My PLM life in decades

September 15, 2025 in Artificial Intelligence, Change, Cloud, Data centric, Digital Enterprise, digital thread, PDM, Platform, PLM | Leave a comment

After a summer holiday in the south of Greece, it is time to resume my activities. The south of Crete is largely an analogue environment, far from any digital hype.

Tempted by LinkedIn posts, I noticed the summer was full of memories, with Martin Eigner sharing 40 years of PLM experience, Oleg Shilovitsky sharing 30 years of PDM Evolution, and Michael Finochario publishing posts on PLM vendors, CAD kernels, and more.

So where do I stand? While digesting all these historical experiences, I reflected on what we can learn from them and what we didn’t learn from them.

 

It started with technology.

From 1990 to 1999, I worked with mid-market companies, where data management was the most significant challenge. The introduction of MS Windows made data management more user-friendly, evolving from drawing management systems with version and status management capabilities.

Who remembers Automanager Workflow from Cyco, before SmarTeam came on the market?

For that reason, in the early days, PDM was an IT job. As the PDM system primarily dealt with engineering data, it was relatively easy to implement as an organizational change process. We transitioned from analogue to electronic in the department.

Connecting with other systems, particularly ERP, was a serious IT job and a financial challenge. Connecting with other systems, particularly ERP, was a serious IT job and a financial challenge. The rapid decline of IT components, combined with the rapid growth of global connectivity, has created new opportunities for collaboration.

As part of the Dassault/IBM/SmarTeam organization, I explained and taught these new capabilities worldwide.

In 2008, my VirtualDutchman blog and coaching journey began, evolving from explanations of technology to modern methodologies, which led to organizational change and expectation management – skills not traditionally associated with IT.

 

Then came digital transformation

With growing connectivity, smartphones and Web 2.0 technology have led to more PLM-like discussions. PLM vendors expanded their scope and developed capabilities beyond mechanical engineering.

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?

At the time, they were largely considered the same in discussions and advertisements..

Meanwhile, digital transformation was occurring at the marketing and sales levels – companies invested in direct communication with their customers through the web.

Meanwhile, the internal ways of working for R&D, engineering, and manufacturing did not change significantly. Still, they were following linear processes, and despite the existence of 3D CAD, the 2D drawing remained the primary carrier of legal information between engineering, manufacturing, and suppliers.

Note: the option where the most benefits could be achieved – connected supply chains – had the lowest focus in 2017 – something that would change with COVID-19.

Fundamental digital transformation in the PLM domain occurred gradually. ARAS came with its overlay approach (the platform), connecting various disciplines and enterprise systems. In contrast, Dassault Systèmes introduced its 3DEXPERIENCE platform, utilizing its own software brands as platform components.

The Aras overlay approach

Most PLM vendors rapidly countered Aras’ overlay approach with their low-code offerings based on Mendix, ThingWorx or Netvibes, to enable data flows beyond the traditional PDM scope. The Coordinated Digital Thread was born.

The good news is that PLM has now clearly become a strategy based on a federated system infrastructure. The single PLM system no longer exists, although many of us still use the term’ PLM system’ to refer to the main component of a PLM infrastructure – the System of Record.

Moving to a federated PLM infrastructure is already a challenge for companies, not because of the available technology, but first of all because of the legacy data and, closely related to that, legacy processes and people skills.

Legacy is creating the inertia, not technology!

 

Next came the cloud – SaaS

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.

A connected enterprise no longer needs interfaces to transfer data from one system to another.

Instead, with apps and dashboards, combined data from different online sources is presented in a single, user-friendly working environment – A combination of the Systems of Record with the new environments – the Systems of Engagement.

The technology used to create dashboards and apps is based on modern data-driven technologies and principles (ontologies, graph databases, and the semantic web). The Connected Digital Thread was born.

However, legacy systems play an essential role again, as some systems of engagement can be implemented in a complementary manner to the systems of record, allowing companies to work within an integrated technology model.

People will work in a particular mode, either coordinated or connected, but organizations can operate in both modes simultaneously. A story I have been sharing a lot – it is not about migrations but about an evolutionary approach towards an integrated technology model.

At this point, it becomes essential that business objectives drive the implementation of a PLM infrastructure. Of course, you hear me say we should start from the business; however, the big difference now is that a company should coordinate the technologies, systems, and tools it acquires to avoid isolated islands of information.

Follow Yousef Hooshmand‘s 5 + 1 business transformation steps.

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 Rob Feronne, the product Digital PLuMber:

There are some great insights in this dialogue and the associated LinkedIn comments.

Despite the increasing availability of technology, it is the legacy of people, processes, and culture that is hindering progress.

Rob Feronne had a shocking lightbulb moment 😲 in our discussion about the future of PLM, where the participants – see below –  answered a question related to the importance of technology in our PLM domain – shocking also for me.

My thumb was up because modern technology matters! The question inspired Oleg Shilovitsky to write a whole blog post on this topic. If you’re truly shocked, read his post, where I agree with the content; the question is too simple to answer with a thumbs up/down.

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..

 

And now there is AI

If you haven’t read anything about AI recently, you must be living in an isolated location. Regardless of the business discussions you are following, it is all about the potential of AI.

Although AI is not a new concept, the fact that various AI capabilities have now reached the end-user level is what drives the hype. Currently, I believe we are at the peak of the hype.

Last week, I participated in an interesting discussion in the series: The Future of PLM moderated by Michael Finochario, this time talking with the analysts. Click on the link to see Michael’s excellent summary and access to the recording of the event.

It was an interesting discussion for a little more than an hour, and the majority of our discussion was about the potential impact of AI on businesses. First, the impact AI can have on the traditional work of an analyst and next, the effects on the PLM domain.

I believe we agreed that AI at this moment is mainly providing higher user efficiency and performance, very much aligned with the interesting research I have been reading in the MIT NANDA report with the title The GenAI Divide: STATE OF AI IN BUSINESS 2025

The report’s interesting findings included high adoption of tools but low transformation. Despite significant investment in Generative AI (GenAI), most organizations are not achieving meaningful business transformation. ​

  • 95% of organizations report zero return on GenAI investments. ​
  • Only 5% of integrated AI pilots generate millions in value. ​
  • 80% of organizations have explored or piloted tools like ChatGPT, but these primarily enhance individual productivity.
  • 60% of organizations evaluated enterprise-grade systems, but only 20% reached the pilot stage, and just 5% reached production. ​
  • Key barriers include brittle workflows, a lack of contextual learning, and operational misalignment. ​

Therefore, the question is – Is current AI the next bubble?

In 2014, I wrote about the lack of digital transformation in the PLM domain, and two images (below) from a report by The Economist could be used again. The report can be found here: The Onrushing Wave.

Click on the image to read the 2013 predictions.

I realized that my current job, as a recreational therapist and firefighter at the time, was not at risk, and that some of the predictions from 10 years ago had become a reality. Who is still bothered by telemarketers or retail salespersons?

However, many of the AI symptoms mentioned in the MIT NANDA report are similar to the hype surrounding digital transformation.

The only reservation I have now – will it take a decade before we understand and demonstrate the value of AI, or are we accelerating?

In this context, the upcoming PLM Roadmap/PDT Europe conference on 5 – 6 November will be interesting, as here we will discuss reality.

For a few of you interested in more, there is the day before the conference, a (free) workshop where we will discuss with some thought leaders and experts from various companies how the future of PLM could look like – based on standards, AI tools and more. Click on the image below the conclusion.

 

Conclusion

The summertime was a nice moment to reflect, inspired by others in my network. What is clear is that there is a shift from technology towards people and change. The rapid expansion of AI tools, along with connected technologies, has created an overwhelming array of possibilities. Now it is time for business leadership to understand them and utilize them for significant business improvement, where the fear is that substantial change will always be slowed down by organizational inertia.

 

Building the Future: Data Quality and Governance in the Digital Age (part 3)

August 4, 2025 in Artificial Intelligence, Change, Competitiveness, Data centric, Data Governance, Data Quality, Digital Enterprise, digital thread, Industry 4.0, PLM | Leave a comment

In the past three weeks, between some short holidays, I had a discussion with Rob Ferrone, who you might know as
“The original product Data PLuMber”.

Our discussion resulted in this concluding post and these two previous posts:

If you haven’t read them before, please take a moment to review them, to understand the flow of our dialogue and to get a full, holistic view of the WHY, WHAT and HOW of data quality and data governance.

A foundation required for any type of modern digital enterprise, with or without AI.

 

A first feedback round

Rob, I was curious whether there were any interesting comments from the readers that enhanced your understanding. For me, Benedict Smith’s point in the discussion thread was an interesting one.

From this reaction, I like to quote:

To suggest it’s merely a lack of discipline is to ignore the evidence. We have some of the most disciplined engineers in the world. The problem isn’t the people; it’s the architecture they are forced to inhabit.

My contention is that we have been trying to solve a reasoning problem with record-keeping tools. We need to stop just polishing the records and start architecting for the reasoning. The “what” will only ever be consistently correct when the “why” finally has a home. 😎 

Here, I realized that the challenge is not only about moving From Coordinated to Coordinated and Connected, but also that our existing record-keeping mindset drives the old way of thinking about data. In the long term, this will be a dead end.

What did you notice?

Jos, indeed, Benedict’s point is great to have in mind for the future and in addition, I also liked the comment from Yousef Hooshmand, where he explains that a data-driven approach with a much higher data granularity automatically leads to a higher quality –  I would quote Yousef:

The current landscapes are largely application-centric and not data-centric, so data is often treated as a second or even third-class citizen.

In contrast, a modern federated and semantic architecture is inherently data-centric. This shift naturally leads to better data quality with significantly less overhead. Just as important, data ownership becomes clearly defined and aligned with business responsibilities.

Take “weight” as a simple example: we often deal with “Target Weight,” “Calculated Weight,” and “Measured Weight.” In a federated, semantic setup, these attributes reside in the systems where their respective data owners (typically the business users) work daily, and are semantically linked in the background.

I believe the interesting part of this discussion is that people are thinking about data-driven concepts as a foundation for the paradigm, shifting from systems of record/systems of engagement to systems of reasoning. Additionally, I see how Yousef applies a data-centric approach in his current enterprise, laying the foundation for systems of reasoning.

 

What’s next?

Rob, your recommendations do not include a transformation, but rather an evolution to become better and more efficient – the typical work of a Product PLuMber, I would say. How about redesigning the way we work?

Bold visions and ideas are essential catalysts for transformations, but I’ve found that the execution of significant, strategic initiatives is often the failure mode.

One of my favourite quotes is:

“A complex system that works is invariably found to have evolved from a simple system that worked.”

John Gall, Systemantics (1975)

For example, I advocate this approach when establishing Digital Threads.

It’s easy to imagine a Digital Thread, but building one that’s sustainable and delivers measurable value is a far more formidable challenge.

Therefore, my take on Digital Thread as a Service is not about a plug-and-play Digital Thread, but the Service of creating valuable Digital Threads.

You achieve the solution by first making the Thread work and progressively ‘leaving a trail of construction’.

The caveat is that this can’t happen in isolation; it must be aligned with a data strategy, a set of principles, and a roadmap that are grounded in the organization’s strategic business imperatives.

 

Your answer relates a lot to Steef Klein’s comment when he discussed: “Industry 4.0: Define your Digital Thread ML-related roadmap – Carefully select your digital innovation steps.” You can read Steef’s full comment here: Your architectural Industry 4.0 future)  

First, I liked the example value cases presented by Steef. They’re a reminder that all these technology-enabled strategies, whether PLM, Digital Thread, or otherwise, are just means to an end. That end is usually growth or financial performance (and hopefully, one day, people too).

It is a bit like Lego, however. You can’t build imaginative but robust solutions unless there is underlying compatibility and interoperability.

It would be a wobbly castle made from a mix of Playmobil, Duplo, Lego and wood blocks (you can tell I have been doing childcare this summer – click on the image to see the details).

As the lines blur between products, services, and even companies themselves, effective collaboration increasingly depends on a shared data language, one that can be understood not just by people, but by the microservices and machines driving automation across ecosystems.

 

Discussing the future?

I think that for those interested in this discussion, I would like to point to the upcoming PLM Roadmap/PDT Europe 2025 conference on November 5th and 6th in Paris, where some of the thought leaders in these concepts will be presenting or attending. The detailed agenda is expected to be published after the summer holidays.

However, this conference also created the opportunity to have a pre-conference workshop, where Håkan Kårdén and I wanted to have an interactive discussion with some of these thought leaders and practitioners from the field.

Sponsored by the Arrowhead fPVN project, we were able to book a room at the conference venue in the afternoon of November 4th. You can find the announcement and more details of the workshop here in Hakan’s post:. Shape the Future of PLM – Together.

Last year at the PLM Roadmap PDT Europe conference in Gothenburg, I saw a presentation of the Arrowhead fPVN project. You can read more here: The long week after the PLM Roadmap/PDT Europe 2024 conference.

And, as you can see from the acknowledged participants below, we want to discuss and understand more concepts and their applications – and for sure, the application of AI concepts will be part of the discussion.

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.

 

And the people?

Rob, we just came from a human-centric PLM conference in Jerez – the Share PLM 2025 summit – where are the humans in this data-driven world?

 

You can’t have a data-driven strategy in isolation. A business operating system comprises the coordinated interaction of people, processes, systems, and data, aligned to the lifecycle of products and services. Strategies should be defined at each layer, for instance, whether the system landscape is federated or monolithic, with each strategy reinforcing and aligning with the broader operating system vision.

In terms of the people layer, a data strategy is only as good as the people who shape, feed, and use it. Systems don’t generate clean data; people do. If users aren’t trained, motivated, or measured on quality, the strategy falls apart.

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.

There are risks; however, a system-driven approach can clash with local flexibility/agility.

People who previously operated on instinct or informal processes may now need to justify actions with data. And if the data is poor or the outputs feel misaligned, people will quickly disengage, reverting to offline workarounds or intuition.

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);

therefore there needs to be product data professionals in the mix – people who care, notice what’s wrong, and know how to fix it across silos.

 

Conclusion

  • Our discussions on data quality and governance revealed a crucial insight: this is not a technical journey, but a human one. While the industry is shifting from systems of record to systems of reasoning, many organizations are still trapped in record-keeping mindsets and fragmented architectures. Better tools alone won’t fix the issue—we need better ownership, strategy, and engagement.
  • True data quality isn’t about being perfect; it’s about the right maturity, at the right time, for the right decisions. Governance, too, isn’t a checkbox—it’s a foundation for trust and continuity. The transition to a data-centric way of working is evolutionary, not revolutionary—requiring people who understand the business, care about the data, and can work across silos.

The takeaway? Start small, build value early, and align people, processes, and systems under a shared strategy. And if you’re serious about your company’s data, join the dialogue in Paris this November.

Where are you on the AI hype cycle ?

 

 

 

PLM and Sustainability: continue talking with Sustaira

July 20, 2025 in Cloud, Data centric, Life Cycle Analysis, Life Cycle Assessment, Platform, PLM | Tags: , , | Leave a comment

Just before or during the summer holidays, we were pleased to resume our interview series on PLM and Sustainability, where the PLM Green Global Alliance interviews PLM-related software vendors and service organizations, discussing their sustainability missions and offerings.

Following recent discussions in the PLM ecosystem, including PSC Transition Technologies (EcoPLM), CIMPA PLM services (LCA), and the Design for Sustainability working group (with multiple vendors & service partners), we now have the opportunity to catch up with Sustaira after almost three years.

In 2022, Sustaira was a startup company focused on building and providing data-driven, efficient support for sustainability reporting and analysis based on the Mendix platform, while engaging with their first potential customers. What has happened in those three years?

 

SUSTAIRA

Sustaira provides a sustainability management software platform that helps organizations track, manage, and report their environmental, social, and governance (ESG) performance through customizable applications and dashboards.

We spoke again with Vincent de la Mar, founder and CEO of Sustaira, and it was pretty clear from our conversation that they have evolved and grown in their business and value proposition for businesses. As you will discover by listening to the interview, they are not, per se, in the PLM domain.

Enjoy the 35-minute interview below.

Slides shown during the interview, combined with additional company information, can be found HERE.

 

What we have learned

  • Sustaira is a modular, AI-driven sustainability platform. It offers approximately 150 “sustainability accelerators,” which are either complete Software as a Service (SaaS) products (such as carbon accounting, goal/KPI tracking, and disclosures) or adaptable SaaS products that allow for complete configuration of data models, logic, and user interfaces.
  • Their strategy is based on three pillars:
    • providing an end-to-end sustainability platform (Ports of Jersey),
    • filling gaps in an enterprise architecture and business needs (Science-Based Target Initiatives)
    • Co-creating new applications with partners (BCAF with Siemens Financial Services)
  • The company has a pragmatic view on AI and thanks to its scalable, data-driven Mendix platform, it can bring integrated value compared to niche applications that might become obsolete due to changing regulations and practices (e.g., dedicated CSRD apps)
  • The Sustainability Global Alliance, in partnership with Capgemini, is a strategic alliance that benefits both parties, with a focus on AI & Sustainability.
  • The strong partnership with Siemens Digital Solutions.
  • Their monthly Sustainability and ESG Insights newsletter, also published in our PGGA group, already has 55.000 subscribers.

Want to learn more?

The following links provide more information related to Sustaira:

 

Conclusion

It was great to observe how Sustaira has grown over the past three years, establishing a broad portfolio of sustainability-related solutions for various types of businesses. Their relationship with Siemens Digital Solutions enables them to bring value and add capabilities to the Siemens portfolio, as their platform can be applied to any company that needs a complementary data-driven service related to sustainability insights and reporting.

Follow the news around this event – click on the image to learn more.

Data Quality and Data Governance – the WHY and HOW (part 2)

July 13, 2025 in Artificial Intelligence, Change, Configuration Management, Data centric, Data Governance, Data Quality, Digital Enterprise, digital thread, PLM | Leave a comment

In my first discussion with Rob Ferrone, the original Product Data PLuMber, we discussed the necessary foundation for implementing a Digital Thread or leveraging AI capabilities beyond the hype. This is important because all these concepts require data quality and data governance as essential elements.

If you missed part 1, here is the link:  Data Quality and Data Governance – A hype?

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.

Cristina Jimenez Pavo’s comment illustrates that the question is in the air.:

Everyone knows that it should be better; high-performing businesses have good data governance, but most people don’t know how to systematically and sustainably improve their data quality. It’s hard and not glamorous (for most), so people tend to focus on buying new systems, which they believe will magically resolve their underlying issues.

 

Data governance as a strategy

Thanks for the clarification. I imagine it is similar to Configuration Management, i.e., with different needs per industry. I have seen ISO 8000 in the aerospace industry, but it has not spread further to other businesses. What about data governance as a strategy, similar to CM?

That’s a great idea. Do you mind if I steal it?

If you ask any PLM or ERP vendor, they’ll claim to have a master product data governance template for every industry. While the core principles—ownership, control, quality, traceability, and change management, as in Configuration Management—are consistent, their application must vary based on the industry context, data types, and business priorities.

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.

The challenge is that both configuration management (CM) and data governance often suffer from a perception problem, being viewed as abstract or compliance-heavy. In truth, they must be practical, embedded in daily workflows, and treated as dynamic systems central to business operations, rather than static documents.

Think of it like the difference between stepping on a scale versus using a smartwatch that tracks your weight, heart rate, and activity, schedules workouts, suggests meals, and aligns with your goals.

Governance should function the same way:
responsive, integrated, and outcome-driven.

 

Who is responsible for data quality?

I have seen companies simplifying data quality as an enhancement step for everyone in the organization, like a “You have to be more accurate” message, similar perhaps to configuration management. Here we touch people and organizational change. How do you make improving data quality happen beyond the wish?

In most companies, managing product data is a responsibility shared among all employees. But increasingly complex systems and processes are not designed around people, making the work challenging, unpleasant, and often poorly executed.

I like to quote Larry English – The Father of Information Quality:

“Information producers will create information only to the quality level for which they are trained, measured and held accountable.”

A common reaction is to add data “police” or transactional administrators, who unintentionally create more noise or burden those generating the data.

The real solution lies in embedding capable, proactive individuals throughout the product lifecycle who care about data quality as much as others care about the product itself – it was the topic I discussed at the 2025 Share PLM summit in Jerez – Rob Ferrone – Bill O-Materials also presented in part 1 of our discussion.

These data professionals collaborate closely with designers, engineers, procurement, manufacturing, supply chain, maintenance, and repair teams. They take ownership of data quality in systems, without relieving engineers of their responsibility for the accuracy of source data.

Some data, like component weight, is best owned by engineers, while others—such as BoM structure—may be better managed by system specialists. The emphasis should be on giving data professionals precise requirements and the authority to deliver.

They not only understand what good data looks like in their domain but also appreciate the needs of adjacent teams. This results in improved data quality across the business, not just within silos. They also work with IT and process teams to manage system changes and lead continuous improvement efforts.

The real challenge is finding leaders with the vision and drive to implement this approach.

 

The costs or benefits associated with good or poor data quality

At the peak of interest in being data-driven, large consulting firms published numerous studies and analyses, proving that data-driven companies achieve better results than their data-averse competitors. Have you seen situations where the business case for improving “product data” quality has led to noticeable business benefits, and if so, in what range? Double digit, single digit?

Improving data quality in isolation delivers limited value. Data quality is a means to an end. To realise real benefits, you must not only know how to improve it, but also how to utilise high-quality data in conjunction with other levers to drive improved business outcomes.

I built a company whose premise was that good-quality product data flowing efficiently throughout the business delivered dividends due to improved business performance. We grew because we delivered results that outweighed our fees.

Last year’s turnover was €35M, so even with a conservatively estimated average in-year ROI of 3:1, the company delivered over € 100 M of cost savings or additional revenue per year to clients, with the majority of these benefits being sustainable.

There is also the potential to unlock new value and business models through data-driven innovation.

For example, connecting disparate product data sources into a unified view and taking steps to sustainably improve data quality enables faster, more accurate, and easier collaboration between OEMs, fleet operators, spare parts providers, workshops, and product users, which leads to a new value proposition around minimizing painful operational downtime.

 

AI and Data Quality

Currently, we are seeing numerous concepts emerge where AI, particularly AI agents, can be highly valuable for PLM. However, we also know that in legacy environments, the overall quality of data is poor. How do you envision AI supporting PLM processes, and where should you start? Or has it already started?

It’s like mining for rare elements—sifting through massive amounts of legacy data to find the diamonds. Is it worth the effort, especially when diamonds can now be manufactured? AI certainly makes the task faster and easier. Interestingly, Elon Musk recently announced plans to use AI to rewrite legacy data and create a new, high-quality knowledge base. This suggests a potential market for trusted, validated, and industry-specific legacy training data.

Will OEMs sell it as valuable IP, or will it be made open source like Tesla’s patents?

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.

That said, data alignment remains complex, as discrepancies can be valid depending on context.

A practical starting point?

Data Quality as a Service. My former company, Quick Release, is piloting an AI-enabled service focused on EBoM to MBoM alignment. It combines a data quality platform with expert knowledge, collecting metadata from PLM, ERP, MES, and other systems to map engineering data models.

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.

 

Summary

We hope the discussions in parts 1 and 2 helped you understand where to begin. It doesn’t need to stay theoretical or feel unachievable.

  1. The first step is simple: recognise product data as an asset that powers performance, not just admin.
    Then treat it accordingly.
  2. You don’t need a 5-year roadmap or a board-approved strategy before you begin. Start by identifying the product data that supports your most critical workflows, the stuff that breaks things when it’s wrong or missing. Work out what “good enough” looks like for that data at each phase of the lifecycle.
    Then look around your business: who owns it, who touches it, and who cares when it fails?
  3. From there, establish the roles, rules, and routines that help this data improve over time, even if it’s manual and messy to begin with. Add tooling where it helps.
  4. Use quality KPIs that reflect the business, not the system. Focus your governance efforts where there’s friction, waste, or rework.
  5. And where are you already getting value? Lock it in. Scale what works.

 

Conclusion 

It’s not about perfection or policies; it’s about momentum and value. Data quality is a lever. Data governance is how you pull it.  
 
Just start pulling- and then get serious with your AI applications! 

 

Are you attending the PLM Roadmap/PDT Europe 2025 conference on
November 5th & 6th in Paris, La Defense?

There is an opportunity to discuss the future of PLM in a workshop before the event.
More information will be shared soon; please mark November 4th in the afternoon on your agenda.

Data Quality and Data Governance – A hype? (part 1)

July 7, 2025 in Data centric, Data Governance, Data Quality, digital thread, PLM | Tags: , , , | Leave a comment

The title of this post is chosen influenced by one of Jan Bosch’s daily reflections # 156: Hype-as-a-Service. You can read his full reflection here.

His post reminded me of a topic that I frequently mention when discussing modern PLM concepts with companies and peers in my network. Data Quality and Data Governance, sometimes, in the context of the connected digital thread, and more recently, about the application of AI in the PLM domain.

I’ve noticed that when I emphasize the importance of data quality and data governance, there is always a lot of agreement from the audience.  However, when discussing these topics with companies, the details become vague.

Yes, there is a desire to improve data quality, and yes, we push our people to improve the quality processes of the information they produce. Still, I was curious if there is an overall strategy for companies.

And who to best talk to? Rob Ferrone, well known as “The original Product Data PLuMber” – together, we will discuss the topic of data quality and governance in two posts. Here is part one – defining the playground.

The need for Product Data People

During the Share PLM Summit, I was inspired by Rob’s theatre play, “The Engineering Murder Mystery.” Thanks to the presence of Michael Finocchiaro, you might have seen the play already on LinkedIn – if you have 20 minutes, watch it now.

Rob’s ultimate plea was to add product data people to your company to make the data reliable and flow. So, for me, he is the person to understand what we mean by data quality and data governance in reality – or is it still hype?

What is data?

Hi Rob, thank you for having this conversation. Before discussing quality and governance, could you share with us what you consider ‘data’ within our PLM scope?  Is it all the data we can imagine?

I propose that relevant PLM data encompasses all product-related information across the lifecycle, from conception to retirement. Core data includes part or item details, usage, function, revision/version, effectivity, suppliers, attributes (e.g., cost, weight, material), specifications, lifecycle state, configuration, and serial number.

Secondary data supports lifecycle stages and includes requirements, structure, simulation results, release dates, orders, delivery tracking, validation reports, documentation, change history, inventory, and repair data.

Tertiary data, such as customer information, can provide valuable support for marketing or design insights. HR data is generally outside the scope, although it may be referenced when evaluating the impact of PLM on engineering resources.

What is data quality?

Now that we have a data scope in mind, I can imagine that there is also some nuance in the term’ data quality’.  Do we strive for 100% correct data, and is the term “100 % correct” perhaps too ambitious? How would you define and address data quality?

You shouldn’t just want data quality for data quality’s sake. You should want it because your business processes depend on it. As for 100%, not all data needs to be accurate and available simultaneously.  It’s about having the proper maturity of data at the right time.

For example, when you begin designing a component, you may not need to have a nominated supplier, and estimated costs may be sufficient. However, missing supplier nomination or estimated costs would count against data quality when it is time to order parts.

And these deliverable timings will vary across components, so 100% quality might only be achieved when the last standard part has been identified and ordered.

It is more important to know when you have reached the required data quality objective for the top-priority content. The image below explains the data quality dimensions:

  • Completeness (Are all required fields filled in?)
    KPI Example: % of product records that include all mandatory fields (e.g., part number, description, lifecycle status, unit of measure)
  • Validity (Do values conform to expected formats, rules, or domains?)
    KPI Example: % of customer addresses that conform to ISO 3166 country codes and contain no invalid characters
  • Integrity (Do relationships between data records hold?)
    KPI Example: % of BOM records where all child parts exist in the Parts Master and are not marked obsolete
  • Consistency (Is data consistent across systems or domains?)
    KPI Example: % of product IDs with matching descriptions and units across PLM and ERP systems
  • Timeliness (Is data available and updated when needed?)
    KPI Example: % of change records updated within 24 hours of approval or effective date
  • Accuracy (Does the data reflect real-world truth?)
    KPI Example: % of asset location records that match actual GPS coordinates from service technician visits

Define data quality KPIs based on business process needs, ensuring they drive meaningful actions aligned with project goals.

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.

Like the VUCA framework, effective data quality management begins by understanding the nature of the issue: is it volatile, uncertain, complex, or ambiguous?

Not all “bad” data is flawed, some may be valid estimates, changes, or system-driven anomalies. Each scenario requires a tailored response; treating all issues the same can lead to wasted effort or overlooked insights.

Furthermore, data quality goes beyond the data itself—it also depends on clear definitions, ownership, monitoring, maintenance, and governance. A holistic approach ensures more accurate insights and better decision-making throughout the product lifecycle.

KPIs?

In many (smaller) companies KPI do not exist; they adjust their business based on experience and financial results. Are companies ready for these KPIs, or do they need to establish a data governance baseline first?

Many companies already use data to run parts of their business, often with little or no data governance. They may track program progress, but rarely systematically monitor data quality. Attention tends to focus on specific data types during certain project phases, often employing audits or spot checks without establishing baselines or implementing continuous monitoring.

This reactive approach means issues are only addressed once they cause visible problems.

When data problems emerge, trust in the system declines. Teams revert to offline analysis, build parallel reports, and generate conflicting data versions. A lack of trust worsens data quality and wastes time resolving discrepancies, making it difficult to restore confidence. Leaders begin to question whether the data can be trusted at all.

Data governance typically evolves; it’s challenging to implement from the start. Organizations must understand their operations before they can govern data effectively.

In start-ups, governance is challenging. While they benefit from a clean slate, their fast-paced, prototype-driven environment prioritizes innovation over stable governance. Unlike established OEMs with mature processes, start-ups focus on agility and innovation, making it challenging to implement structured governance in the early stages.

Data governance is a business strategy, similar to Product Lifecycle Management.

Before they go on the journey of creating data management capabilities, companies must first understand:

  1. The cost of not doing it.
  2. The value of doing it.
  3. The cost of doing it.

What is the cost associated with not doing data quality and governance?

Similar to configuration management, companies might find it a bureaucratic overhead that is hard to justify. As long as things are going well (enough) and the company’s revenue or reputation is not at risk, why add this extra work?

Product data quality is either a tax or a dividend. In Part 2, I will discuss the benefits. In Part 1, this discussion, I will focus on the cost of not doing it.

Every business has stories of costly failures caused by incorrect part orders, uncommunicated changes, or outdated service catalogs. It’s a systematic disease in modern, complex organisations. It’s part of our day-to-day working lives: multiple files with slightly different file names, important data hidden in lengthy email chains, and various sources for the same information (where the value differs across sources), among other challenges.

Above image from Susan Lauda’s presentation at the PLMx 2018 conference in Hamburg, where she shared the hidden costs of poor data. Please read about it in my blog post: The weekend after PLMx Hamburg.

Poor product data can impact more than most teams realize. It wastes time—people chase missing info, duplicate work, and rerun reports. It delays builds, decisions, and delivery, hurting timelines and eroding trust. Quality drops due to incorrect specifications, resulting in rework and field issues. Financial costs manifest as scrap, excess inventory, freight, warranty claims, and lost revenue.

Worse, poor data leads to poor decisions, wrong platforms, bad supplier calls, and unrealistic timelines. It also creates compliance risks and traceability gaps that can trigger legal trouble. When supply chain visibility is lost, the consequences aren’t just internal, they become public.

For example, in Tony’s Chocolonely’s case, despite their ethical positioning, they were removed from the Slave Free Chocolate list after 1,700 child labour cases were discovered in their supplier network.

The good news is that most of the unwanted costs are preventable. There are often very early indicators that something was going to be a problem. They are just not being looked at.

Better data governance equals better decision-making power.
Visibility prevents the inevitable.

Conclusion of part 1

Thanks to Rob’s answers, I am confident that you now have a better understanding of what Data Quality and Data Governance mean in the context of your business. In addition, we discussed the cost of doing nothing. In Part 2, we will explore how to implement it in your company, and Rob will share some examples of the benefits.

Feel free to post your questions for the original Product Data PLuMber in the comments.

Four years ago, during the COVID-19 pandemic, we discussed the critical role of a data plumber.

Are we tired?

June 23, 2025 in Brain, Change, Climate Change, Design for Sustainabillity, Education, Green Development, Green Energy, Human Centric, PLM | Tags: , , , , | Leave a comment

In recent months, I’ve noticed a decline in momentum around sustainability discussions, both in my professional network and personal life. With current global crises—like the Middle East conflict and the erosion of democratic institutions—dominating our attention, long-term topics like sustainability seem to have taken a back seat.

But don’t stop reading yet—there is good news, though we’ll start with the bad.

 

The Convenient Truth

Human behavior is primarily emotional. A lesson valuable in the PLM domain and discussed during the Share PLM summit. As SharePLM notes in their change management approach, we rely on our “gator brain”—our limbic system – call it System 1 and System 2 or Thinking Fast and Slow. Faced with uncomfortable truths, we often seek out comforting alternatives.

The film Don’t Look Up humorously captures this tendency. It mirrors real-life responses to climate change: “CO₂ levels were high before, so it’s nothing new.” Yet the data tells a different story. For 800,000 years, CO₂ ranged between 170–300 ppm. Today’s level is ~420 ppm—an unprecedented spike in just 150 years as illustrated below.

Frustratingly, some of this scientific data is no longer prominently published. The narrative has become inconvenient, particularly for the fossil fuel industry.

 

Persistent Myths

Then there is the pseudo-scientific claim that fossil fuels are infinite because the Earth’s core continually generates them. The Abiogenic Petroleum Origin theory is a fringe theory, sometimes revived from old Soviet science, and lacks credible evidence. See image below

Oil remains a finite, biologically sourced resource. Yet such myths persist, often supported by overly complex jargon designed to impress rather than inform.

 

The Dissonance of Daily Life

A young couple casually mentioned flying to the Canary Islands for a weekend at a recent birthday party. When someone objected on climate grounds, they simply replied, “But the climate is so nice there!”

“Great climate on the Canary Islands”

This reflects a common divide among young people—some are deeply concerned about the climate, while many prioritize enjoying life now. And that’s understandable. The sustainability transition is hard because it challenges our comfort, habits, and current economic models.

 

The Cost of Transition

Companies now face regulatory pressure such as  CSRD (Corporate Sustainability Reporting Directive), DPP (Digital Product Passport), ESG, and more, especially when selling in or to the European market. These shifts aren’t usually driven by passion but by obligation. Transitioning to sustainable business models comes at a cost—learning curves and overheads that don’t align with most corporations’ short-term, profit-driven strategies.

However, we have also seen how long-term visions can be crushed by shareholder demands:

  • Xerox (1970s–1980s) pioneered GUI, the mouse, and Ethernet, but failed to commercialize them. Apple and Microsoft reaped the benefits instead.
  • General Electric under Jeff Immelt tried to pivot to renewables and tech-driven industries. Shareholders, frustrated by slow returns, dismantled many initiatives.

  • My presentation at the 2019 PLM Roadmap / PDT Europe conference – click on the image to get access through SlideShare.

  • Despite ambitious sustainability goals, Siemens faced similar investor pressure, leading to spin-offs like Siemens Energy and Gamesa.

The lesson?

Transforming a business sustainably requires vision, compelling leadership, and patience—qualities often at odds with quarterly profit expectations. I explored these tensions again in my presentation at the PLM Roadmap/PDT Europe 2024 conference, read more here:  Model-Based: The Digital Twin.

I noticed discomfort in smaller, closed-company sessions, some attendees said, “We’re far from that vision. ”

My response: “That’s okay. Sustainability is a generational journey, but it must start now”.

 

Signs of Hope

Now for the good news. In our recent PGGA (PLM Green Global Alliance) meeting, we asked: “Are we tired?” Surprisingly, the mood was optimistic.

Our PGGA core team meeting on June 20th

Yes, some companies are downscaling their green initiatives or engaging in superficial greenwashing. But other developments give hope:

  • China is now the global leader in clean energy investments, responsible for ~37% of the world’s total. In 2023 alone, it installed over 216 GW of solar PV—more than the rest of the world combined—and leads in wind power too. With over 1,400 GW of renewable capacity, China demonstrates that a centralized strategy can overcome investor hesitation.
  • Long-term-focused companies like Iberdrola (Spain), Ørsted (Denmark), Tesla (US), BYD, and CATL (China) continue to invest heavily in EVs and batteries—critical to our shared future.

A Call to Engineers: Design for Sustainability

We may be small at the PLM Green Global Alliance, but we’re committed to educating and supporting the Product Lifecycle Management (PLM) community on sustainability.

That’s why I’m excited to announce the launch of our Design for Sustainability initiative on June 25th.

Led by Eric Rieger and Matthew Sullivan, this initiative will bring together engineers to collaborate and explore sustainable design practices. Whether or not you can attend live, we encourage everyone to engage with the recording afterward.

Conclusion

Sustainability might not dominate headlines today. In fact, there’s a rising tide of misinformation, offering people a “convenient truth” that avoids hard choices. But our work remains urgent. Building a livable planet for future generations requires long-term vision and commitment, even when it is difficult or unpopular.

So, are you tired—or ready to shape the future?

 

 

 

The weekend after the Share PLM Summit 2025

June 2, 2025 in Brain, Change, Customer centric, Data centric, Human Centric, Innovation, PLM | Tags: , , , , | Leave a comment

Wow, what a tremendous amount of impressions to digest when traveling back from Jerez de la Frontera, where Share PLM held its first PLM conference. You might have seen the energy from the messages on LinkedIn, as this conference had a new and unique daring starting point: Starting from human-led transformations.

Look what Jens Chemnitz, Linda Kangastie, Martin Eigner, Jakob Äsell or Oleg Shilovitsky had to say.

For over twenty years, I have attended all kinds of PLM events, either vendor-neutral or from specific vendors. None of these conferences created so many connections between the attendees and the human side of PLM implementation.

We can present perfect PLM concepts, architectures and methodologies, but the crucial success factor is the people—they can make or break a transformative project.

Here are some of the first highlights for those who missed the event and feel sorry they missed the vibe. I might follow up in a second post with more details. And sorry for the reduced quality—I am still enjoying Spain and refuse to use AI to generate this human-centric content.

The scenery

Approximately 75 people have been attending the event in a historic bodega, Bodegas Fundador, in the historic center of Jerez. It is not a typical place for PLM experts, but an excellent place for humans with an Andalusian atmosphere. It was great to see companies like Razorleaf, Technia, Aras, XPLM and QCM sponsor the event, confirming their commitment. You cannot start a conference from scratch alone.

The next great differentiator was the diversity of the audience. Almost 50 % of the attendees were women, all working on the human side of PLM.

Another brilliant idea was to have the summit breakfast in the back of the stage area, so before the conference days started, you could mingle and mix with the people instead of having a lonely breakfast in your hotel.

Now, let’s go into some of the highlights; there were more.

A warm welcome from Share PLM

Beatriz Gonzalez, CEO and co-founder of Share PLM, kicked off the conference, explaining the importance of human-led transformations and organizational change management and sharing some of their best practices that have led to success for their customers.

You might have seen this famous image in the past, explaining why you must address people’s emotions.

 

Working with Design Sprints?

Have you ever heard of design sprints as a methodology for problem-solving within your company? If not, you should read the book by Jake Knapp- Creator of Design Sprint.

Andrea Järvén, program manager at  Tetra Pak and closely working with the PLM team, recommended this to us. She explained how Tetra Pak successfully used design sprints to implement changes. You would use design sprints when development cycles run too looong, Teams lose enthusiasm and focus, work is fragmented, and the challenges are too complex.

Instead of a big waterfall project, you run many small design sprints with the relevant stakeholders per sprint, coming step by step closer to the desired outcome.

The sprints are short – five days of the full commitment of a team targeting a business challenge, where every day has a dedicated goal, as you can see from the image above.

It was an eye-opener, and I am eager to learn where this methodology can be used in the PLM projects I contribute.

Unlocking Success: Building a Resilient Team for Your PLM Journey

Johan Mikkelä from FLSmidth shared a great story about the skills, capacities, and mindset needed for a PLM transformational project.

Johan brought up several topics to consider when implementing a PLM project based on his experiences.

One statement that resonated well with the audience of this conference was:

The more diversified your team is, the faster you can adapt to changes.

He mentioned that PLM projects feel like a marathon, and I believe it is true when you talk about a single project.

However, instead of a marathon, we should approach PLM activities as a never-ending project, but a pleasant journey that is not about reaching a finish but about step-by-step enjoying, observing, and changing a little direction when needed.

 

Strategic Shift of Focus – a human-centric perspective

Besides great storytelling, Antonio Casaschi‘s PLM learning journey at Assa Abloy was a perfect example of why PLM  theory and reality often do not match. With much energy and experience, he came to Assa Abloy to work on the PLM strategy.

He started his PLM strategies top-down, trying to rationalize the PLM infrastructure within Assa Abloy with a historically bad perception of a big Teamcenter implementation from the past. Antonio and his team were the enemies disrupting the day-to-day life of the 200+ companies under the umbrella of Assa Abloy.

A logical lesson learned here is that aiming top-down for a common PLM strategy is impossible in a company that acquires another six new companies per quarter.

His final strategy is a bottom-up strategy, where he and the team listen to and work with the end-users in the native environments. They have become trusted advisors now as they have broad PLM experience but focus on current user pains. With the proper interaction, his team of trusted advisors can help each of the individual companies move towards a more efficient and future-focused infrastructure at their own pace.

The great lessons I learned from Antonio are:

  • If your plan does not work out, be open to failure. Learn from your failures and aim for the next success.
  • Human relations—I trust you, understand you, and know what to do—are crucial in such a complex company landscape.

 

Navigating Change: Lessons from My First Year as a Program Manager

Linda Kangastie from Valmet Technologies Oy in Finland shared her experiences within the company, from being a PLM key user to now being a PLM program manager for the PAP Digi Roadmap, containing PLM, sales tools, installed base, digitalization, process harmonization and change management, business transformation—a considerable scope.

The recommendations she gave should be a checklist for most PLM projects – if you are missing one of them, ask yourself what you are missing:

  1. THE ROADMAP and THE BIG PICTURE – is your project supported by a vision and a related roadmap of milestones to achieve?
  2. Biggest Buy-in comes with money! – The importance of a proper business case describing the value of the PLM activities and working with use cases demonstrating the value.
  3. Identify the correct people in the organization – the people that help you win, find sparring partners in your organization and make sure you have a common language.
  4. Repetition – taking time to educate, learn new concepts and have informal discussions with people –is a continuous process.

As you can see, there is no discussion about technology– it is about business and people.

To conclude, other speakers mentioned this topic too; it is about being honest and increasing trust.

The Future Is Human: Leading with Soul in a World of AI

Helena Guitierez‘s keynote on day two was the one that touched me the most as she shared her optimistic vision of the future where AI will allow us to be so more efficient in using our time, combined, of course, with new ways of working and behaviors.

As an example, she demonstrated she had taken an academic paper from Martin Eigner, and by using an AI tool, the German paper was transformed into an English learning course, including quizzes. And all of this with ½ day compared to the 3 to 4 days it would take the Share PLM team for that.

With the time we save for non-value-added work, we should not remain addicted to passive entertainment behind a flat screen. There is the opportunity to restore human and social interactions in person in areas and places where we want to satisfy our human curiosity.

I agree with her optimism. During Corona and the introduction of teams and Zoom sessions, I saw people become resources who popped up at designated times behind a flat screen.

The real human world was gone, with people talking in the corridors at the coffee machine. These are places where social interactions and innovation happen. Coffee stimulates our human brain; we are social beings, not resources.

 

Death on the Shop Floor: A PLM Murder Mystery

Rob Ferrone‘s theatre play was an original way of explaining and showing that everyone in the company does their best. The product was found dead, and Andrea Järvén alias Angie Neering,  Oleg Shilovitsky alias Per Chasing, Patrick Willemsen alias Manny Facturing, Linda Kangastie alias Gannt Chartman and Antonio Casaschi alias Archie Tect were either pleaded guilty by the public jury or not guilty, mainly on the audience’s prejudices.

You can watch the play here, thanks to Michael Finocchiaro :

According to Rob, the absolute need to solve these problems that allow products to die is the missing discipline of product data people, who care for the flow, speed, and quality of product data. Rob gave some examples of his experience with Quick Release project he had worked with.

My learnings from this presentation are that you can make PLM stories fun, but even more important, instead of focusing on data quality by pushing each individual to be more accurate—it seems easy to push, but we know the quality; you should implement a workforce with this responsibility. The ROI for these people is clear.

Note: I believe that once companies become more mature in working with data-driven tools and processes, AI will slowly take over the role of these product data people.

 

Conclusion

I greatly respect Helena Guitierez and the Share PLM team. I appreciate how they demonstrated that organizing a human-centric PLM summit brings much more excitement than traditional technology—or industry-focused PLM conferences. Starting from the human side of the transformation, the audience was much more diverse and connected.

Closing the conference with a fantastic flamenco performance was perhaps another excellent demonstration of the human-centric approach. The raw performance, a combination of dance, music, and passion, went straight into the heart of the audience – this is how PLM should be (not every day)

There is so much more to share. Meanwhile, you can read more highlights through Michal Finocchiaro’s overview channel here.

 

 

Modern and Intelligent PLM – a dream?

May 12, 2025 in Artificial Intelligence, Blocking change, Change, Data centric, Digital Twin, intelligence, Knowledge Management, model-based, Observations, Platform, PLM | Leave a comment

First, an important announcement. In the last two weeks, I have finalized preparations for the upcoming Share PLM Summit in Jerez on 27-28 May. With the Share PLM team, we have been working on a non-typical PLM agenda. Share PLM, like me, focuses on organizational change management and the HOW of PLM implementations; there will be more emphasis on the people side.

Often, PLM implementations are either IT-driven or business-driven to implement a need, and yes, there are people who need to work with it as the closing topic. Time and budget are spent on technology and process definitions, and people get trained. Often, only train the trainer, as there is no more budget or time to let the organization adapt, and rapid ROI is expected.

This approach neglects that PLM implementations are enablers for business transformation. Instead of doing things slightly more efficiently, significant gains can be made by doing things differently, starting with the people and their optimal new way of working, and then providing the best tools.

The conference aims to start with the people, sharing human-related experiences and enabling networking between people – not only about the industry practices (there will be sessions and discussions on this topic too).

If you are curious about the details, listen to the podcast recording we published last week to understand the difference – click on the image on the left.

And if you are interested and have the opportunity, join us and meet some great thought leaders and others with this shared interest.

 

Why is modern PLM a dream?

If you are connected to the LinkedIn posts in my PLM feed, you might have the impression that everyone is gearing up for modern PLM. Articles often created with AI support spark vivid discussions. Before diving into them with my perspective, I want to set the scene by explaining what I mean by modern PLM and traditional PLM.

Traditional PLM

Traditional PLM is often associated with implementing a PLM system, mainly serving engineering. Downstream engineering data usage is usually pushed manually or through interfaces to other enterprise systems, like ERP, MES and service systems.

Traditional PLM is closely connected to the coordinated way of working: a linear process based on passing documents  (drawings) and datasets (BOMs). Historically, CAD integrations have been the most significant characteristic of these systems.

The coordinated approach fits people working within their authoring tools and, through integrations, sharing data. The PLM system becomes a system of record, and working in a system of record is not designed to be user-friendly.

Unfortunately, most PLM implementations in the field are based on this approach and are sometimes characterized as advanced PDM.

You recognize traditional PLM thinking when people talk about the single source of truth.

Modern PLM

When I talk about modern PLM, it is no longer about a single system. Modern PLM starts from a business strategy implemented by a data-driven infrastructure. The strategy part remains a challenge at the board level: how do you translate PLM capabilities into business benefits – the WHY?

More on this challenge will be discussed later, as in our PLM community, most discussions are IT-driven: architectures, ontologies, and technologies – the WHAT.

For the WHAT, there seems to be a consensus that modern PLM is based on a federated

I think this article from Oleg Shilovitsky, “Rethinking PLM: Is It Time to Move Beyond the Monolith?“ AND the discussion thread in this post is a must-read. I will not quote the content here again.

After reading Oleg’s post and the comments, come back here

 

The reason for this approach: It is a perfect example of the connected approach. Instead of collecting all the information inside one post (book ?), the information can be accessed by following digital threads. It also illustrates that in a connected environment, you do not own the data; the data comes from accountable people.

Building such a modern infrastructure is challenging when your company depends mainly on its legacy—the people, processes and systems. Where to change, how to change and when to change are questions that should be answered at the top and require a vision and evolutionary implementation strategy.

A company should build a layer of connected data on top of the coordinated infrastructure to support users in their new business roles. Implementing a digital twin has significant business benefits if the twin is used to connect with real-time stakeholders from both the virtual and physical worlds.

But there is more than digital threads with real-time data. On top of this infrastructure, a company can run all kinds of modeling tools, automation and analytics. I noticed that in our PLM community, we might focus too much on the data and not enough on the importance of combining it with a model-based business approach. For more details, read my recent post: Model-based: the elephant in the room.

Again, there are no quotes from the article; you know how to dive deeper into the connected topic.

Despite the considerable legacy pressure there are already companies implementing a coordinated and connected approach. An excellent description of a potential approach comes from Yousef Hooshmand‘s paper:  From a Monolithic PLM Landscape to a Federated Domain and Data Mesh.

You might recognize modern PLM thinking when people talk about the nearest source of truth and the single source of change.

 

Is Intelligent PLM the next step?

So far in this article, I have not mentioned AI as the solution to all our challenges. I see an analogy here with the introduction of the smartphone. 2008 was the moment that platforms were introduced, mainly for consumers. Airbnb, Uber, Amazon, Spotify, and Netflix have appeared and disrupted the traditional ways of selling products and services.

The advantage of these platforms is that they are all created data-driven, not suffering from legacy issues.

In our PLM domain, it took more than 10 years for platforms to become a topic of discussion for businesses. The 2015 PLM Roadmap/PDT conference was the first step in discussing the Product Innovation Platform – see my The Weekend after PDT 2015 post.

At that time, Peter Bilello shared the CIMdata perspective, Marc Halpern (Gartner) showed my favorite positioning slide (below), and Martin Eigner presented, according to my notes, this digital trend in PLM in his session:” What becomes different for PLM/SysLM?”

2015 Marc Halpern – the Product Innovation Platform (PIP)

While concepts started to become clearer, businesses mainly remained the same. The coordinated approach is the most convenient, as you do not need to reshape your organization. And then came the LLMs that changed everything.

Suddenly, it became possible for organizations to unlock knowledge hidden in their company and make it accessible to people.

Without drastically changing the organization, companies could now improve people’s performance and output (theoretically); therefore, it became a topic of interest for management. One big challenge for reaping the benefits is the quality of the data and information accessed.

I will not dive deeper into this topic today, as Benedict Smith, in his article Intelligent PLM – CFO’s 2025 Vision, did all the work, and I am very much aligned with his statements. It is a long read (7000 words) and a great starting point for discovering the aspects of Intelligent PLM and the connection to the CFO.

 

You might recognize intelligent  PLM thinking when people and AI agents talk about the most likely truth.

 

Conclusion

Are you interested in these topics and their meaning for your business and career? Join me at the Share PLM conference, where I will discuss “The dilemma: Humans cannot transform—help them!” Time to work on your dreams!

 

Join us to discuss the (Intelligent) PLM dream

Join us in Design for Sustainability meetings!

April 14, 2025 in Circulair Economy, Design for Sustainabillity, Green Development, Innovation, Life Cycle Assessment, Material Compliance, Modularity, PLM, Sustainability | 1 comment

Within the PLM Green Global Alliance (PGGA), we had an internal kick-off meeting related to the topic of Design for Sustainability. As you might have seen on our website,  Erik Rieger, PLM Evangelist and now working for PTC, took the initiative to start this focus group.

You might know Erik from a previous interview from the PGGA where we discussed TTPSC’s ecoPLM offering based on Windchill: PLM and Sustainability: talking about ecoPLM.

When Erik announced the Design for Sustainability initiative, it was Matthew Sullivan from CIMPA PLM Service who immediately contacted Erik to work together on this initiative.

And again, you might know CIMPA PLM services from our recent interview with them related to regulations and best practices related to sustainability in the aerospace industry (CSRD, LCA, DPP, AI and more):  PLM and Sustainability: talking with CIMPA.

Erik and Matthew decided to participate in an introductory interview, during which they shared their background, passion, and goals related to Design for Sustainability.

Watch the episode here:

 

Why Design for Sustainability?

Design for Sustainability (DfS) is an approach to designing products, services, systems, and experiences that prioritize environmental, social, and economic sustainability throughout their entire lifecycle. It means creating things in a way that reduces negative impacts on the planet and people while still being functional, profitable, and desirable.

In theory, this should be one of the key areas in which our PGGA members can have a common discussion.

As Erik mentions, it is estimated that 80 % of the environmental impact is defined during the design phase. This is a number that has been coming back in several of our PGGA discussions with all the other software vendors.

 

More on Design for Sustainability

Just after the recording, Dave Duncan, head of Sustainability at PTC, published the eBook Product Sustainability for Dummies. An excellent book that brings all aspects of sustainability and products together in an easy-to-digest manner. There is also a chapter on Design for Sustainability in the eBook.

Note: Dave Duncan is a recognized PGGA leader in PLM and Sustainability, as we reported last year.

Read the post here: Leaders in PLM and Sustainability – December 2024

 

A call for action

We hope you watched and enjoyed the interview with Erik and Matthew as an inspiration to become active in this Design for Sustainability discussion group.

The intention is, as mentioned, to share experiences and discuss challenges within the group. It will be a private group where people can discuss openly to avoid any business conflicts. The plan is to start with an initial kick-off Zoom meeting in June the date still to be fixed.

If you are interested in joining this exciting discussion group, please contact Erik Rieger, who will be the focal point for this group. We are looking forward to your contribution, and now is the time to prepare and act.

Join us in the discussion

 

 

 

PLM, value or cost?

April 7, 2025 in Artificial Intelligence, Blocking change, Change, Digital Enterprise, Education, Innovation, Observations, PLM, ROI on PLM, Storytelling | Tags: , , , , , , | Leave a comment

In the last two weeks, I have had mixed discussions related to PLM, where I realized the two different ways people can look at PLM. Are implementing PLM capabilities driven by a cost-benefit analysis and a business case? Or is implementing PLM capabilities driven by strategy providing business value for a company?

Most companies I am working with focus on the first option – there needs to be a business case.

This observation is a pleasant passageway into a broader discussion started by Rob Ferrone recently with his article Money for nothing and PLM for free. He explains the PDM cost of doing business, which goes beyond the software’s cost. Often, companies consider the other expenses inescapable.

At the same time, Benedict Smith wrote some visionary posts about the potential power of an AI-driven PLM strategy, the most recent article being PLM augmentation – Panning for Gold.

It is a visionary article about what is possible in the PLM space (if there was no legacy ☹), based on Robust Reasoning and how you could even start with LLM Augmentation for PLM “Micro-Tasks.

Interestingly, the articles from both Rob and Benedict were supported by AI-generated images – I believe this is the future: Creating an AI image of the message you have in mind.

When you have digested their articles, it is time to dive deeper into the different perspectives of value and costs for PLM.

 

From a system to a strategy

The biggest obstacle I have discovered is that people relate PLM to a system or, even worse, to an engineering tool. This 20-year-old misunderstanding probably comes from the fact that in the past, implementing PLM was more an IT activity – providing the best support for engineers and their data – than a business-driven set of capabilities needed to support the product lifecycle.

 

The System approach

Traditional organizations are siloed, and initially, PLM always had the challenge of supporting product information shared throughout the whole lifecycle, where there was no conventional focus per discipline to invest in sharing – every discipline has its P&L – and sharing comes with a cost.

At the management level, the financial data coming from the ERP system drives the business. ERP systems are transactional and can provide real-time data about the company’s performance. C-level management wants to be sure they can see what is happening, so there is a massive focus on implementing the best ERP system.

In some cases, I noticed that the investment in ERP was twenty times more than the PLM investment.

Why would you invest in PLM? Although the ERP engine will slow down without proper PLM, the complexity of PLM compared to ERP is a reason for management to look at the costs, as the PLM benefits are hard to grasp and depend on so much more than just execution.

See also my old 2015 article:   How do you measure collaboration?

As I mentioned, the Cost of Non-Quality, too many iterations, time lost by searching, material scrap, manufacturing delays or customer complaints – often are considered inescapable parts of doing business (like everyone else) – it happens all the time..

The strategy approach

It is clear that when we accept the modern definition of PLM, we should be considering product lifecycle management as the management of the product lifecycle (as Patrick Hillberg says eloquently in our Share PLM podcast – see the image at the bottom of this post, too).

When you implement a strategy, it is evident that there should be a long(er) term vision behind it, which can be challenging for companies. Also, please read my previous article: The importance of a (PLM) vision.

I cannot believe that, although perhaps not fully understood, the importance of a data-driven approach will be discussed at many strategic board meetings. A data-driven approach is needed to implement a digital thread as the foundation for enhanced business models based on digital twins and to ensure data quality and governance supporting AI initiatives.

It is a process I have been preaching: From Coordinated to Coordinated and Connected.

We can be sure that at the board level, strategy discussions should be about value creation, not about reducing costs or avoiding risks as the future strategy.

 

Understanding the (PLM) value

The biggest challenge for companies is to understand how to modernize their PLM infrastructure to bring value.

* Step 1 is obvious. Stop considering PLM as a system with capabilities, but investigate how you transform your infrastructure from a collection of systems and (document)  interfaces towards a federated infrastructure of connected tools.

Note: the paradigm shift from a Single Source of Truth (in my system) towards a Nearest Source of Truth and a Single Source of Change.

 

* Step 2 is education. A data-driven approach creates new opportunities and impacts how companies should run their business. Different skills are needed, and other organizational structures are required, from disciplines working in siloes to hybrid organizations where people can work in domain-driven environments (the Systems of Record) and product-centric teams (the System of Engagement). AI tools and capabilities will likely create an effortless flow of information within the enterprise.

* Step 3 is building a compelling story to implement the vision.   Implementing new ways of working based on new technical capabilities requires also organizational change. If your organization keeps working similarly, you might gain some percentage of efficiency improvements.

The real benefits come from doing things differently, and technology allows you to do it differently. However, this requires people to work differently, too, and this is the most common mistake in transformational projects.

Companies understand the WHY and WHAT but leave the HOW to the middle management.

People are squeezed into an ideal performance without taking them on the journey. For that reason, it is essential to build a compelling story that motivates individuals to join the transformation. Assisting companies in building compelling story lines is one of the areas where I specialize.

Feel free to contact me to explore the opportunity for your business.

It is not the technology!

With the upcoming availability of AI tools, implementing a PLM strategy will no longer depend on how IT understands the technology, the systems and the interfaces needed.

As Yousef Hooshmand‘s  above image describes, a federated infrastructure of connected (SaaS) solutions will enable companies to focus on accurate data (priority #1) and people creating and using accurate data (priority #1). As you can see, people and data in modern PLM are the highest priority.

Therefore, I look forward to participating in the upcoming Share PLM Summit on 27-28 May in Jerez.

It will be a breakthrough – where traditional PLM conferences focus on technology and best practices. This conference will focus on how we can involve and motivate people. Regardless of which industry you are active in, it is a universal topic for any company that wants to transform.

 

Conclusion

Returning to this article’s introduction, modern PLM is an opportunity to transform the business and make it future-proof. It needs to be done for sure now or in the near future. Therefore PLM initiatives should be considered from the value point first instead of focusing on the costs.  How well are you connected to your management’s vision to make PLM a value discussion?

Enjoy the podcast – several topics discuss relate to this post.

 

 

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  1. Oleg Shilovitsky's avatar

    Hi Jos, Knowing your background in methodology and education, I wanted to share a longer article with you: “What is…

  2. Bart Willemsen's avatar

    Interesting reflection, Jos. In my experience, the situation you describe is very recognizable. At the company where I work, sustainability…

  4. Unknown's avatar
  5. Håkan Kårdén's avatar

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