’s easy to imagine a Digital Thread, but building one that’s sustainable and delivers measurable value is a far more formidable challenge.
Mark the date and this workshop in your agenda if you are able and willing to contribute. After the summer holidays, we will develop a more detailed agenda about the concepts to be discussed. Stay tuned to our LinkedIn feed at the end of August/beginning of September.
Data needs to be an integral, essential and valuable part of the product or service. Individuals become both consumers and producers of data, expected to input clean data, interpret dashboards, and act on insights. In a business where people collaborate across boundaries, ask questions, and share insight, data becomes a competitive asset.
Here it is critical that leaders truly believe in the value and set the tone, and because it rare to have everyone in the business care about the data as passionately as they do about the prime function of their unique role (e.g. designer);
In my first discussion with 
Rob, did you receive any feedback related to part 1? I spoke with a company that emphasized the importance of data quality; however, they were more interested in applying plasters, as they consider a broader approach too disruptive to their current business. Do you see similar situations?
Honestly, not much feedback. Data Governance isn’t as sexy or exciting as discussions on Designing, Engineering, Manufacturing, or PLM Technology. HOWEVER, as the saying goes, all roads lead to Rome, and all Digital Engineering discussions ultimately lead to data.
Designing effective data governance involves tailoring foundational elements, including data stewardship, standards, lineage, metadata, glossaries, and quality rules. These elements must reflect the realities of operations, striking a balance between trade-offs such as speed versus rigor or openness versus control.
AI also offers enormous potential for data quality and governance. From live monitoring to proactive guidance, adopting this approach will become a much easier business strategy. One can imagine AI forming the core of a company’s Digital Thread—no longer requiring rigidly hardwired systems and data flows, but instead intelligently comparing team data and flagging misalignments.
Experts define quality rules (completeness, consistency, relationship integrity), and AI enables automated anomaly detection. Initially, humans triage issues, but over time, as trust in AI grows, more of the process can be automated. Eventually, no oversight may be needed; alerts could be sent directly to those empowered to act, whether human or AI.
While defining quality is one challenge, detecting issues is another. Data quality problems vary in severity and detection difficulty, and their importance can shift depending on the development stage. It’s vital not to prioritize one measure over others, e.g., having timely data doesn’t guarantee that it has been validated.
Data governance typically evolves; it’s challenging to implement from the start. Organizations must understand their operations before they can govern data effectively.
Jos Voskuil's Weblog 
Interesting reflection, Jos. In my experience, the situation you describe is very recognizable. At the company where I work, sustainability…
[…] (The following post from PLM Green Global Alliance cofounder Jos Voskuil first appeared in his European PLM-focused blog HERE.) […]
[…] recent discussions in the PLM ecosystem, including PSC Transition Technologies (EcoPLM), CIMPA PLM services (LCA), and the Design for…
Jos, all interesting and relevant. There are additional elements to be mentioned and Ontologies seem to be one of the…
Jos, as usual, you've provided a buffet of "food for thought". Where do you see AI being trained by a…