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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.
- The first step is simple: recognise product data as an asset that powers performance, not just admin.
Then treat it accordingly. - 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? - 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.
- Use quality KPIs that reflect the business, not the system. Focus your governance efforts where there’s friction, waste, or rework.
- 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.
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:
- The cost of not doing it.
- The value of doing it.
- 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.
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:
- THE ROADMAP and THE BIG PICTURE – is your project supported by a vision and a related roadmap of milestones to achieve?
- 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.
- 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.
- 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.
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
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.
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
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
First, I wish you all a prosperous 2025 and hope you will take the time to digest information beyond headlines.
Taking time to digest information is my number one principle now, which means you will see fewer blog posts from my side and potentially more podcast recordings.
My theme for 2025 : “It is all about people, data,
a sustainable business and a smooth digital transformation”.
Fewer blog posts
Fewer blog posts, as although AI might be a blessing for content writers, it becomes as exciting as Wikipedia pages. Here, I think differently than Oleg Shilovitsky, whose posts brought innovative thoughts to our PLM community – “Just my thoughts”.
Now Oleg endorses AI, as you can read in his post: PLM in 2025: A new chapter of blogging transformation. I asked ChatGPT to summarize my post in 50 words, and this is the answer I got – it saves you reading the rest:
The author’s 2025 focus emphasizes digesting information deeply, reducing blog posts, and increasing podcast recordings exploring real-life PLM applications. They stress balancing people and data-centric strategies, sustainable digital transformation, AI’s transformative role, and forward-looking concepts like Fusion Strategy. Success requires prioritizing business needs, people, and accurate data to harness AI’s potential.
Summarizing blog posts with AI saves you time. Thinking about AI-generated content, I understand that when you work in marketing, you want to create visibility for your brand or offer.
Do we need a blogging transformation? I am used to browsing through marketing content and then looking for the reality beyond it – facts and figures. Now it will be harder to discover innovative thoughts in this AI-generated domain.
Am I old fashioned? Time will tell.
More podcast recordings
As I wrote in a recent post, “PLM in real life and Gen AI“, I believe we can learn much from exploring real-life examples. You can always find the theory somewhere and many of the articles make sense and address common points. Some random examples:
- Top 4 Reasons Why PLM Implementations Fail
- 13 Common PLM Implementation Problems And How to Avoid Them
- 10 steps to a Successful PLM implementation
- 11 Essential Product Lifecycle Management Best Practices for Success
Similar recommendations exist for topics like ERP, MES, CRM or Digital Transformation (one of the most hyped terms).
They all describe WHAT to do or not to do. The challenge however is: HOW to apply this knowledge in your unique environment, considering people, skills, politics and culture.
With the focus on the HOW, I worked with Helena Gutierrez last year on the Share PLM podcast series 2. In this series, we interviewed successful individuals from various organizations to explore HOW they approached PLM within their companies. Our goal was to gain insights from their experiences, particularly those moments when things didn’t go as planned, as these are often the most valuable learning opportunities.
I am excited to announce that the podcast will continue this year with Series 3! Joining me this season will be Beatriz Gonzales, Share PLM’s co-founder and new CEO. For Series 3, we’ve decided to broaden the scope of our interviews. In addition to featuring professionals working within companies, we’ll also speak with external experts, such as coaches and implementation partners, who support organizations in their PLM journey.
Our goal is to uncover not only best practices from these experts but also insights into emerging “next practices.”
Stay tuned for series 3!
#datacentric or #peoplecentric ?
The title of the paragraph covers topics from the previous paragraphs and it was also the theme from a recent post shared through LinkedIn from Lionel Grealou: Driving Transformation: Data or People First?
We all agree here that it is not either one or the other, and as the discussion related to the post further clarifies, it is about a business strategy that leads to both of these aspects.
This is the challenge with strategies. A strategy can be excellent – on paper – the success comes from the execution.
This discussion reminds me of the lecture Yousef Hooshmand gave at the PLM platform in the Netherlands last year – two of his images that could cover the whole debate:
Whatever you implement starts from the user experience, giving the data-centric approach the highest priority and designing the solution for change, meaning avoiding embedded hard-coded ways of working.
While companies strive to standardize processes to provide efficiency and traceability, the processes should be reconfigurable or adaptable when needed, reconfigured on reliable data sources.
Jan Bosch shared this last thought too in his Digital Reflection #5: Cog in the Machine. My favorite quote from this refection
“However, in a world where change is accelerating, we need to organize ourselves in ways that make it easy to incorporate change and not ulcer-inducing hard. How do we get there?”
Of course, before we reach tools and technology, the other image Yousef Hooshmand shared below gives a guiding principle that I believe everyone should follow in their context.
It starts with having a C-level long-term commitment when you want to perform a business transformation, and then, in an MVP approach, you start from the business, which will ultimately lead you to the tools and technologies.
The challenge seen in this discussion is that:
most manufacturing companies are still too focused on investing in what they are good at now and do not explore the future enough.
This behavior is why Industry 4.0 is still far from being implemented, and the current German manufacturing industry is in a crisis.
It requires an organization that understands the big picture and has a (fusion) strategy.
Fusion Strategy ?
Is the Fusion Strategy the next step, as Steef Klein often mentions in our PLM discussions? The Fusion Strategy, introduced by world-renowned innovation guru Vijay Govindarajan (The Three Box Solution) and digital strategy expert Venkat Venkatraman (Fusion Strategy), offers a roadmap that will help industrial companies combine what they do best – creating physical products – with what digital technology companies do best – capturing and analyzing data through algorithms and AI.
It is a topic I want to explore this year and see how to connect it to companies in my ecosystem. It is an unknown phenomenon as most of them struggle with a data-driven foundation and skills and focus on the right AI applications.
The End of SaaS?
A potential interesting trend als related to AI I want to clarify further is the modern enterprise architecture . Over the past two years, we have seen a growing understanding that we should not think in systems connected through interfaces but towards a digitally connected infrastructure where APIs, low-code platforms or standardized interfaces will be responsible for real-time collaboration.
I wrote about these concepts in my PLM Roadmap / PDT Europe review. Look at the section: R-evolutionizing PLM and ERP and Heliple. At that time, I shared the picture below, which illustrates the digital enterprise.
The five depicted platforms in the image ( IIoT, CRM, PLM, ERP, MES) are not necessarily a single system. They can be an ecosystem of applications and services providing capabilities in that domain. In modern ways of thinking, each platform could be built upon a SaaS portfolio, ensuring optimal and scalable collaboration based on the company’s needs.
Implementing such an enterprise based on a combination of SaaS offerings might be a strategy for companies to eliminate IT overhead.
However, known forward-thinking experts like Vijay Govindarajan and Venkat Venkatraman with their Fusion Strategy. Also, Satya Nadella, CEO of Microsoft, imagines instead of connected platforms a future with an AI layer taking care of the context of the information – the Microsoft Copilot message. Some of his statements:
This transformation is poised to disrupt traditional tools and workflows, paving the way for a new generation of applications.
The business logic is all going to these AI agents. They’re not going to discriminate between what the backend is — they’ll update multiple databases, and all the logic will be in the AI tier.
Software as a Business Weapon?
Interesting thoughts to follow and to combine with this Forbes article, The End Of The SaaS Era: Rethinking Software’s Role In Business by Josipa Majic Predin. She introduces the New Paradigm: Software as a Business Weapon.
Quote:
Instead of focusing solely on selling software subscriptions, innovative companies are using software to enhance and transform existing businesses. The goal is to leverage technology to make certain businesses significantly more valuable, efficient, and competitive.
This approach involves developing software that can improve the operations of “real world” businesses by 20-30% or more. By creating such powerful tools, technology companies can position themselves to acquire or partner with the businesses they’ve enhanced, thereby capturing a larger share of the value they’ve created.
It is interesting to see these thoughts popping up, usually 10 to 20 years ahead before companies adopt them. However, I believe with AI’s unleashed power, this is where we should be active and learn. It is an exciting area where terms like eBOM or mBOM sound hackneyed.
Sustainability?
As a PLM Green Global Alliance member, I will continue to explore topics related to PLM and how they can serve Sustainability. They are connected as the image from the 2022 PLM Roadmap/PDT Europe indicates:
I will keep on focusing on separate areas within my PGGA network.
Conclusion
I believe 2025 will be the year to focus on understanding the practical applications of AI. Amid the hype and noise, there lies significant potential to re-imagine our PLM landscape and vision. However, success begins with prioritizing the business, empowering people, and ensuring accurate data.
Most times in this PLM and Sustainability series, Klaus Brettschneider and Jos Voskuil from the PLM Green Global Alliance core team speak with PLM related vendors or service partners.
This year we have been speaking with Transition Technologies PSC, Configit, aPriori, Makersite and the PLM Vendors PTC, Siemens and SAP.
Where the first group of companies provided complementary software offerings to support sustainability – “the fourth dimension”– the PLM vendors focused more on the solutions within their portfolio.
This time we spoke with , CIMPA PLM services, a company supporting their customers with PLM and Sustainability challenges, offering an end-to-end support.
What makes them special is that they are also core partner of the PLM Global Green Alliance, where they moderate the Circular Economy theme – read their introduction here: PLM and Circular Economy.
CIMPA PLM services
We spoke with Pierre DAVID and Mahdi BESBES from CIMPA PLM services. Pierre is an environmental engineer and Mahdi is a consulting manager focusing on parts/components traceability in the context of sustainability and a circular economy. Many of the activities described by Pierre and Mahdi were related to the aerospace industry.
We had an enjoyable and in-depth discussion of sustainability, as the aerospace industry is well-advanced in traceability during the upstream design processes. Good digital traceability is an excellent foundation to extend for sustainability purposes.
CSRD, LCA, DPP, AI and more
A bunch of abbreviations you will have to learn. We went through the need for a data-driven PLM infrastructure to support sustainability initiatives, like Life Cycle Assessments and more. We zoomed in on the current Corporate Sustainability Reporting Directive(CSRD) highlighting the challenges with the CSRD guidelines and how to connect the strategy (why we do the CSRD) to its execution (providing reports and KPIs that make sense to individuals).
In addition, we discussed the importance of using the proper methodology and databases for lifecycle assessments. Looking forward, we discussed the potential of AI and the value of the Digital Product Passport for products in service.
Enjoy the 37 minutes discussion and you are always welcome to comment or start a discussion with us.
What we learned
- Sustainability initiatives are quite mature in the aerospace industry and thanks to its nature of traceability, this industry is leading in methodology and best practices.
- The various challenges with the CSRD directive – standardization, strategy and execution.
- The importance of the right databases when performing lifecycle analysis.
- CIMPA is working on how AI can be used for assessing environmental impacts and the value of the Digital Product Passport for products in service to extend its traceability
Want to learn more?
Here are some links related to the topics discussed in our meeting:
- CIMPA’s theme page on the PLM Green website: PLM and Circular Economy
- CIMPA’s commitments towards A sustainable, human and guiding approach
- Sopra Steria, CIMPA’s parent company: INSIDE #8 magazine
Conclusion
The discussion was insightful, given the advanced environment in which CIMPA consultants operate compared to other manufacturing industries. Our dialogue offered valuable lessons in the aerospace industry, that others can draw on to advance and better understand their sustainability initiatives
This year, I will celebrate 25 years since I started my company, TacIT, to focus on knowledge management. However, quickly, I was back in the domain of engineering data management, which became a broader topic, which we now call PLM.
Looking back, there have been significant changes in these 25 years, from systems to strategy, for documents to data, from linear to iterative. However, in this post, I want to look at my 2024 observations to see where we can progress. This brings me to the first observation.
PLM is human
Despite many academic and marketing arguments describing WHAT and WHY companies need specific business or software capabilities, there is, above all, the need for people to be personally inspired and connected. We want to belong to a successful group of people, teams and companies because we are humans, not resources.
It is all about people, which was also the title of my session during the March 2024 3DEXPERIENCE User Conference in Eindhoven (NL). I led a panel discussion on the importance of people with Dr. Cara Antoine, Daniel Schöpf, and Florens Wolters, each of whom actively led transformational initiatives within their companies.
Through Dr. Cara Antoine, e at Capgemini and a key voice for women in tech, I learned about her book Make It Personal. The book inspired me and motivated me to continue using a human-centric approach. Give this book to your leadership and read it yourself. It is practical, easy to read, and encouraging
Recently, in my post “PLM in real life and Gen AI“, I shared insights related to PLM blogs and Gen AI – original content is becoming increasingly the same, and the human touch is disappearing, while generating more and longer blogs.
I propose keeping Gen AI-generated text for the boring part of PLM and exploring the human side of PLM engagements in blogs. What does this mean? In the post, I also shared the highlights of the Series 2 podcast I did together with Helena Gutierrez from Share PLM. Every recording had its unique human touch and knowledge.
We are now in full preparation for Series 3—let us know who your hero is and who should be our guest in 2025!
PLM is business
One of the most significant changes I noticed in my PLM-related projects was that many of the activities connected the PLM activities to the company’s business objectives. Not surprisingly, it was mostly a bottom-up activity, explaining to the upper management that a modern, data-driven PLM strategy is crucial to achieving business or sustainability goals.
I wrote two long posts about these experiences. The first one,” PLM – business first,” zooms in on the changing mindset that PLM is not an engineering system anymore but part of a digital infrastructure that supports companies in achieving their business goals. The image below from Dr. Yousef Hooshmand is one of my favorites in this context. The 5 + 1 steps, where the extra step is crucial: Long Executive Commitment.
So, to get an executive commitment, you need to explain and address business challenges.
Executive commitment and participation can be achieved through a Benefits Dependency Network approach, as illustrated in this webinar I did with the Heliple-2 team, where we were justifying the business needs for Federated PLM. More about the Federated PLM part in the next paragraph.
Another point to consider is that when the PLM team is part of the IT organization (the costs side), they have a big challenge in leading or even participating in business discussions. In this context, read (again) Jan Bosch’s post: Structure Eats Strategy.
The second post, more recent, summarized the experiences I had with several customer engagements. The title says it all: “Don’t use the P**-word! – 5 lessons learned“, with an overlap in content with the first post.
Conclusion: A successful PLM strategy starts with the business and needs storytelling to align all stakeholders with a shared vision or goal.
PLM is technology
This year has seen the maturation of PLM technology concepts. We are moving away from a monolithic PLM system and exploring federated and connected infrastructures, preferably a mix of Systems of Record (the old PLMs/ERPs) and Systems of Engagement (the new ways of domain collaboration). The Heliple project manifests such an approach, where the vertical layers are Systems of Record, and the horizontal modules could be Systems of Engagement.
I had several discussions with typical System of Engagement vendors, like Colab (“Where traditional PLM fails”) and Partful (“Connected Digital Thread for Lower and Mid-market OEMs“), but I also had broader discussions during the PLM Roadmap PDT Europe conference – see: R-evolutionizing PLM and ERP and Heliple.
I also follow Dr. Jorg Fischer, who lectures about digital transformation concepts in the manufacturing business domain. Unfortunately, for a broader audience, Jörg published a lot in German, and typically, his references for PLM and ERP are based on SAP and Teamcenter. His blog posts are always interesting to follow – have a look at his recent blog in English: 7 keys to solve PLM & ERP.
Of course, Oleg Shilovitsky’s impressive and continuous flow of posts related to modern PLM concepts is amazing—just browse through his Beyond PLM home page to read about the actual topics happening in his PLM ecosystem or for example, read about modern technology concepts in this recent OpenBOM article.
Conceptually, we are making progress. As a commonality, all future concepts focus on data, not so much on managing documents—and here comes the focus on data.
PLM needs accurate data
In a data-driven environment, apps or systems will use a collection of datasets to provide a user with a working environment, either a dashboard or an interactive real-time environment. Below is my AI (Artist Impression) of a digital enterprise.
Of course, it seems logical; the data must be accurate as you no longer have control over access to the data in a data-driven environment. You can be accountable for the data; others can consume the data you created without checking its accuracy by your guidance.
Therefore, data governance and an excellent enterprise architecture are crucial to support the new paradigm:
The nearest source of truth supported by a single source of change
Quote: Yousef Hoohmand
Forget the Single Source of Truth idea, a previous century paradigm.
With data comes Artificial intelligence and algorithms that can play an essential role in your business, providing solutions or insights that support decision-making.
In 2024, most of us have been exploring the benefits of ChatGPT and Generative AI. You can describe examples of where AI could assist in every aspect of the product lifecycle. I saw great examples from Eaton, Ocado, and others at the PLM Roadmap/PDT Europe conference.
See my review here: A long week after the PLM Roadmap / PDT Europe conference.
Still, before benefiting from AI in your organization, it remains essential that the AI runs on top of accurate data.
Sustainability needs (digital) PLM
This paragraph is the only reverse dependency towards PLM and probably the one that is less in people’s minds, perhaps because PLM is already complex enough. In 2024, with the PLM Green Global Alliance, we had good conversations with PLM-related software vendors or service partners (aPriori, Configit, Makersite, PTC, SAP, Siemens and Transition Technologies PSC) where we discussed their solutions and how they are used in the field by companies.
We discovered here that most activities are driven by regulations, like ESG reporting, the new CSRD directive for Europe and the implementation of the Digital Product Passport. What is clear from all these activities is that companies need to have a data-driven PLM infrastructure to connect product data to environmental impacts, like carbon emissions equivalents.
Besides complying with regulations, I have been discussing the topic of Product-As-A-Service, or the Product Service System, this year, with excellent feedback from Dave Duncan. You can find a link to his speech: Improving Product Sustainability – PTC with PGGA.
Also, during the PLM Roadmap / PDT Europe conference, I discussed this topic, explaining that achieving a circular economy is a long-term vision, and the starting point is to establish a connected infrastructure within your organizations and with your customers/users in the field.
Sustainability should be on everyone’s agenda. From the interactions on LinkedIn, you can see that we prefer to discuss terms like PDM/PLM or eBOM/mBOM in the PLM domain. Very few connect PLM to sustainability.
Sustainability is a long-term mission; however, as we have seen from long-term missions, they can be overwhelmed by the day’s madness and short-term needs.
PLM is Politics
You might not expect this paragraph in my log, as most PLM discussions are about the WHAT and the WHY of a PLM solution or infrastructure. However, the most challenging part of PLM is the HOW, and this is the area that I am still focused on.
In the early days of mediating mainly in SmarTeam implementations, it became clear that the technology was not the issue. A crisis was often due to a lack of (technical) skills or methodology and misplaced expectations.
Unicorns & HIPPOs in an enterprise (Peter Vind)
When the way out became clear, politics often started. Sometimes, there was the HIPPO (HIghest Paid Person’s Opinion) in the company, as Peter Vind explained, or there was the blame game, which I described in my 2019 “The PLM blame game post”.
What makes it even more difficult is that people’s opinions in PLM discussions are often influenced by their friendly relations or history with a particular vendor or implementer from the past, which troubles a proper solution path.
These aspects are challenging to discuss, and nobody wants to discuss them openly. A company (and a country) must promote curiosity instead of adhering to mainstream thinking and working methods. In our latest Share PLM podcast, Brian Berger, a VP at Metso, mentions the importance of diversity within an organization.
“It is a constant element of working in a global business, and the importance cannot be overstated.”
This observation should make us think again when we want to simplify everything and dim the colors.
Conclusion
Initially, I thought this would be a shorter post, but again, it became a long read – therefore, perhaps ideal when closing 2024 and looking forward to activities and focus for 2025. Use this time to read books and educate yourself beyond the social media posts (even my blogs are limited 😉)
In addition, I noticed the build-up of this post was unconsciously influenced by Martijn Dullaart‘s series of messages titled “Configuration Management is ……”. Thanks, Martijn, for your continuous contributions to our joint passion – a digital enterprise where PLM and CM flawlessly interact based on methodology and accurate data.
Jos Voskuil's Weblog 
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