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.

 

 

The importance of a (PLM) vision

March 17, 2025 in Artificial Intelligence, Blocking change, Data centric, Knowledge Management, PLM | Tags: , , | Leave a comment

In the last two weeks, I had some interesting observations and discussions related to the need to have a (PLM) vision. I placed the word PLM between brackets, as PLM is no longer an isolated topic in an organization. A PLM strategy should align with the business strategy and vision.

To be clear, if you or your company wants to survive in the future, you need a sustainable vision and a matching strategy as the times they are a changing, again!

I love the text: “Don’t criticize what you can’t understand” – a timeless quote.

 

First, there was Rob Ferrone’s article: Multi-view. Perspectives that shape PLM – a must-read to understand who to talk to about which dimension of PLM – and it is worth browsing through the comments too – there you will find the discussions, and it helps you to understand the PLM players.

Note: it is time that AI-generated images become more creative 😉

Next, there is still the discussion started by Gareth Webb, Digital Thread and the Knowledge Graph, further stirred by Oleg Shilovitsky.

Based on the likes and comments, it is clearly a topic that creates interaction – people are thinking and talking about it – the Digital Thread as a Service.

One of the remaining points in this debate is still the HOW and WHEN companies decide to implement a Digital Thread, a Knowledge Graph and other modern data concepts.

So far my impression is that most companies implement their digital enhancements (treads/graphs) in a bottom-up approach, not driven by a management vision but more like band-aids or places where it fits well, without a strategy or vision.

The same week, we, Beatriz Gonzáles and I, recorded a Share PLM podcast session with Paul Kaiser from MHP Americas as a guest. Paul is the head of the Digital Core & Technology department, where he leads management and IT consulting services focused on end-to-end business transformation.

During our discussion, Paul mentioned the challenge in engagements when the company has no (PLM) vision. These companies expect external consultants to formulate and implement the vision – a recipe for failure due to wrong expectations.

The podcast can be found HERE , and the session inspired me to write this post.

“We just want to be profitable“.

I believe it is a typical characteristic of small and medium enterprises that people are busy with their day-to-day activities. In addition, these companies rarely appoint new top management, which could shake up the company in a positive direction. These companies evolve …..

You often see a stable management team with members who grew up with the company and now monitor and guide it, watching its finances and competition. They know how the current business is running.

Based on these findings, there will be classical efficiency plans, i.e., cutting costs somewhere, dropping some non-performing products, or investing in new technology that they cannot resist. Still, minor process changes and fundamental organizational changes are not expected.

Most of the time, the efficiency plans provide single-digit benefits.

Everyone is happy when the company feels stable and profitable, even if the margins are under pressure. The challenge for this type of company without a vision is that they navigate in the dark when the outside world changes – like nowadays.

 

The world is changing drastically.

Since 2014, I have advocated for digital transformation in the PLM domain and explained it simply using the statement: From Coordinated to Connected, which already implies much complexity.

Moving from document/files to datasets and models, from a linear delivery model to a DevOps model, from waterfall to agile and many other  From-To statements.

Moving From-To is a transformational journey, which means you will learn and adapt to new ways of working during the journey. Still, the journey should have a target, directed by a vision.

However, not many companies have started this journey because they just wanted to be profitable.

“Why should we go in an unknown direction?”

With the emergence of sustainability regulations, e.g., GHG and ESG reporting, carbon taxes, material reporting, and the Digital Product Passport, which goes beyond RoHS and REACH and applies to much more industries, there came the realization that there is a need to digitize the product lifecycle processes and data beyond documents. Manual analysis and validation are too expensive and unreliable.

At this stage, there is already a visible shift between companies that have proactively implemented a digitally connected infrastructure and companies that still see compliance with regulations as an additional burden. The first group brings products to the market faster and more sustainably than the second group because sustainability is embedded in their product lifecycle management.

And just when companies felt they could manage the transition from Coordinated to Coordinated and Connected, there was the fundamental disruption of embedded AI in everything, including the PLM domain.

  • Large Language Models LLMs can go through all the structured and unstructured data, providing real-time access to information, which would take experts years of learning. Suddenly, everyone can behave experienced.
  • The rigidness of traditional databases can be complemented by graph databases, which visualize knowledge that can be added and discovered on the fly without IT experts. Suddenly, an enterprise is no longer a collection of interfaced systems but a digital infrastructure where data flows – some call it Digital Thread as a Service (DTaaS)
  • Suddenly, people feel overwhelmed by complexity, leading to fear and doing nothing, a killing attitude.

2014 The Economist – the onrushing wave

I cannot predict what will happen in the next 5 to 10 years, but I am sure the current change is one we have never seen before. Be prepared and flexible to act—to be on top of the wave, you need the skills to get there.

 

Building the vision

The image below might not be new to you, but it illustrates how companies could manage a complex change.

I will focus only on the first two elements, Vision and Skills, as they are the two elements we as individuals can influence. The other elements are partly related to financial and business constraints.

Vision and Skills are closely related because you can have a fantastic vision. Still, to realize the vision, you need a strategy driven by relevant skills to define and implement the vision. With the upcoming AI, traditional knowledge-based skills will suddenly no longer be a guarantee for future jobs.

AI brings a new dimension for everyone working in a company. To remain relevant, you must develop your unique human skills that make you different from robots or libraries. The importance of human skills might not be new, but now it has become apparent with the explosion of available AI tools.

Look at this 2013 table about predicted skills for the future – You can read the details in their paper, The Future of Employment, by Carl Benedikt Frey & Michael Osborne(2013)  – click on the image to see the details.

In my 2015 PLM lectures, I joked when showing this image that my job as a PLM coach was secured, because you are a recreational therapist and firefighter combined.

It has become a reality, and many of my coaching engagements nowadays focus on explaining and helping companies formulate and understand their possible path forward. Helping them align and develop a vision of progressing in a volatile world – the technology is there, the skills and the vision are often not yet there.

Combining business strategy with in-depth PLM concepts is a relatively unique approach in our domain. Many of my peers have other primary goals, such as Rob Ferrone’s article: Multi-view. Perspectives that shape PLM explains.

And then there is …..

The Share PLM Summit 2025

Modern times need new types of information building and sharing, and therefore, I am eager to participate in the upcoming Share PLM Summit at the end of May in Jerez (Spain).

See the link to the event here: The Share PLM Summit 2025 – with the theme: Where People Take Center Stage to Drive Human-Centric Transformations in PLM and Lead the Future of Digital Innovation.

In my lecture, I will focus on how humans can participate in/anticipate this digital AI-based transformation. But even more, I look forward to the lectures and discussions with other peers, as more people-centric thought leaders and technology leaders will join us:

Quoting Oleg Shilovitsky:

PLM was built to manage data, but too often, it makes people work for the data instead of working the other way around. At Share PLM Summit 2025, I’ll discuss how PLM must evolve from rigid, siloed systems to intelligent, connected, and people-centric data architectures.

We need both, and I hope to see you at the end of May at this unique PLM conference.

Conclusion

We are at a decisive point of the digital transformation as AI will challenge people skills, knowledge and existing ways of working.  Combined with a turbulent world order, we need to prepare to be flexible and resilient. Therefore instead of focusing on current best practices we need to prepare for the future – a vision developed by skilled people. How will you or your company work on that? Join us if you have questions or ideas.

 

Model-based – the elephant in the room

March 3, 2025 in Artificial Intelligence, Digital Enterprise, digital thread, MBD, MBSE, model-based, PLM | Tags: , , , | Leave a comment

Four years ago, I wrote a series of posts with the common theme: The road to model-based and connected PLM. I discussed the various aspects of model-based and the transition from considering PLM as a system towards considering PLM as a strategy to implement a connected infrastructure.

Since then, a lot has happened. The terminology of Digital Twin and Digital Thread has become better understood. The difference between Coordinated and Connected ways of working has become more apparent. Spoiler: You need both ways. And at this moment, Artificial Intelligence (AI) has become a new hype.

Many current discussions in the PLM domain are about structures and data connectivity, Bills of Materials (BOM), or Bills of Information(BOI)  combined with the new term Digital Thread as a Service (DTaaS) introduced by Oleg Shilovitsky and Rob Ferrone. Here, we envision a digitally connected enterprise, based connected services.

 

A lot can be explored in this direction; also relevant Lionel Grealou’s article in Engineering.com: RIP SaaS, long live AI-as-a-service and follow-up discussions related tot his topic. I chimed in with Data, Processes and AI.

 

However, we also need to focus on the term model-based or model-driven. When we talk about models currently, Large Language Models (LMM) are the hype, and when you are working in the design space, 3D CAD models might be your first association.

There is still confusion in the PLM domain: what do we mean by model-based, and where are we progressing with working model-based?

A topic I want to explore in this post.

 

It is not only Model-Based Definition (MBD)

Before I started The Road to Model-Based series, there was already the misunderstanding that model-based means 3D CAD model-based. See my post from that time: Model-Based – the confusion.

Model-Based Definition (MBD) is an excellent first step in understanding information continuity, in this case primarily between engineering and manufacturing, where the annotated model is used as the source for manufacturing.

In this way, there is no need for separate 2D drawings with manufacturing details, reducing the extra need to keep the engineering and manufacturing information in sync and, in addition, reducing the chance of misinterpretations.

MBD is a common practice in aerospace and particularly in the automotive industry. Other industries are struggling to introduce MBD, either because the OEM is not ready or willing to share information in a different format than 3D + 2D drawings, or their supplier consider MBD too complex for them compared to their current document-driven approach.

In its current practice, we must remember that MBD is part of a coordinated approach.

Companies exchange technical data packages based on potential MBD standards (ASME Y14.47 /ISO 16792 but also JT and 3D PDF). It is not yet part of the connected enterprise, but it connects engineering and manufacturing using the 3D Model as the core information carrier.

As I wrote, learning to work with MBD is a stepping stone in understanding a modern model-based and data-driven enterprise. See my 2022 post: Why Model-based Definition is important for us all.

To conclude on MBD, Model-based definition is a crucial practice to improve collaboration between engineering, manufacturing, and suppliers, and it might be parallel to collaborative BOM structures.

And it is transformational as the following benefits are reported through ChatGPT:

  • Up to 30% faster in product development cycles due to reduced need for 2D drawings and fewer design iterations. Boeing reported a 50% reduction in engineering change requests by using MBD.

  • Companies using MBD see a 20–50% reduction in manufacturing errors caused by misinterpretations of 2D drawings. Caterpillar reported a 30% improvement in first-pass yield due to better communication between design and manufacturing teams.
  • MBD can reduce product launch time by 20–50% by eliminating bottlenecks related to traditional drawings and manual data entry.
  • 20–30% reduction in documentation costs by eliminating or reducing 2D drawings. Up to 60% savings on rework and scrap costs by reducing errors and inconsistencies.

Over five years, Lockheed Martin achieved a $300 million cost savings by implementing MBD across parts of its supply chain.

 

MBSE is not a silo.

The MBSE playground

For many people, Model-Based Systems Engineering(MBSE) seems to be something not relevant to their business, or it is a discipline for a small group of specialists that are conducting system engineering practices, not in the traditional document-driven V-shape approach but in an iterative process following the V-shape, meanwhile using models to predict and verify assumptions.

And what is the value connected in a PLM environment?

A quick heads up – what is a model

A  model is a simplified representation of a system, process, or concept used to understand, predict, or optimize real-world phenomena. Models can be mathematical, computational, or conceptual. 

We need models to: 

  • Simplify Complexity – Break down intricate systems into manageable components and focus on the main components.
  • Make Predictions – Forecast outcomes in science, engineering, and economics by simulating behavior – Large Language Models, Machine Learning. 
  • Optimize Decisions – Improve efficiency in various fields like AI, finance, and logistics by running simulations and find the best virtual solution to apply.
  • Test Hypotheses – Evaluate scenarios without real-world risks or costs for example a virtual crash test..

It is important to realize models are as accurate as the data elements they are running on – every modeling practices has a certain need for base data, be it measurements, formulas, statistics.

I watched and listened to the interesting podcast below, where Jonathan Scott and Pat Coulehan discuss this topic: Bridging MBSE and PLM: Overcoming Challenges in Digital Engineering. If you have time – watch it to grasp the challenges.

The challenge in an MBSE environment is that it is not a single tool with a single version of the truth; it is merely a federated environment of shared datasets that are interpreted by modeling applications to understand and define the behavior of a product.

In addition, an interesting article from Nicolas Figay might help you understand the value for a broader audience. Read his article:  MBSE: Beyond Diagrams – Unlocking Model Intelligence for Computer-Aided Engineering.

Ultimately, and this is the agreement I found on many PLM conferences, we agree that MBSE practices are the foundation for downstream processes and operations.

We need a data-driven modeling environment to implement Digital Twins, which can span multiple systems and diagrams.

In this context, I like the Boeing diamond presented by Don Farr at the 2018 PLM Roadmap EMEA conference. It is a model view of a system, where between the virtual and the physical flow, we will have data flowing through a digital thread.

Where this image describes a model-based, data-driven infrastructure to deliver a solution, we can, in addition, apply the DevOp approach to the bigger picture for solutions in operation, as depicted by the PTC image below.

 

Model-based the foundation of the digital twins

To conclude on MBSE, I hope that it is clear why I am promoting considering MBSE not only as the environment to conceptualize a solution but also as the foundation for a digital enterprise where information is connected through digital threads and AI models (**new**)

The data borders between traditional system domains will disappear – the single source of change and the nearest source of truth – paradigm, and this post, The Big  Blocks of Future Lifecycle Management, from Prof. Dr. Jörg Fischer, are all about data domains.

However, having accessible data using all kinds of modern data sources and tools are necessary to build digital twins – either to simulate and predict a physical solution or to analyze a physical solution and, based on the analysis, either adjust the solutions or improve your virtual simulations.

Digital Twins at any stage of the product life cycle are crucial to developing and maintaining sustainable solutions, as I discussed in previous lectures. See the image below:

Conclusion

Data quality and architecture are the future of a modern digital enterprise – the building blocks. And there is a lot of discussion related to Artificial Intelligence. This will only work when we master the methodology and practices related to a data-driven and sustainable approach using models. MBD is not new, MBSE perhaps still new, building blocks for a model-based approach. Where are you in your lifecycle?

 

 

 

Modularity – a need for the business and the future

February 18, 2025 in Change, Circulair Economy, Data centric, Green Development, Modularity, PLM, Sustainability | Tags: , , , , | Leave a comment

In my business ecosystem, I have seen a lot of discussions about technical and architectural topics since last year that are closely connected to the topic of artificial intelligence. We are discussing architectures and solutions that will make our business extremely effective. The discussion is mostly software vendor-driven as vendors usually do not have to deal with the legacy, and they can imagine focusing on the ultimate result.

Legacy (people, skills, processes and data) is the mean inhibitor for fast forward in such situations, as I wrote in my previous post: Data, Processes and AI.

However, there are also less visible discussions about business efficiency – methodology and business models – and future sustainability.

These discussions are more challenging to follow as you need a broader and long-term vision, as implementing solutions/changes takes much longer than buying tools.

This time, I want to revisit the discussion on modularity and the need for business efficiency and sustainability.

 

Modularity – what is it?

Modularity is a design principle that breaks a system into smaller, independent, and interchangeable components, or modules, that function together as a whole. Each module performs a specific task and can be developed, tested, and maintained separately, improving flexibility and scalability.

Modularity is a best practice in software development. Although modular thinking takes a higher initial effort, the advantages are enormous for reuse, flexibility, optimization, or adding new functionality. And as software code has no material cost or scrap, modular software solutions excel in delivery and maintenance.

In the hardware world, this is different. Often, companies have a history of delivering a specific (hardware) solution, and the product has been improved by adding features and options where the top products remain the company’s flagships.

Modularity enables easy upgrades and replacements in hardware and engineering, reducing costs and complexity. As I work mainly with manufacturing companies in my network, I will focus on modularity in the hardware world.

 

Modularity – the business goal

How often have you heard that a business aims to transition from Engineering to Order (ETO) to Configure/Build to Order (BTO) or Assemble to Order (ATO)? Companies often believe that the starting point of implementing a PLM system is enough, as it will help identify commonalities in product variations, therefore leading to more modular products.

The primary targeted business benefits often include reduced R&D time and cost but also reduced risk due to component reuse and reuse of experience. However, the ultimate goal for CTO/ATO companies is to minimize R&D involvement in their sales and delivery process.

More options can be offered to potential customers without spending more time on engineering.

Four years ago, I discussed modularity with Björn Eriksson and Daniel Strandhammar, who wrote “The Modular Way” during the COVID-19 pandemic. I liked the book because it is excellent for understanding the broader scope of modularity along with marketing, sales, and long-term strategy. Each business type has its modularity benefits.

I had a follow-up discussion with panelists active in modularization and later with Daniel Strandhammar about the book’s content in this blog post: PLM and Modularity.

 

Next, I got involved with the North European Modularity Network (NEM) group, a group of Scandinavian companies that share modularization experiences and build common knowledge.

Historically, modularization has been a popular topic in North Europe, and meanwhile, the group is expanding beyond Scandinavia. Participants in the group focus on education-sharing strategies rather than tools.

The 2023 biannual meeting  I attended hosted by Vestas in Ringkobing was an eye-opener for me.

We should work more integrated, not only on the topic of Modularity and PLM but also on a third important topic: Sustainability in the context of the Circular Economy.

You can review my impression of the event and presentation in my post: “The week after North European Modularity (NEM)“

That post concludes that Modularity, like PLM, is a strategy rather than an R&D mission. Integrating modularity topics into PLM conferences or Circular Economy events would facilitate mutual learning and collaboration.

 

Modularity and Sustainability

The PLM Green Global Alliance started in 2020 initially had few members. However, after significant natural disasters and the announcement of regulations related to the European Green Deal, sustainability became a management priority. Greenwashing was no longer sufficient.

One key topic discussed in the PLM Green Global Alliance is the circular economy moderated by CIMPA PLM services. The circular economy is crucial as our current consumption of Earth’s resources is unsustainable.

The well-known butterfly diagram from the Ellen MacArthur Foundation below, illustrates the higher complexity of a circular economy, both for the renewables (left) and the hardware (right)

In a circular economy, modularity is essential. The SHARE loop focuses on a Product Service Model, where companies provide services based on products used by different  users. This approach requires a new business model, customer experience, and durable hardware. After Black Friday last year, I wrote about this transition: The Product Service System and a Circular Economy.

Modularity is vital in the MAINTAIN/PROLONG loop. Modular products can be upgraded without replacing the entire product, and modules are easier to repair. An example is Fairphone from the Netherlands, where users can repair and upgrade their smartphones, contributing to sustainability.

In the REUSE/REMANUFACTURE loop, modularity allows for reusing hardware parts when electronics or software components are upgraded. This approach reduces waste and supports sustainability.

The REFURBISH/REMANUFACTURE loop also benefits from modularity, though to a lesser extent. This loop helps preserve scarce materials, such as batteries, reducing the need for resource extraction from places like the moon, Mars, or Greenland.

A call for action

If you reached this point of the article, my question is now to reflect on your business or company. Modularity is, for many companies, a dream (or vision) and will become, for most companies, a must to provide a sustainable business.

Modularity does not depend on PLM technology, as famous companies like Scania, Electrolux and Vestas have shown (in my reference network).

Where is your company and its business offerings?

IMPORTANT:

If you aim to implement modularity to support the concepts of the Circular Economy, make sure you do it in a data-driven, model-based environment – here, technology counts.

 

Conclusion

Don’t miss the focus on the potential relevance of modularity for your company. Modularity improves business and sustainability, AND it touches all enterprise stakeholders. Technology alone will not save the business. Your thoughts?

Do you want to learn more about implementing PLM at an ETO space company?
Listen to our latest podcast: OHB’s Digital Evolution: Transforming Aerospace PLM with Lucía Núñez Núñez

Data, Processes and AI

February 4, 2025 in Artificial Intelligence, Data centric, digital thread, Digital Twin, Minimum Viable Product, PLM | Tags: , , , , , | Leave a comment

Last week, my memory was triggered by this LinkedIn post and discussion started by Oleg Shilovitsky: Rethinking the Data vs. Process Debate in the Age of Digital Transformation and AI.

me, 1989

In the past twenty years, the debate in the PLM community has changed a lot. PLM started as a central file repository, combined with processes to ensure the correct status and quality of the information.

Then, digital transformation in the PLM domain became achievable and there was a focus shift towards (meta)data. Now, we are entering the era of artificial intelligence, reshaping how we look at data.

In this technology evolution, there are lessons learned that are still valid for 2025, and I want to share some of my experiences in this post.

In addition, it was great to read Martin Eigner’s great reflection on the past 40 years of PDM/PLM. Martin shared his experiences and insights, not directly focusing on the data and processes debate, but very complementary and helping to understand the future.

 

It started with processes (for me 2003-2014)

In the early days when I worked with SmarTeam, one of my main missions was to develop templates on top of the flexible toolkit SmarTeam.

For those who do not know SmarTeam, it was one of the first Windows PDM/PLM systems, and thanks to its open API (COM-based), companies could easily customize and adapt it. It came with standard data elements and behaviors like Projects, Documents (CAD-specific and Generic), Items and later Products.

On top of this foundation, almost every customer implemented their business logic (current practices).

And there the problems came …..

The implementations became too much a highly customized environment, not necessarily thought-through as every customer worked differently based on their (paper) history. Thanks to learning from the discussions in the field supporting stalled implementations, I was also assigned to develop templates (e.g. SmarTeam Design Express)  and standard methodology (the FDA toolkit), as the mid-market customers requested. The focus was on standard processes.

You can read my 2009 observations here: Can chaos become order through PLM?

 

The need for standardization?

When developing templates (the right data model and processes), it was also essential to provide template processes for releasing a product and controlling the status and product changes – from Engineering Change Request to Engineering Change Order. Many companies had their processes described in their ISO 900x manual, but were they followed correctly?

In 2010, I wrote  ECR/ECO for Dummies, and it has been my second most-read post over the years. Only the 2019  post The importance of EBOM and MBOM in PLM (reprise) had more readers. These statistics show that many people are, and were, seeking education on general PLM processes and data model principles.

It was also the time when the PLM communities discussed out-of-the-box or flexible processes as Oleg referred to in his post..

 

You would expect companies to follow these best practices, and many small and medium enterprises that started with PLM did so. However, I discovered there was and still is the challenge with legacy (people and process), particularly in larger enterprises.

 

The challenge with legacy

The technology was there, the usability was not there. Many implementations of a PLM system go through a critical stage. Are companies willing to change their methodology and habits to align with common best practices, or do they still want to implement their unique ways of working (from the past)?

“The embedded process is limiting our freedom, we need to be flexible”

is an often-heard statement. When every step is micro-managed in the PLM system, you create a bureaucracy detested by the user.  In general, when the processes are implemented in a way first focusing on crucial steps with the option to improve later,  you will get the best results and acceptance. Nowadays, we could call it an MVP approach.

I have seen companies that created a task or issue for every single activity a person should do. Managers loved the (demo) dashboard. It never lead to success as the approach created frustration at the end user level as their To-Do list grew and grew.

 

Another example of the micro-management mindset is when I worked with a company that had the opposite definition of Version and Revision in their current terminology. Initially, they insisted that the new PLM system should support this, meaning everywhere in the interface where Revisions was mentioned should be Version and the reverse for Version and Revision.

Can you imagine the cost of implementing and maintaining this legacy per upgrade?

 

And then came data (for me 2014 – now)

In 2015, during the pivotal PLM Roadmap/PDT conference related to Product Innovation Platforms, it brought the idea of framing digital transformation in the PLM domain in a single sentence: From Coordinated to Connected. See the original image from Marc Halpern here below and those who have read my posts over the years have seen this terminology’s evolution. Now I would say (till 2024): From Coordinated to Coordinated and Connected.

A data-driven approach was not new at that time. Roughly speaking, around 2006 – close to the introduction of the Smartphone – there was already a trend spurred by better global data connectivity at lower cost. Easy connectivity allowed PLM to expand into industries that were not closely connected to 3D CAD systems(CATIA, CREO or NX). Agile PLM, Aras, and SAP PLM became visible – PLM is no longer for design management but also for go-to-market governance in the CPG and apparel industry.

However, a data-driven approach was still rare in mainstream manufacturing companies, where drawings, office documents, email and Excel were the main information carriers next to the dominant ERP system.

A data-driven approach was a consultant’s dream, and when looking at the impact of digital transformation in other parts of the business, why not for PLM, too? My favorite and still valid 2014 image is the one below from Accenture describing Digital PLM. Here business and PLM come together – the WHY!

 

Again, the challenge with legacy

At that time, I saw a few companies linking their digital transformation to implementing a new PLM system. Those were the days the PLM vendors were battling for the big enterprise deals, sometimes motivated by an IT mindset that unifying the existing PDM/PLM systems would fulfill the digital dream. Science was not winning, but emotion. Read the PLM blame game – still actual.

One of my key observations is that companies struggle when they approach PLM transformation with a migration mindset. Moving from Coordinated to Connected isn’t just about technology—it’s about fundamentally changing how we work. Instead of a document-driven approach, organizations must embrace a data-driven, connected way of working.

The PLM community increasingly agrees that PLM isn’t a single system; it’s a strategy that requires a federated approach—whether through SaaS or even beyond it.

Before AI became a hype, we discussed the digital thread, digital twins, graph databases, ontologies, and data meshes. Legacy – people (skills), processes(rigid) and data(not reliable) – are the elephant in the room. Yet, the biggest challenge remains: many companies see PLM transformation as just buying new tools.

A fundamental transformation requires a hybrid approach—maintaining traditional operations while enabling multidisciplinary, data-driven teams. However, this shift demands new skills and creates the need to learn and adapt, and many organizations hesitate to take that risk.

In his Product Data Plumber Perspective on 2025. Rob Ferrone addressed the challenge to move forward too, and I liked one of his responses in the underlying discussion that says it all – it is hard to get out of your day to day comfort (and data):

Rob Ferrone’s quote:
Transformations are announced, followed by training, then communication fades. Plans shift, initiatives are replaced, and improvements are delayed for the next “fix-all” solution. Meanwhile, employees feel stuck, their future dictated by a distant, ever-changing strategy team.

 

And then there is Artificial Intelligence (2024 ……)

In the past two years, I have been reading and digesting much news related to AI, particularly generative AI.

Initially, I was a little skeptical because of all the hallucinations and hype; however, the progress in this domain is enormous.

I believe that AI has the potential to change our digital thread and digital twin concepts dramatically where the focus was on digital continuity of data.

Now this digital continuity might not be required, reading articles like The End of SaaS (a more and more louder voice), usage of the Fusion Strategy (the importance of AI) and an (academic) example, on a smaller scale, I about learned last year the Swedish Arrowhead™ fPVN project.

I hope that five years from now, there will not be a paragraph with the title Pity there was again legacy.

We should have learned from the past that there is always the first wave of tools – they come with a big hype and promise – think about the Startgate Project but also Deepseek.

Still remember, the change comes from doing things differently, not from efficiency gains. To do things differently you need an educated, visionary management with the power and skills to take a company in a new direction. If not,  legacy will win (again)

 

Conclusion

In my 25 years of working in the data management domain, now known as PLM, I have seen several impressive new developments – from 2D to 3D, from documents to data, from physical prototypes to  models and more. All these developments took decades to become mainstream. Whilst the technology was there, the legacy kept us back. Will this ever change? Your thoughts?

The pivotal 2015 PLM Roadmap / PDT conference

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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…