The BOM revisited – what are the challenges?

January 28, 2026 in Data centric, Digital Enterprise, digital thread, EBOM, MBOM, PLM | Leave a comment

The last month, it seems like in my ecosystem, people are incredibly focused on “THE BOM” combined with AI agents working around the clock. One of the reasons I have this impression, of course, is my irregular participation in the Future of PLM panel discussions, moderated and organized by Michael Finocharrio.

Yesterday, the continuously growing Future of PLM team held another interesting discussion: “A BOMversation”. You can watch the replay and the comments during the debate here: To BOM or Not to BOM: A BOMversation

 

On the other hand, there is Prof. Jorg Fischer with his provocative post: 📌 2026 – The year we have to unlearn BOMs!  –

Sounds like a dramatic opening, but when you read his post and my post below, you will learn that there is a lot of (conceptual) alignment.

Then there are PLM vendors who announce “next-generation BOM management,” startup companies that promise AI-powered configuration engines, and consultants who explain how the BOM has become the foundation of digital transformation. (I do not think so)

And as Anup Karumanchi states, BOMs can be the reason if production keeps breaking.

I must confess that I also have a strong opinion about the various BOMs and their application in multiple industries.

My 2019 blog post: The importance of EBOM and MBOM is in the top 3 of most-read posts. BOM discussions, single BOM, multiview BOM, etc., always attract an audience.

I continuously observe a big challenge at the companies I am working with – the difference between theory and reality.

If the BOM is so important, why do so many organizations still struggle to make it work across engineering, manufacturing, supply chain, and service?

The answer is two-fold: LEGACY DATA, PROCESSES and PEOPLE, and the understanding that the BOM we are using today was designed for a different industrial reality.

Let me share my experiences, which take longer to digest than an entertaining webinar.

Some BOM history and theory

Historically, the BOM was a production artifact. It described what was needed to build something and in what quantities. When PLM systems emerged, the 3D CAD model structure became the authoritative structure representing product definition, driven mainly by the PLM vendors with dominant 3D CAD tools in their portfolio.

As the various disciplines in the company were not integrated at all, the BOM structure derived from the 3D CAD model was often a simplified way to prepare a BOM for ERP. The transfer to ERP was done manually (retype the structure in ERP), advanced (using Excel export and import with some manipulation) or advanced through an “intelligent” interface.

There are still a lot of companies working this way, probably because, due to the siloed organization, there is no one owning or driving a smooth flow of information in the company.

 

The need for an eBOM and mBOM

When companies become more mature and start to implement a PLM system, they will discover, depending on their core business processes, that it makes sense to split the BOM concept into a specification structure, the eBOM and a manufacturing structure for ERP, the mBOM.

The advantage of this split is that the engineering specification can remain stable over time, as it provides a functional view of the product with its functional assemblies and part definitions.

This definition needs to be resolved and adapted for a specific plant with its local suppliers and resources. PLM systems often support the transformation from the eBOM to a proposed mBOM, and if done more completely with a Bill of Process.


The advantages of a split in an eBOM and an mBOM are:

 

  • Reduced the number of engineering changes when supplier parts change
  • Centralized control of all product IP related to its specifications (eBOM/3DCAD)
  • Efficient support for modularity, as each module has its own lifecycle and can be used in multiple products.

 

Implementing an eBOM/mBOM concept

The theory, the methodology and implementation are clear, and you can ask ChatGPT and others to support you in this step.

However where ChatGPT or service providers often fail is to motivate a company to move to this next steps, as either their legacy data and tools are incompatible (WHY CHANGE?), the future is not understood and feels risky (I DON’T LIKE IT) or for political career reasons a change is blocked (I DON’T LIKE YOU or the HIPPO says differently)

Image inspired by Share PLM and Peter Vind’s messaging

 

Extending to the sBOM

When you sell products in large volumes, like cars or consumer products, companies have discovered and organized a well-established service business, as the margins are high here.

Companies that sell almost unique solutions for customers, batch-size 1 or small series, are also discovering or asked by their customers to come up with service plans and related pricing.

The challenge for these companies is that there is a lot of guesswork to be done, as the service business was not planned in their legacy business. A quick and dirty solution was to use the mBOM in ERP as the source of information. However, the ERP system usually does not provide any context information, such as where the part is located and what potential other parts need to be replaced—a challenging job for service engineers.

A less quick and still a little dirty solution was create a new structure in the PLM system, which provided the service kits and service parts for the defined product, preferably done based on the eBOM, if an eBOM exists.

The ideal solution would be that service engineers are working in parallel and in the same environment as the other engineers, but this requires an organisational change.

 

The organization often becomes the blocker.

As long as the PLM system is considered a tool for engineering, advanced extensions to other disciplines will be hard to achieve.

A linear organization aligned with a traditional release process will have difficulties changing to work with a common PLM backbone that satisfies engineering, manufacturing engineering and service engineering at the same time.

Now, the term PLM becomes Product Lifecycle MANAGEMENT and this brings us to the core issue: the BOM is too often reduced to a parts list without understanding the broader context of the product, needed for service or operation support where artifacts can be hardware and software in a system.

What is really needed is an extended data model with at least a logical product structure that can represent multiple views of the same product: engineering intent, manufacturing reality, service configuration, software composition, and operational context. These views should not be separate silos connected by fragile integrations. They should be derived from a shared, consistent digital infrastructure – this is what I extract from Prof. Jorg Fischer’s post, be it that he comes with a strong SAP background and focus on CTO+ 

Most companies are still organized around linear processes with a focus on mechanical products: engineering hands over to manufacturing, manufacturing hands over to service, and feedback loops are weak or nonexistent.

Changing the BOM without changing the organization is like repainting a house with structural cracks. It may look better, but the underlying issues remain.
Listen to this snippet from the BOMversation where Patrick Hilberg touches this point too.

With this approach, the digital thread becomes more than a buzzword. A digital thread must provide digital continuity, which means that changes propagate across domains, that data is contextualized, and that lifecycle feedback flows back into product development. Without this continuity, digital twins concepts remain isolated models rather than living representations of real products.

However, the most significant barrier is not technical. It is organizational. There is an interesting parallel with how we address climate change and are willing to take action against it.

For decades, we have known what needs to change. The science is precise. The solutions exist. Yet progress is slow because transformation requires breaking established habits, business models, and power structures.

Digital transformation in product lifecycle management follows a similar pattern. Everyone agrees that data silos are a problem. Everyone wants “end-to-end visibility.” Yet few organizations are willing to rethink ownership of product data and processes fundamentally.

So what does the future BOM look like?

It is not a single hierarchical tree. It is part of a maze; some will say it is a graph. It is a connected network of product-related information: physical components, software artifacts, service elements, configurations, requirements, and operational data. It supports multiple synchronized views without duplicating information. It evolves as products change when operated in the field.

The evolving understanding of the BOM and its context

Most importantly, it is not owned by one department. It becomes a shared enterprise asset – with shared accountability for various datasets. But we should not abandon the BOM concept. On the contrary, the BOM remains essential and managing BOMs consistently is already a challenge.

But its role must shift from being a collection of static structures to becoming part of the digital product definition infrastructure, extended by a logical product structure and beyond – the MBSE question.

The BOM is not dead. But the traditional BOM mindset is no longer sufficient. The question is not whether the BOM will change. It already is. The real question is whether organizations are ready to change with it.

Conclusion

Inspired by various BOMversations and AI graphical support,  I tried to reflect the business reality, observed for over 10++ years. Technology and the Academic truth do not create breakthroughs in organisations due to the big legacy and fear of failure. Will AI fix this gap, as many software vendors believe, or do we need a new generation with no legacy PLM experience, as some others suggest? Your thoughts?

p.s. My trick to join the BOMversation without being thrown from the balcony 🙃

 

 

2026 has started – What to say?

January 19, 2026 in PLM | Leave a comment

This post is for the 1,564 members of the PLM Green Global Alliance LinkedIn group—and for those wondering why they should join.

Since October 2019, when Rich McFall launched the group alongside the PLM Green Global Alliance website, we’ve been building a shared space to consolidate insights, discussions, and lessons learned.

Today, the core team—Jos Voskuil, Klaus Brettschneider, Mark Reisig, Evgeniya Burimskaya, and Erik Rieger—continues to contribute deep expertise across sustainability, LCA, energy, circular economy, and design.

In November 2025, we marked our fifth anniversary with a webinar reflecting on past learnings and looking ahead to 2026. The conversation is still worth revisiting.

Now, as 2026 unfolds, the mood may feel mixed. Progress is never automatic. There are ups, downs, and work to be done.

 

Rich McFall stepping back!

In early December, it became clear that Rich would no longer be able to support the PGGA for personal reasons. We respect his decision and thank Rich for the energy and private money he has put into setting up the website, pushing the moderators to remain active and publishing the newsletter every month. From the frequency of the newsletter over the last year, you might have noticed Rich struggled to be active.

Rich, we all wish you a happy and healthy continuation of your life and hope we will see you back once in a while as a contributor.

Meanwhile, Klaus Brettschneider is working on taking over and upgrading the PGGA website to bring our mission to the next level.

 

PGGA – the challenge is in the name

When you launch a product or start an alliance, the name can be excellent at the start, but later it might work against you. I believe we are facing this situation too with our PGGA (PLM Green Global Alliance)

Let’s analyze the name.

 

The P stands for PLM.

Whether a business delivers products or services, most of the environmental impact is locked in during the design phase—often quoted at close to 80%. That makes design a strategic responsibility not only for engineering.

Any company with a long-term vision—call it sustainable in the broad sense—must understand the environmental footprint of its offerings across manufacturing, operation, and repurposing.

Today’s “we don’t care” wave may be loud and egoistic, but it lacks durability. Forward-looking countries and companies already know this.

Moving toward a circular, fossil-free economy demands innovation. In that future, waste and recycling are costly options, pushed out by regulation and resource scarcity alike. Better to change your product or service delivery to align with a circular economy where possible. You can read more in my 2024 post: The Product Service System and a Circular Economy

Changing business models only possible by changing your PLM approach is often overlooked at the board level. PLM as a strategy shapes outcomes—and ultimately drives business success.

Therefore, the P remains our primary focus area.

 

The G for Green

Green has gradually acquired a negative connotation, weakened by early marketing hype and repeated greenwashing exposures. For many, green has lost its attractiveness.

After the 2015 United Nations Climate Change Conference, countries began exploring how to contribute to a healthier planet, increasingly at risk from rising carbon emissions caused by fossil fuels. This momentum led Europe, in 2020, to launch the Green Deal.

The spirit was right, but as with every new direction, failures followed. Lessons were learned, and perhaps too much academic work was done without involving businesses or the populations affected.

It resembles old, failing PLM projects, where management launches a big-bang implementation without grasping the friction and complexity of a fundamental transformation. Behavioral change, as always, is hard, as the iconic Share PLM image illustrates – click on the image for the details.

Meanwhile, threatened industries stirred resistance, science came under pressure, and a brown movement began pushing the green one aside.

The good news is that sustainability is still alive, progressing quietly. Long-term sustainability means reducing dependence on fossil fuels, while a new battle emerges over scarce materials for the energy transition. Those who understand this are already acting—now calling it future risk avoidance.

Green has gone, replaced by proactive business sustainability.

 

The G from Global

When reading or listening to the news, it seems that globalization is over and imperialism is back with a primary focus on economic control. For some countries, this means even control over people’s information and thoughts, by restricting access to information, deleting scientific data and meanwhile dividing humanity into good and bad people.

And while these countries might control 25 % of their population, the majority of people still have the opportunity to fight climate change within their reach, work on a more sustainable economy and world.

As a global community, we have to fight and ensure that what we do is based on science and sharing facts and findings is crucial. As a coincidence, while writing this post, this article from my fellow Dutchman Robert Dijkgraaf, who is a leader in research and policy in many roles, including as minister of Education, Culture, and Science of the Netherlands (2022-2024) and director of the Institute for Advanced Study in Princeton (2012-2022).

His article: The fight to keep science global fits the mindset of our PGGA – take the time to read it and reflect on it.

Global remains our goal, and given the global population, we are looking for more voices from Asia and Africa.

 

The A from Alliance

With more than 1500 members in our LinkedIn group, we are curious why you joined the PLM Global Green Alliance and assuming your positive intent to contribute to a sustainable future, where can we help you best?

There are the contributions from the leading PLM software vendors (PTC, Dassault Systèmes, SAP, Siemens, Aras, Autodesk), solution providers(Sustaira, Makersite, aPriori, Direktin) and service partners (CIMPA, ecoPLM, CIMdata)  that focus on providing capabilities, methodologies and consultancy to support the transition to a more sustainable economy and bearable climate.

 

Let us know your questions, struggles, and intentions, either through personalized or anonymized interactions on LinkedIn – together we are an alliance.

 

Conclusion

Although the political climate is currently in the same condition as the planetary climate, there is still work to be done. Being an observer is the worst option, and changing the world as an individual is also a mission impossible.

However, with our niche group, The PLM Green Global Alliance, we can be part of the generation that actively pursues a sustainable future for ourselves and the next generations.

And as you can learn from this podcast, you are not alone.

 

 

AI for PLM – a blessing or a soap bubble in 2026?

December 16, 2025 in Artificial Intelligence, Brain, Change, Circulair Economy, Data centric, Digital Enterprise, Education, Human Centric, intelligence, Knowledge Management, PLM, Social Media | Leave a comment

December is the last month when daylight is getting shorter in the Netherlands, and with the end of the year approaching, this is the time to reflect on 2025.

For me, it has been an interesting year, and I hope it has been similar for you. I started 2025 with this post: My 2025 focus, sharing the topics that would drive my primary intentions—a quick walk through some of these topics and what to reflect on what I have learned.

 

Fewer blog posts

It was already clear that AI-generated content was going to drown the blogging space. The result: Original content became less and less visible, and a self-reinforcing amount of general messages reduced further excitement.

As I have no commercial drive to be visible, I will continue to write posts only when relevant to personal situations or ideas, with the intention of being shared and discussed with the readers of my posts – approximate 26 / year.

Therefore, if you are still interested in content that has not been generated with AI,  I recommend subscribing to my blog and interacting directly with me through the comments, either on LinkedIn or via a direct message.

 

More podcast recordings

Together with the Share PLM podcast team, Beatriz Gonzales and Maria Morris, we enjoyed talking with a large variety of people active in PLM, all having their personal stories related to PLM to share—each episode ending with an experience to share and a desired takeaway for the listeners. We did it with great pleasure and learned from each episode.

You can find all the recordings from 2025 (Season 3) here.

In Season 4, we want to add the C-level perspective to our PLM and People podcast discussions.

 

#DataCentric or #PeopleCentric ?

It was PeopleCentric first at the beginning of the year, with the Share PLM Summit in Jerez and DataCentric in the second half of the year, with activities connected to the PLM Roadmap/PDT Europe conference in Paris.

In case you missed the excitement and lessons learned, here they are:

Both topics will become even more critical due to the impact of AI tools on our day-to-day work.

 

Sustainability?

Already an uncomfortable term for some of us at the beginning of 2025, it has become one of the best-kept secrets of 2025. Where traditional countries and companies revert to their short-term bad habits – optimize shareholders value, there are also forward-looking enterprises that are actively rephrasing their sustainable strategies as risk mitigation strategies with the awareness that adaptation is inevitable. Better start early than too late – not a typical human strategy.

In case you are interested, I recommend you read and listen to:

 

And now it is time to discuss AI.

With all the investments and marketing related to AI, it is unavoidable to neglect it. For sure, it is a hype, but I believe that we are into something revolutionary for society, like the impact of the industrial revolution on our society 150 years ago.

However, there are also the same symptoms of the .com-hype 25 years ago.

Who are going to be the winners? Currently, the hardware, datacenter and energy providers, not the AI-solution providers. But this can change.

Let’s look into some of the potential benefits.

 

Individual efficiency?

Many of the current AI tools allow individuals to perform better at first sight. Suddenly, someone who could not write understandable (email) messages, draw images or create structured presentations now has a better connection with others—the question to ask is whether these improved efficiencies will also result in business benefits for an organization.

Looking back at the introduction of email with Lotus Notes, for example, email repositories became information siloes and did not really improve the intellectual behavior of people.

Later, Microsoft took over the dominant role as the office software provider with enhanced search and storage capabilities, but still, most of the individual knowledge remained hidden or inaccurate as it missed the proper context.

As a result of this, some companies tried to reduce the usage of individual emails and work more and more in communities with a specific context. Also, due to COVID and improved connectivity, this led to the success of Teams. And now with Copilot embedded in the Microsoft suite, I am curious to learn what companies perceive as measurable business benefits.

The chatbot?

For many companies, the chatbot is a way to reduce the number of people active in customer relations, either sales or services. I believe that, combined with the usage of LLMs, an improvement in customer service can be achieved. Or at least the perception, as so far I do not recall any interaction with a chatbot to be specific enough to solve my problem.

 

The risks with AI?

Now I may sound like a boomer who started focusing on knowledge management 25 years ago – exploring tacit knowledge.

Tacit knowledge is the knowledge a real expert has by combining different areas of expertise and understanding what makes sense.

Could tacit knowledge be replaced by an external model that gives you all the (correct?) answers?

In verifiable situations, we know when the model is hallucinating – but what if the scope is beyond our understanding? Would we still rely on AI, and could AI be manipulated in ways that we lose touch with the real facts?

Already, the first research papers are coming out warning of reduced human cognitive performance, e.g., this paper: Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning, Motivation, Processes, and Performance.

Combined with laziness (a typical human behavior – system 1), these results made me think of a statement made by  Sean Illing:

“People love the truth, but they hate facts.”

A statement highly relevant to what we see happening now with social media – we do not think or research deep enough anymore, we select the facts that we like and consider them our truth.

 

What happens in our PLM domain?

In the PLM domain, companies are indeed reluctant to use LLMs directly, where some of them use RAG (Retrieval-Augmented Generation) to feed the LLM with a relevant context.

Still, the answers require human interpretation, as you cannot avoid hallucinations in your product lifecycle management processes.

As long as the results are based on inconsistent data sources that lack the relevant context, the answers are of low quality.

Meanwhile, every vendor in the PLM space is now offering AI-agents, most of the time within their own portfolio space. The ultimate dream is polygot agents (who are buying them / who are developing them) that can work together and create a new type of agility beyond traditional workflows. An interesting article in this context comes from Oleg Shilovitsky: Why Does PLM Need Task Re-Engineering Before It Can Have AI?

Still, these potential “quick” fixes create a risk for companies in the long term. Buying AI tools does not fix the foundation that is based on legacy.

In particular, related to the Shape the Future of PLM – Together workshop in Paris on Nov 4th, the consensus was that companies need to invest in understanding and implementing domain-specific ontologies and semantic models to provide a data-driven infrastructure that allows AI to make accurate decisions or valid recommendations.

You can read the summary of the event and recommendations here: Accelerating the Future of PLM & ALM on the ArrowHead’s website.

You can also read this post from Ole Olesen-Bagneux: Why will 2026 be the year of the ontologist?

Although the topics in the workshop might look “too advanced” for your company, they are crucial to transform into a long-term, sustainable, data-driven, model-based, and AI-supported enterprise.

Somewhere, you have to cross the chasm from documents to data in context.

Being busy is not an excuse, as you can also read in Thomas Nys’s LinkedIn post: Your Engineers spend 40 % of their time maintaining yesterday’s shortcuts. And you’re wondering why your AI initiative isn’t moving faster. I loved the image.

 

Human Resources?

The AI revolution will have an impact on society, and it is up to us individuals how well we adapt.

Remember, the first 50 – 100 years of the Industrial Revolution made only a few people extremely rich. James Watt, the Rothschild family, Andrew Carnegie, John D. Rockefeller, Cornelius Vanderbilt, J.P. Morgan, Alfred Krupp and the Schneider family became so rich due to ownership of factories and machinery, the control of raw materials (coal, iron, oil), the use of new technology (steam power, mechanization) combined with access to cheap labor and weak labor laws and limited competition early on.

Most humans moved into urbanized areas to become nothing but cheap resources, even children. And remember, many of us are still human resources!

A new conspiracy?

In 2016, Ida Auken’s lecture at the WEF created traction during COVID among people who believed in conspiracies. Her story focused on a more circular economy with respect for the Earth’s resources. The story was framed into the message:

“In the future, you will own nothing and be happy.”

The conspiracy theorist believed all their possessions would be taken away by the elite in the long term.

I want to conclude with a new message for these conspiracy theorists active on X or other discussion fora:

“In the future, you will know nothing, and you won’t be aware enough to care.”

 

Conclusion

2026 is going to be an interesting year, where we cannot allow ourselves to sit still and watch what is happening. Active participation is more challenging but also more rewarding than being a consumer. In May 2026, I hope to meet some of you at the Share PLM Summit in Jerez and share the human side, followed by the PDM Roadmap/PDT Europe conference in Q4 in Gothenburg, where we will catch up on the technical and data side.

I am wishing you all a wise and happy/healthy 2026

 Link to the article with comments on LinkedIn

The “EASY” part of PLM Roadmap / PDT Europe 2025

November 24, 2025 in Artificial Intelligence, Brain, Change, Data centric, Digital Enterprise, Human Centric, MBD, model-based, PLM, Storytelling | Leave a comment

Last week, I wrote about the first day of the crowded PLM Roadmap/PDT Europe conference.

You can still read my post here in case you missed it: A very long week after PLM Roadmap / PDT Europe 2025

 

My conclusion from that post was that day 1 was a challenging day if you are a newbie in the domain of PLM and data-driven practices. We discussed and learned about relevant standards that support a digital enterprise, as well as the need for ontologies and semantic models to give data meaning and serve as a foundation for potential AI tools and use cases.

This post will focus on the other aspects of product lifecycle management – the evolving methodologies and the human side.

Note: I try to avoid the abbreviation PLM, as many of us in the field associate PLM with a system, where, for me, the system is more of an IT solution, where the strategy and practices are best named as product lifecycle management.

And as a reminder, I used the image above in other conversations. Every company does product lifecycle management; only the number of people, their processes, or their tools might differ. As Peter Billelo mentioned in his opening speech, the products are why the company exists.

 

Unlocking Efficiency with Model-Based Definition

Day 2 started energetically with Dennys Gomes‘ keynote, which introduced model-based definition (MBD) at Vestas, a world-leading OEM for wind turbines.

Personally, I consider MBD as one of the stepping stones to learning and mastering a model-based enterprise, although do not be confused by the term “model”. In MBD, we use the 3D CAD model as the source to manage and support a data-driven connection among engineering, manufacturing, and suppliers. The business benefits are clear, as reported by companies that follow this approach.

However, it also involves changes in technology, methodology, skills, and even contractual relations.

Dennys started sharing the analysis they conducted on the amount of information in current manufacturing drawings. The image below shows that only the green marker information was used, so the time and effort spent creating the drawings were wasted.

It was an opportunity to explore model-based definition, and the team ran several pilots to learn how to handle MBD, improve their skills, methodologies, and tool usage. As mentioned before, it is a profound change to move from coordinated to connected ways of working; it does not happen by simply installing a new tool.

The image above shows the learning phases and the ultimate benefits accomplished. Besides moving to a model-based definition of the information, Dennys mentioned they used the opportunity to simplify and automate the generation of the information.

Vestas is on a clear path, and it is interesting to see their ambition in the MBD roadmap below.

An inspirational story, hopefully motivating other companies to make this first step to a model-based enterprise. Perhaps difficult at the beginning from the people’s perspective, but as a business, it is a profitable and required direction.

 

Bridging The Gap Between IT and Business

It was a great pleasure to listen again to Peter Vind from Siemens Energy, who first explained to the audience how to position the role of an enterprise architect in a company compared to society. He mentioned he has to deal with the unicorns at the C-level, who, like politicians in a city, sometimes have the most “innovative” ideas – can they be realized?

To answer these questions, Peter is referring to the Business Capability Model (BCM) he uses as an Enterprise Architect.

Business Capabilities define ‘what’ a company needs to do to execute its strategy, are structured into logical clusters, and should be the foundation for the enterprise, on which both IT and business can come to a common approach.

The detailed image above is worth studying if you are interested in the levels and the mappings of the capabilities. The BCM approach was beneficial when the company became disconnected from Siemens AG, enabling it to rationalize its application portfolio.

Next, Peter zoomed in on some of the examples of how a BCM and structured application portfolio management can help to rationalize the AI hype/demand – where is it applicable, where does AI have impact – and as he illustrated, it is not that simple. With the BCM, you have a base for further analysis.

Other future-relevant topics he shared included how to address the introduction of the digital product passport and how the BCM methodology supports the shift in business models toward a modern “Power-as-a-Service” model.

He concludes that having a Business Capability Model gives you a stable foundation for managing your enterprise architecture now and into the future. The BCM complements other methodologies that connect business strategy to (IT) execution. See also my 2024  post: Don’t use the P** word! – 5 lessons learned.

 

Holistic PLM in Action.

or companies struggling with their digital transformation in the PLM domain, Andreas Wank, Head of Smart Innovation at Pepperl+Fuchs SE, shared his journey so far. All the essential aspects of such a transformation were mentioned. Pepperl+Fuchs has a portfolio of approximately 15,000 products that combine hardware and software.

It started with the WHY. With such a massive portfolio, business innovation is under pressure without a PLM infrastructure. Too many changes, fragmented data, no single source of truth, and siloed ways of working lead to much rework, errors, and iterations that keep the company busy while missing the global value drivers.

Next, the journey!

The above image is an excellent way to communicate the why, what, and how to a broader audience. All the main messages are in the image, which helps people align with them.

The first phase of the project, creating digital continuity, is also an excellent example of digital transformation in traditional document-driven enterprises. From files to data align with the From Coordinated To Connected theme.

Next, the focus was to describe these new ways of working with all stakeholders involved before starting the selection and implementation of PLM tools. This approach is so crucial, as one of my big lessons learned from the past is: “Never start a PLM implementation in R&D.”

If you start in R&D, the priority shifts away from the easy flow of data between all stakeholders; it becomes an R&D System that others will have to live with.

You never get a second, first impression!

Pepperl+Fuchs spends a long time validating its PLM selection – something you might only see in privately owned companies that are not driven by shareholder demands, but take the time to prepare and understand their next move.

As Andreas also explained, it is not only about the functional processes. As the image shows, migration (often the elephant in the room) and integration with the other enterprise systems also need to be considered. And all of this is combined with managing the transition and the necessary organizational change.

Andreas shared some best practices illustrating the focus on the transition and human aspects. They have implemented a regular survey to measure the PLM mood in the company. And when the mood went radical down on Sept 24, from 4.1 to 2.8 on a scale of 1 to 5, it was time to act.

They used one week at a separate location, where 30 of his colleagues worked on the reported issues in one room, leading to 70 decisions that week. And the result was measurable, as shown in the image below.

Andreas’s story was such a perfect fit for the discussions we have in the Share PLM podcast series that we asked him to tell it in more detail, also for those who have missed it. Subscribe and stay tuned for the podcast, coming soon.

 

Trust, Small Changes, and Transformation.

Ashwath Sooriyanarayanan and Sofia Lindgren, both active at the corporate level in the PLM domain at Assa Abloy, came with an interesting story about their PLM lessons learned.

To understand their story, it is essential to comprehend Assa Abloy as a special company, as the image below explains. With over 1000 sites, 200 production facilities, and, last year, on average every two weeks, a new acquisition, it is hard to standardize the company, driven by a corporate organization.

However, this was precisely what Assa Abloy has been trying to do over the past few years. Working towards a single PLM system, with generic processes for all, spending a lot of time integrating and migrating data from the different entities became a mission impossible.

To increase user acceptance, they fell into the trap of customizing the system ever more to meet many user demands. A dead end, as many other companies have probably experienced similarly.

And then they came with a strategic shift. Instead of holding on to the past and the money invested in technology, they shifted to the human side.

The PLM group became a trusted organisation supporting the individual entities. Instead of telling them what to do (Top-Down), they talked with the local business and provided standardized PLM knowledge and capabilities where needed (Bottom-Up).

This “modular” approach made the PLM group the trusted partner of the individual business. A unique approach, making us realize that the human aspect remains part of implementing PLM

Humans cannot be transformed

Given the length of this blog post, I will not spend too much text on my closing presentation at the conference. After a technical start on DAY 1, we gradually moved to broader, human-related topics in the latter part.

You can find my presentation here on SlideShare as usual, and perhaps the best summary from my session was given in this post from Paul Comis. Enjoy his conclusion.

 

Conclusion

Two and a half intensive days in Paris again at the PLM Roadmap / PDT Europe conference, where some of the crucial aspects of PLM were shared in detail. The value of the conference lies in the stories and discussions with the participants. Only slides do not provide enough education. You need to be curious and active to discover the best perspective.

For those celebrating: Wishing you a wonderful Thanksgiving!

 

 

 

 

A very long week after PLM Roadmap / PDT Europe 2025

November 19, 2025 in Artificial Intelligence, Asset Lifecycle Management, Cloud, Data centric, Digital Enterprise, digital thread, Digital Twin, MBSE, PLM, Standards | Leave a comment

For those of you following my blog over the years, there is, every time after the PLM Roadmap PDT Europe conference, one or two blog posts, where the first starts with “The weekend after ….”

This time, November has been a hectic week for me, with first this engaging workshop “Shape the future of PLM – together” – you can read about it in my blog post or the latest post from Arrowhead fPVN, the sponsor of the workshop.

 

Last week, I celebrated with the core team from the PLM Green Global Alliance our 5th anniversary, during which we discussed sustainability in action. The term sustainability is currently under the radar, but if you want to learn what is happening, read this post with a link to the webinar recording.

Last week, I was also active at the PTC/User Benelux conference, where I had many interesting discussions about PTC’s strategy and portfolio. A big and well-organized event in the town where I grew up in the world of teaching and data management.

And now it is time for the PLM roadmap / PDT conference review

The conference

The conference is my favorite technical conference 😉 for learning what is happening in the field. Over the years, we have seen reports from the Aerospace & Defense PLM Action Groups, which systematically work on various themes related to a digital enterprise. The usage of standards, MBSE, Supplier Collaboration, Digital Thread & Digital Twin are all topics discussed.

This time, the conference was sold out with 150+ attendees, just fitting in the conference space, and the two-day program started with a challenging day 1 of advanced topics, and on day 2 we saw more company experiences.

Combined with the traditional dinner in the middle, it was again a great networking event to charge the brain. We still need the brain besides AI. Some of the highlights of day 1 in this post.

 

 

PLM’s Integral Role in Digital Transformation

As usual, Peter Bilello, CIMdata’s President & CEO, kicked off the conference, and his message has not changed over the years. PLM should be understood as a strategic, enterprise-wide approach that manages intellectual assets and connects the entire product lifecycle.

I like the image below explaining the WHY behind product lifecycle management.

It enables end-to-end digitalization, supports digital threads and twins, and provides the backbone for data governance, analytics, AI, and skills transformation.

Peter walked us briefly through CIMdata’s Critical Dozen (a YouTube recording is available here), all of which are relevant to the scope of digital transformation. Without strong PLM foundations and governance, digital transformation efforts will fail.

 

The Digital Thread as the Foundation of the Omniverse

Prof. Dr.-Ing. Martin Eigner, well known for his lifetime passion and vision in product lifecycle management (PDM and PLM tools & methodology), shared insights from his 40-year journey, highlighting the growing complexity and ever-increasing fragmentation of customer solution landscapes.

In his current eco-system, ERP (read SAP) is playing a significant role as an execution platform, complemented by PDM or ECTR capabilities. Few of his customers go for the broad PLM systems, and therefore, he stresses the importance of the so-called Extended Digital Thread.

Prof Eigner describes the EDT more precisely as an overlaying infrastructure implemented by a graph database that serves as a performant knowledge graph of the enterprise.

The EDT serves as the foundation for AI-driven applications, supporting impact analysis, change management, and natural-language interaction with product data. The presentation also provides a detailed view of Digital Twin concepts, ranging from component to system and process twins, and demonstrates how twins enhance predictive maintenance, sustainability, and process optimization.

Combined with the  NVIDIA Omniverse as the next step toward immersive, real-time collaboration and simulation, enabling virtual factories and physics-accurate visualization. The outlook emphasizes that combining EDT, Digital Twin, AI, and Omniverse moves the industry closer to the original PLM vision: a unified, consistent Single Source of Truth 😮that boosts innovation, efficiency, and ROI.

For me, hearing and reading the term Single Source of Truth still creates discomfort with reality and humanity, so we still have something to discuss.

 

Semantic Digital Thread for Enhanced Systems Engineering in a Federated PLM Landscape

Dr. Yousef Hooshmand‘s presentation was a great continuation of the Extended Digital Thread theme discussed by Dr. Martin Eigner. Where the core of Martin’s EDT is based on traceability between artifacts and processes throughout the lifecycle, Yousef introduced a (for me) totally new concept: starting with managing and structuring the data to manage the knowledge, rather than starting from the models and tools to understand the knowledge.

It is a fundamentally different approach to addressing the same problem of complexity. During our pre-conference workshop “Shape the future of PLM – together,” I already got a bit familiar with this approach, and Yousef’s recently released paper provides all the details.

All the relevant information can be found in his recent LinkedIn post here.

In his presentation during the conference, Yousef illustrated the value and applicability of the Semantic Digital Thread approach by presenting it in an automotive use case:  Impact Analysis and Cost Estimation (image above)

To understand the Semantic Digital Thread, it is essential to understand the Semantic Data Model and its building blocks or layers, as illustrated in the image below:

In addition, such an infrastructure is ideal for AI applications and avoids vendor- or tool lock-in, providing a significant long-term advantage.

I am sure it will take time for us to digest the content if you are entering the domain of a data-driven enterprise (the connected approach) instead of a document-driven enterprise (the coordinated approach).

However, as many of the other presentations on day 1 also stated: “data without context is worthless – then they become just bits and bytes.” For advanced and future scenarios, you cannot avoid working with ontologies, semantic models and graph databases.

Where is your company on the path to becoming more data-driven?

Note: I just saw this post and the image above, which emphasizes the importance of the relationship between ontologies and the application of AI agents.

 

Evaluation of SysML v2 for use in Collaborative MBSE between OEMs and Suppliers

It was interesting to hear Chris Watkins’ speech, which presented the findings from the AD PLM Action Group MBSE Collaboration Working Group on digital collaboration based on SysML v2.

The topic they research is that currently there are no common methods and standards for exchanging digital model-based requirements and architecture deliverables for the design, procurement, and acceptance of aerospace systems equipment across the industry.

The action group explored the value of SysML v2 for data-driven collaboration between OEMs and suppliers, particularly in the early concept phases.

Chris started with a brief explanation of what SysXML v2 is – image below:

As the image illustrates, SysML v2-ready tools allow people to work in their proprietary interfaces while sharing results in common, defined structures and ontologies.

When analyzing various collaboration scenarios, one of the main challenges remained managing changes, the required ontologies, and working in a shared IT environment.

👉You can read the full report here: AD PAG reports: Model-Based Systems Engineering.

An interesting point of discussion here is that, in the report, participants note that, despite calling out significant gaps and concerns, a substantial majority of the industry indicated that their MBSE solution provider is a good partner. At the same time, only a small minority expressed a negative view.

Would Data-Centric Systems Engineering change the discussion? See table 1 below from Yousef’s paper:

An illustration that there was enough food for discussion during the conference.

 

PLM Interoperability and the Untapped Value of 40 Years in Standardization

In the context of collaboration, two sessions fit together perfectly.

First, Kenny Swope from Boeing. Kenny is a longtime Boeing engineering leader and global industrial-data standards expert who oversees enterprise interoperability efforts, chairs ISO/TC 184/SC 4, and mentors youth in technology through 4-H and FIRST programs.

Kenny shared that over the past 40+ years, the understanding and value of this approach have become increasingly apparent, especially as organizations move toward a digital enterprise. In a digital enterprise, these standards are needed for efficient interoperability between various stakeholders. And the next session was an example of this.

 

Unlocking Enterprise Knowledge

Fredrik Anthonisen, the CTO of the POSC Caesar Association (PCA), started his story about the potential value of efficient standard use.

According to a Siemens report, “The true costs of downtime” a  $1,4 trillion is lost to unplanned downtime.

The root cause is that, most of the time, the information needed to support the MRO activity is inaccessible or incomplete.

Making data available using standards can provide part of the answer, but static documents and slow consensus processes can’t keep up with the pace of change.

Therefore, PCA established the PCA enterprise reference data cloud, where all stakeholders in enterprise collaboration can relate their data to digital exposed standards, as the left side of the image shows.

Fredrik shared a use case (on the right side of the image) as an example. Also, he mentioned that the process for defining and making the digital reference data available to participants is ongoing. The reference data needs to become the trusted resource for the participants to monetize the benefits.

Summary

Day 1 had many more interesting and advanced concepts related to standards and the potential usage of AI.

Jean-Charles Leclerc, Head of Innovation & Standards at TotalEnergies, in his session, “Bringing Meaning Back To Data,” elaborated on the need to provide data in the context of the domain for which it is intended, rather than “indexed” LLM data.

Very much aligned with Yousef’s statement that there is a need to apply semantic technologies, and especially ontologies, to turn the data into knowledge.

More details can also be found in the “Shape the future of PLM – together” post, where Jean-Charles was one of the leading voices.

The panel discussion at the end of day 1 was free of people jumping on the hype. Yes, benefits are envisioned across the product lifecycle management domain, but to be valuable, the foundation needs to be more structured than it has been in the past.

“Reliable AI comes from a foundation that supports knowledge in its domain context.”

 

 

Conclusion

For the casual user, day 1 was tough – digital transformation in the product lifecycle domain requires skills that might not yet exist in smaller organizations. Understanding the need for ontologies (generic/domain-specific) and semantic models is essential to benefit from what AI can bring – a challenging and enjoyable journey to follow!

 

Five years PLM Green Global Alliance – discussing the trends and future directions

November 17, 2025 in Circulair Economy, Climate Change, Design for Sustainabillity, Green Development, Green Energy, Life Cycle Analysis, Life Cycle Assessment, PLM, Sustainability | Leave a comment

On November 11th, we celebrated our 5th anniversary of the PLM Green Global Alliance (PGGA) with a webinar where ♻️ Jos Voskuil (me) interviewed the five other PGGA core team members about developments and experiences in their focus domain, potentially allowing for a broader discussion.

In our discussion, we focused on the trends and future directions of the PLM Green Global Alliance, emphasizing the intersection of Product Lifecycle Management (PLM) and sustainability.

Probably, November 11th was not the best day for broad attendance, and therefore, we hope that the recording of this webinar will allow you to connect and comment on this post.

Enjoy the discussion – watch it, or listen to it, as this time we did not share any visuals in the debate. Still, we hope to get your reflections and feedback on the interview related to the LinkedIn post.

The discussion centered on the trends and future directions of the PLM Green Global Alliance, with a focus on the intersection of Product Lifecycle Management (PLM) and sustainability.

 

Short Summary

♻️ Rich McFall shared his motivations for founding the alliance, highlighting the need for a platform that connects individuals committed to sustainability and addresses the previously limited discourse on PLM’s role in promoting environmental responsibility. He noted a significant variance in vendor engagement with sustainability, indicating that while some companies are proactive, others remain hesitant.

The conversation delved into the growing awareness and capabilities of how to perform a Life Cycle Assessment (LCA) with ♻️ Klaus Brettschneider, followed by the importance of integrating sustainability into PLM strategies, with ♻️ Mark Reisig discussing the ongoing energy transition and the growing investments in green technologies, particularly in China and Europe.

♻️ Evgeniya Burimskaya raised concerns about implementing circular economy principles in the aerospace industry, emphasizing the necessity of lifecycle analysis and the upcoming digital product passport requirements. The dialogue also touched on the Design for Sustainability initiative, led by ♻️ Erik Rieger, which aims to embed sustainability into the product design phase, necessitating a cultural shift in engineering education to prioritize sustainability.

Conclusion

We concluded with understanding the urgent realities of climate change, but also advocating for an optimistic mindset in the face of challenges – it is perhaps not as bad as it seems in the new media. There are significant investments in green energy, serving as a beacon of hope, which encourage people to remain committed to collaborative efforts in advancing sustainable practices.

We agreed on the long-term nature of behavioral change within organizations and the role of the Green Alliance in fostering this transformation, concluding with a positive outlook on the potential for future generations to drive necessary changes in sustainability.

A week after Shaping the Future of PLM – No time to lose!

November 12, 2025 in Artificial Intelligence, Asset Lifecycle Management, Data centric, Digital Enterprise, Digital Twin, ISO 15926, Knowledge Management, Multidisciplinary, PLM, Standards | Leave a comment

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:

  1. Traditional PLM architectures struggle with the fundamental challenge of managing increasingly complex relationships between product data, process information, and enterprise systems.
  2. 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.

👉 from ArchNova consulting – you can find her full comment in this LinkedIn post.

Today’s discussions provided valuable insights into how the PLM language is evolving in the AI era and how we can better bridge the gap between business and technology to achieve fundamental transformation.
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. 

These infrastructures are necessary to implement AI meaningfully, which extends beyond data retrieval to managing and exploring knowledge. Due to the limited time we had in this workshop, we were not able to explore other dimensions of this transformation
For the participants of the workshop – stay tuned and we will send you the final conclusions

PLM and Sustainability: talking with Direktin

November 3, 2025 in Data centric, Design for Sustainabillity, Digital Twin, Green Energy, Life Cycle Assessment, Sustainability | Tags: , , | Leave a comment

Recently, we initiated the Design for Sustainability workgroup, an initiative from two of our PGGA members, Erik Rieger and Matthew Sullivan. You can find a recording of the kick-off here on our YouTube channel.

Thanks to the launch of the Design for Sustainability workgroup, we were introduced to Dr. Elvira Rakova, founder and CEO of the startup company Direktin.

Her mission is to build the Digital Ecosystem of engineering tools and simulation for Compressed Air Systems.  As typical PLM professionals with a focus on product design, we were curious to learn about developments in the manufacturing space. And it was an interesting discussion, almost a lecture.

Compressed air and Direktin

Dr. Elvira Rakova has been working with compressed air in manufacturing plants for several years, during which she has observed the inefficiency of how compressed air is utilized in these facilities. It is an available resource for all kinds of machines in the plant, often overdimensioned and a significant source of wasted energy.

To address this waste of energy, linked to CO2 emissions, she started her company to help companies scale, dimension, and analyse their compressed air usage. A mix of software and consultancy to make manufacturing processes using compressed air responsible for less carbon emissions, and for the plant owners, saving significant money related to energy usage.

For us, it was an educational discussion, and we recommend that you watch or listen to the next 36 minutes

What I learned

  • The use of compressed air and its energy/environmental impact were like dark matter to me.
    I never noticed it when visiting customers as a significant source to become more sustainable.
  • Although the topic of compressed air seems easy to understand, its usage and impact are all tough to address quickly and easily, due to legacy in plants, lack of visibility on compressed air (energy usage) and needs and standardization among the providers of machinery.
  • The need for data analysis is crucial in addressing the reporting challenges of Scope 3 emissions, and it is also increasingly important as part of the Digital Product Passport data to be provided. Companies must invest in the digitalization of their plants to better analyze and improve energy usage, such as in the case of compressed air.
  • In the end, we concluded that for sustainability, it is all about digital partnerships connecting the design world and the manufacturing world and for that reason, Elvira is personally motivated to join and support the Design for Sustainability workgroup

Want to learn more?

Conclusions

 The PLM Green Global Alliance is not only about designing products; we have also seen lifecycle assessments for manufacturing, as discussed with Makersite and aPriori. These companies focused more on traditional operations in a manufacturing plant. Through our lecture/discussion on the use of compressed air in manufacturing plants, we identified a new domain that requires attention. 

Don’t forget to register for our sustainability in action event on November 11th

 

 

The future of PLM in 2025 or 2050?

October 28, 2025 in Artificial Intelligence, Brain, Circulair Economy, Climate Change, Cloud, Data centric, Design for Sustainabillity, Digital Enterprise, Education, Green Development, Platform, PLM, Product Innovation, Sustainability | Leave a comment

This week is busy for me as I am finalizing several essential activities related to my favorite hobby, product lifecycle management or is it PLM😉?

And most of these activities will result in lengthy blog posts, starting with:
“The week(end) after <<fill in the event>>”.

Here are the upcoming actions:
Click on each image if you want to see the details:

In this Future of PLM Podcast series, moderated by Michael Finocciaro, we will continue the debate on how to position PLM (as a system or a strategy) and move away from an engineering framing. Personally, I never saw PLM as a system and started talking more and more about product lifecycle management (the strategy) versus PLM/PDM (the systems).

Note: the intention is to be interactive with the audience, so feel free to post questions/remarks in the comments, either upfront or during the event.

You might have seen in the past two weeks some posts and discussions I had with the Share PLM team about a unique offering we are preparing: the PLM Awareness program. From our field experience, PLM is too often treated as a technical issue, handled by a (too) small team.

We believe every PLM program should start by fostering awareness of what people can expect nowadays, given the technology, experiences, and possibilities available. If you want to work with motivated people, you have to involve them and give them all the proper understanding to start with.

Join us for the online event to understand the value and ask your questions. We are looking forward to your participation.

This is another event related to the future of PLM; however, this time it is an in-person workshop, where, inspired by four PLM thought leaders, we will discuss and work on a common understanding of what is required for a modern PLM framework. The workshop, sponsored by the Arrowhead fPVN project, will be held in Paris on November 4th, preceding the PLM Roadmap/PDT Europe conference.

We will not discuss the term PLM; we will discuss business drivers, supporting technologies and more. My role as a moderator of this event is to assist with the workshop, and I will share its findings with a broader audience that wasn’t able to attend.

Be ready to learn more in the near future!

Suppose you have followed my blog posts for the past 10 years. In that case, you know this conference is always a place to get inspired, whether by leading companies across industries or by innovative and engaging new developments. This conference has always inspired and helped me gain a better understanding of digital transformation in the PLM domain and how larger enterprises are addressing their challenges.

This time, I will conclude the conference with a lecture focusing on the challenging side of digital transformation and AI: we humans cannot transform ourselves, so we need help.

At the end of this year, we will “celebrate” our fifth anniversary of the PLM Green Global Alliance. When we started the PGGA in 2020, there was an initial focus on the impact of carbon emissions on the climate, and in the years that followed, climate disasters around the world caused serious damage to countries and people.

How could we, as a PLM community, support each other in developing and sharing best practices for innovative, lower-carbon products and processes?

In parallel, driven by regulations, there was also a need to improve current PLM practices to efficiently support ESG reporting, lifecycle analysis, and, soon, the Digital Product Passport. Regulations that push for a modern data-driven infrastructure, and we discussed this with the major PLM vendors and related software or solution partners. See our YouTube channel @PLM_Green_Global_Alliance

In this online Zoom event, we invite you to join us to discuss the topics mentioned in the announcement. Join us in this event and help us celebrate!

I am closing that week at the PTC/User Benelux event in Eindhoven, the Netherlands, with a keynote speech about digital transformation in the PLM domain. Eindhoven is the city where I grew up, completed my amateur soccer career, ran my first and only marathon, and started my career in PLM with SmarTeam. The city and location feel like home. I am looking forward to discussing and meeting with the PTC user community to learn how they experience product lifecycle management, or is it PLM😉?

With all these upcoming events, I did not have the time to focus on a new blog post; however, luckily, in the 10x PLM discussion started by Oleg Shilovitsky there was an interesting comment from Rob Ferrone related to that triggered my mind. Quote:

The big breakthrough will come from 1. advances in human-machine interface and 2. less % of work executed by human in the loop. Copy/paste, typing, voice recognition are all significant limits right now. It’s like trying to empty a bucket of water through a drinking straw. When tech becomes more intelligent and proactive then we will see at least 10x.

This remark reminded me of one of my first blog posts in 2008, when I was trying to predict what PLM would look like in 2050. I thought it is a nice moment to read it (again). Enjoy!

 

PLM in 2050

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:

  • Data is no longer replicated – every piece of information will have a Universal Unique ID, also known as a UUID. In 2020, this initiative became mature, thanks to the merger of some big PLM and ERP vendors, who brought this initiative to reality. This initiative dramatically reduced exchange costs in supply chains and led to bankruptcy for many companies that provided translation and exchange software.
  • Companies store their data in ‘the cloud’ based on the concept outlined above. Only some old-fashioned companies still handle their own data storage and exchange, as they fear someone will access their data. Analysts compare this behavior with the situation in the year 1950, when people kept their money under a mattress, not trusting banks (and they were not always wrong)
  • After 3D, a complete virtual world based on holography became the next step in product development and understanding. Thanks to the revolutionary quantum-3D technology, this concept could even be applied to life sciences. Before ordering a product, customers could first experience and describe their needs in a virtual environment.
  • Finally, the cumbersome keyboard and mouse were replaced by voice and eye recognition. Initially, voice recognition

    and eye tracking were cumbersome. Information was captured by talking to the system and by recording eye movements during hologram analysis. This made the life of engineers so much easier, as while researching and talking, their knowledge was stored and tagged for reuse. No need for designers to send old-fashioned emails or type their design decisions for future reuse
  • Due to the hologram technology, the world became greener. People did not need to travel around the world, and the standard became virtual meetings with global teams(airlines discontinued business class). Even holidays can be experienced in the virtual world thanks to a Dutch initiative inspired by coffee. The whole IT infrastructure was powered by efficient solar energy, drastically reducing the amount of carbon dioxide.
  • Then, with a shock, I noticed PLM no longer existed. Companies were focusing on their core business processes. Systems/terms like PLM, ERP, and CRM no longer existed. Some older people still remembered the battle between those systems over data ownership and the political discomfort this caused within companies.
  • As people were working so efficiently, there was no need to work all week. There were community time slots when everyone was active, but 50 per cent of the time, people had time to recreate (to re-create or recreate was the question). Some older French and German designers remembered the days when they had only 10 weeks holiday per year, unimaginable nowadays.

As we still have more than 40 years to reach this future, I wish you all a successful and excellent 2009.

I am looking forward to being part of the green future next year.

 

 

There is more than THE BOM!

October 13, 2025 in Asset Lifecycle Management, Configuration Management, CTO, Data centric, Digital Enterprise, digital thread, ETO, PDM, PLM, SmarTeam | Leave a comment

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.

The fun started with a post from Oleg referring to a so-called BOM battle presented at Autodesk University by Gus Quade.

The image seems fake; however, the muscle power behind the BOM players looks real.

Prof. Dr. Jörg Fischer, also pictured, is advocating for rethinking PLM and BOM structures, and I share his discomfort.

Prof. Fischer wrote recently:  “Forget everything you know about EBOM and MBOM. CTO+ is rewriting the rules of PLM. “

I am not a CTO expert, but I can grasp the underlying concepts and understand why it is closely associated with SAP. It aligns with the ultimate goal of maintaining a continuous flow of information throughout the company, with ERP (SAP?) at its core.

My question is, how far are we from that option?

Current PLM implementations often focus on a linear process and data collection from left to right, as illustrated in the old Aras image below. I call this the coordinated approach.

During the recent Dutch PLM platform meeting, we also discussed the potential need for an eBOM, mBOM, and potentially the sBOM. A topic many mid-sized manufacturing companies have not mastered or implemented yet – illustrating the friction in current businesses.

Meanwhile, we discuss agentic AI, the need for data quality, ontologies and graph databases. Take a look at the upcoming workshop on the Future of PLM, scheduled for November 4th in Paris, which serves as a precursor to the PLM Roadmap/PDT Europe 2025 conference on November 5th and 6th.

The reality in the field and future capabilities seem to be so far apart, which made me think about what the next step is after BOM management to move towards the future.

The evolution of the BOM

For those active in PLM, this brief theory ensures we share a common understanding of BOMs.

 

Level 0: In the beginning, there was THE BOM.

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.

 

Level 1: Then came a new BOM from CAD.

With the rise of PDM systems and 3D CAD, another BOM emerged — reflecting the product’s design structure, including assemblies and parts. Often referred to as the CAD or engineering BOM, it frequently contained manufacturing details, such as supplier parts or consumables like paint and glue.

This hybrid BOM bridged engineering and manufacturing, linking CAD/PDM with ERP. Many machine manufacturers adopted this model, as each project was customer-specific and often involved reusing data by copying similar projects.

Many industrial manufacturers still use this linear approach to deliver solutions to their customers.

 

Level 2: The real eBOM and mBOM arrived.

Later, companies began distinguishing between the engineering BOM (eBOM) and manufacturing BOM (mBOM), especially as engineering became centralized and manufacturing decentralized.

The eBOM represented the stable engineering definition, while the mBOM was derived locally, adapting parts to specific suppliers or production needs.

At the same time, many organizations aimed to evolve toward a Configure-to-Order (CTO) business model — a long-term aspiration in aligning engineering and manufacturing flexibility, as noted by Prof. Jörg Fischer in his CTO+ concept.

 

A side step: The impact of modularity

Shifting from Engineer-to-Order (ETO) to Configure-to-Order (CTO) relies on adopting a modular product architecture. Modularity enables specific modules to remain stable while others evolve in response to ongoing innovation.

It’s not just about creating a 200% eBOM or 150% mBOM but about defining modules with their own lifecycles that may span multiple product platforms. Many companies still struggle to apply these principles, as seen in discussions within the North European Modularity (NEM) network.

See one of my reports: The week after the North European Modularity network meeting.

We remain here primarily in the xBOM mindset: the eBOM defines engineering specifications, while the mBOM defines the physical realization—specific to suppliers or production sites.

 

Level 3: Extending to the sBOM?

To support service operations, the service BOM (sBOM) is introduced, managing serviceable parts and kits linked to the product. Managing service information in a connected manner adds complexity but also significant value, as the best margins often come from after-sales service.

Click on the image above to understand the relations between the eBOM, mBOM(s) and sBOM.

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.

For most hardware manufacturers, the story ends here—but when software and product updates become part of the service, the lifecycle story continues.

 

The next levels: Software and Product Services require more than a BOM

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.

There were two themes we discussed:

  • The product gets physical updates and upgrades – how can we manage this with the sBOM – challenges with BOM versions or revisions ( a legacy approach)
  • The product functions based on software-driven behavior, and the software can be updated on demand – how can we manage this with the sBOM (a different lifecycle)

The conclusion and answer to these two questions were:

We cannot use the sBOM anymore for this; in both cases, you need an additional (infra)structure to keep track of changes over time, I call it the logical product structure or product architecture.

 

The Logical Product Structure

Since 2008, I have been involved in Asset Lifecycle Management projects, explaining the complementary value of PLM methodology and concepts related to an MRO environment, particularly for managing significant assets, such as those in the nuclear plants industry.

Historically, the configuration management of a plant was a human effort undertaken by individuals with extensive intrinsic knowledge.

A nuclear plant is an asset with a very long lifecycle that requires regular upgrades and services, and where safety is the top priority. However, thanks to digitization and an aging workforce, there was also a need to embed these practices within a digital infrastructure.

What I learned is that the logical product structure, also known as the plant breakdown structure (PBS), became an essential structure for combining the as-designed and as-operated structures of the plant.

In the SmarTeam image below, the plant breakdown structure was represented by the tag structure.

Coming back to our industrial products in service, it is conceptually a similar approach, albeit that the safety drivers and business margins might make it less urgent. For a product, there can also be a logical product structure that represents the logical components and their connections.

The logical structure of a product remains stable over time; however, specific modules or capabilities may be required, while the physical implementation (mBOM) and engineering definition (eBOM) may evolve over time.

Additionally, all relevant service activities, including issues and operational and maintenance data, can be linked to the logical structure. The logical structure is also the structure used for a digital twin representation.

 

The logical product structure and software

The logical product structure is also where hardware and software meet. The software can be managed in an ALM environment and provides traceability to the product in service through the product structure.

Note: this is a very simplified version, as you can imagine, it looks more like a web of connected datasets – the top level shows the traceability between the various artifacts – HW and SW

Where is the product structure defined?

The product structure originates from a system architect, and it depends on the tools they are using, where it is defined – historically in a document, later in an Excel file – the coordinated approach.

In a modern data-driven environment, you can find the product structure in an MBSE environment and then connect to a PLM system – the federated and connected approach.

There are also PLM vendors that have the main MBSE data elements in their core data model, reducing the need for building connectivity between the main PLM and MBSE elements. In my experience, the “all-in-one” solutions still underperform in usability and completeness.

Conclusion

I wrote this post to raise awareness that a narrow focus on BOM structures can create a potential risk for the future. Changing business models, for example, the product-service system, require a data-driven infrastructure where both hardware and software artifacts need to be managed in context. Probably not in a single system but supported by a federated infrastructure with a mix of technologies. And I feel sorry that I could not write about a model-based enterprise at this time!

I am looking forward to discussing the future of PLM with a select group of thought leaders on November 4th in Paris, as a precursor to the upcoming PLM Roadmap/PDT Europe conference. For the workshop on November 4th, we almost reached our maximum size we can accommodate, but for the conference, there is still the option to join us.

Please review the agenda and join us for engaging and educational discussions if you can.

And if you are not tired of discussing PLM as a term, a system or a strategy – watch the recording of this unique collection of PLM voices moderated by Michael Finochario.

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  2. Unknown's avatar
  3. Håkan Kårdén's avatar

    Jos, all interesting and relevant. There are additional elements to be mentioned and Ontologies seem to be one of the…

  4. Lewis Kennebrew's avatar

    Jos, as usual, you've provided a buffet of "food for thought". Where do you see AI being trained by a…

  5. Håkan Kårdén's avatar