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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.
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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?
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.
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?
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
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.
Recently, I attended several events related to the various aspects of product lifecycle management; most of them were tool-centric, explaining the benefits and values of their products.
In parallel, I am working with several companies, assisting their PLM teams to make their plans understood by the upper management, which has always been my mission in the past.
However, nowadays, people working in the business are feeling more and more challenged and pained by not acting adequately to the upcoming business demands.
The image below has been shown so many times, and every time, the context becomes more relevant.
Too often, an evolutionary mindset with small steps is considered instead of looking toward the future and reasoning back for what needs to be done.
Let me share some experiences and potential solutions.
Don’t use the P** word!
The title of this post is one of the most essential points to consider. By using the term PLM, the discussion is most of the time framed in a debate related to the purchase or installation of a system, the PLM system, which is an engineering tool.
PLM vendors, like Dassault Systèmes and Siemens, have recognized this, and the word PLM is no longer on their home pages.
They are now delivering experiences or digital industries software.
Other companies, such as PTC and Aras, broadened the discussion by naming other domains, such as manufacturing and services, all connected through a digital thread.
The challenge for all these software vendors is why a company would consider buying their products. A growing issue for them is also why would they like to change their existing PLM system to another one, as there is so much legacy.
For all of these vendors, success can come if champions inside the targeted company understand the technology and can translate its needs into their daily work.
Here, we meet the internal PLM team, which is motivated by the technology and wants to spread the message to the organization. Often, with no or limited success, as the value and the context they are considering are not understood or felt as urgent.
Lesson 1:
Don’t use the word PLM in your management messaging.
In some of the current projects I have seen, people talk about the digital highway or a digital infrastructure to take this hurdle. For example, listen to the SharePLM podcast with Roger Kabo from Marel, who talks about their vision and digital product highway.
The Marel digital product highway
As soon as you use the word PLM, most people think about a (costly) system, as this is how PLM is framed. Engineering, like IT, is often considered a cost center, as money is made by manufacturing and selling products.
According to experts (CIMdata/Gartner), Product Lifecycle Management is considered a strategic approach. However, the majority of people talk about a PLM system. Of course, vendors and system integrators will speak about their PLM offerings.
To avoid this framing, first of all, try to explain what you want to establish for the business. The terms Digital Product Highway or Digital Infrastructure, for example, avoid thinking in systems.
Lesson 2:
Don’t tell your management why they need to reward your project – they should tell you what they need.
This might seem like a bit of strange advice; however, you have to realize that most of the time, people do not talk about the details at the management level. At the management level, there are strategies and business objectives, and you will only get attention when your proposal addresses the business needs. At the management level, there should be an understanding of the business need and its potential value for the organization. Next, analyzing the business changes and required tools will lead to an understanding of what value the PLM team can bring.
Yousef Hooshmand’s 5 + 1 approach illustrates this perfectly. It is crucial to note that long-term executive commitment is needed to have a serious project, and therefore, the connection to their business objective is vital.
Therefore, if you can connect your project to the business objectives of someone in management, you have the opportunity to get executive sponsorship. A crucial advice you hear all the time when discussing successful PLM projects.
Lesson 3:
Alignment must come from within the organization.
Last week, at the 20th anniversary of the Dutch PLM platform, Yousef Hooshmand gave the keynote speech starting with the images below:
On the left side, we see the medieval Catholic church sincerely selling salvation through indulgences, where the legend says Luther bought the hell, demonstrating salvation comes from inside, not from external activities – read the legend here.
On the right side, we see the Digital Transformation expert sincerely selling digital transformation to companies. According to LinkedIn, there are about 1.170.000 people with the term Digital Transformation in their profile.
As Yousef mentioned, the intentions of these people can be sincere, but also, here, the transformation must come from inside (the company).
When I work with companies, I use the Benefits Dependency Network methodology to create a storyboard for the company. The BDN network then serves as a base for creating storylines that help people in the organization have a connected view starting from their perspective.
Companies might hire strategic consultancy firms to help them formulate their long-term strategy. This can be very helpful where, in the best case, the consultancy firm educates the company, but the company should decide on the direction.
In an older blog post, I wrote about this methodology, presented by Johannes Storvik at the Technia Innovation forum, and how it defines a value-driven implementation.
Dassault Systèmes and its partners use this methodology in their Value Engagement process, which is tuned to their solution portfolio.
You can also watch the webinar Federated PLM Webinar 5 – The Business Case for the Federated PLM, in which I explained the methodology used.
Lesson 4:
PLM is a business need not an IT service
This lesson is essential for those who believe that PLM is still a system or an IT service. In some companies, I have seen that the (understaffed) PLM team is part of a larger IT organization. In this type of organization, the PLM team, as part of IT, is purely considered a cost center that is available to support the demand from the business.
The business usually focuses on incremental and economic profitability, less on transformational ways of working.
In this context, it is relevant to read Chris Seiler’s post: How to escape the vicious circle in times of transformation? Where he reflects on his 2002 MBA study, which is still valid for many big corporate organizations.
It is a long read, but it is gratifying if you are interested. It shows that PLM concepts should be discussed and executed at the business level. Of course, I read the article with my PLM-twisted brain.
A Pessimistic Scenario of External Driving Forces and Their Internal Interrelations (Christ Seiler)
The image above from Chris’s post could be a starting point for a Benefits-Dependent Network diagram, expanded with Objectives, Business Changes and Benefits to fight this vicious downturn.
As PLM is no longer a system but a business strategy, the PLM team should be integrated into the business potential overlooked by the CIO or CDO, as a CEO is usually not able to give this long-term executive commitment.
Lesson 5:
Educate yourselves and your management
The last lesson is crucial, as due to improving technologies like AI and, earlier, the concepts of the digital twin, traditional ways of coordinated working will become inefficient and redundant.
However, before jumping on these new technologies, everyone, at every level in the organization, should be aware of:
WHY will this be relevant for our business? Is it to cut costs – being more efficient as fewer humans are in the process? Is it to be able to comply with new upcoming (sustainability) regulations? Is it because the aging workforce leaves a knowledge gap?
WHAT will our business need in the next 5 to 10 years? Are there new ways of working that we want to introduce, but we lack the technology and the tools? Do we have skills in-house? Remember, digital transformation must come from the inside.
HOW are we going to adapt our business? Can we do it in a learning mode, as the end target is not clear yet—the MVP (Minimum Viable Product) approach? Are we moving from selling products to providing a Product Service System?
My lesson: Get inspired by the software vendors who will show you what might be possible. Get educated on the topic and understand what it would mean for your organization. Start from the people and the business needs before jumping on the tools.
In the upcoming PLM Roadmap/PDT Europe conference on 23-24 October, we will be meeting again with a group of P** experts to discuss our experiences and progress in this domain. I will give a lecture here about what it takes to move to a sustainable economy based on a Product-as-a-service concept.
If you want to learn more – join us – here is the link to the agenda.
Conclusion
I hope you enjoyed reading a blog post not generated by ChatGPT, although I am using bullet points. With the overflow of information, it remains crucial to keep a holistic overview. I hope that with this post, I have helped the P** teams in their mission, and I look forward to learning from your experiences in this domain.
I have not been writing much new content recently as I feel that from the conceptual side, so much has already been said and written. A way to confuse people is to overload them with information. We see it in our daily lives and our PLM domain.
With so much information, people become apathetic, and you will hear only the loudest and most straightforward solutions.
One desire may be that we should go back to the past when everything was easier to understand—are you sure about that?
This attitude has often led to companies doing nothing, not taking any risks, and just providing plasters and stitches when things become painful. Strategic decision-making is the key to avoiding this trap.
I just read this article in the Guardian: The German problem? It is an analog country in a digital world.
The article also describes the lessons learned from the UK (quote):
Britain was the dominant economic power in the 19th century on the back of the technologies of the first Industrial Revolution and found it hard to break with the old ways even when it should have been obvious that its coal and textile industries were in long-term decline.
As a result, Britain lagged behind its competitors. One of these was Germany, which excelled in advanced manufacturing and precision engineering.
Many technology concepts originated from Germany in the past and even now we are talking about Industrie 4.0 and Catena-X as advanced concepts. But are they implemented? Did companies change their culture and ways of working required for a connected and digital enterprise?
Technology is not the issue.
The current PLM concepts, which discuss a federated PLM infrastructure based on connected data, have become increasingly stable.
Perhaps people are using different terminologies and focusing on specific aspects of a business; however, all these (technical) discussions talk about similar business concepts:
- Prof. Dr. Jorg W. Fischer, managing partner at Steinbeis – Reshape Information Management (STZ-RIM), writes a lot about a modern data-driven infrastructure, mainly in the context of PLM and ERP. His recent article: The Freeway from PLM to ERP.
- Oleg Shilovitsky, CEO of OpenBOM, has a never-ending flow of information about data and infrastructure concepts and an understandable focus on BOMs. One of his recent articles, PLM 2030: Challenges and Opportunities of Data Lifecycle Management
- Matthias Ahrens, enterprise architect at Forvia / Hella, often shares interesting concepts related to enterprise architecture relevant to PLM. His latest share: Think PLM beyond a chain of tools!
- Dr. Yousef Hooshmand, PLM lead at NIO, shared his academic white paper and experiences at Daimler and NIO through various presentations. His publication can be found here: From a Monolithic PLM Landscape to a Federated Domain and Data Mesh.
- Erik Herzog, technical fellow at SAAB Aeronautics, has been active for the past two years, sharing the concept of federated PLM applied in the Heliple project. His latest publication post: Heliple Federated PLM at the INCOSE International Symposium in Dublin
Several more people are sharing their knowledge and experience in the domain of modern PLM concepts, and you will see that technology is not the issue. The hype of AI may become an issue.
From IT focus to Business focus
One issue I observed at several companies I worked with is that the PLM’s responsibility is inside the IT organization – click on the image to get the mindset.
This situation is a historical one, as in the traditional PLM mode, the focus was on the on-premise installation and maintenance of a PLM system. Topics like stability, performance and security are typical IT topics.
IT departments have often been considered cost centers, and their primary purpose is to keep costs low.
Does the slogan ONE CAD, ONE PLM or ONE ERP resonate in your company?
It is all a result of trying to standardize a company’s tools. It is not deficient in a coordinated enterprise where information is exchanged in documents and BOMs. Although I wrote in 2011 about the tension between business and IT in my post “PLM and IT—love/hate relation?”
Now, modern PLM is about a connected infrastructure where accurate data is the #1 priority.
Most of the new processes will be implemented in value streams, where the data is created in SaaS solutions running in the cloud. In such environments, business should be leading, and of course, where needed, IT should support the overall architecture concepts.
In this context, I recommend an older but still valid article: The Changing Role of IT: From Gatekeeper to Business Partner.
This changing role for IT should come in parallel to the changing role for the PLM team. The PLM team needs to first focus on enabling the new types of businesses and value streams, not on features and capabilities. This change in focus means they become part of the value creation teams instead of a cost center.
From successful PLM implementations, I have seen that the team directly reported to the CEO, CTO or CIO, no longer as a subdivision of the larger IT organization.
Where is your PLM team?
Is it a cost center or a value-creation engine?
The role of business leaders
As mentioned before, with a PLM team reporting to the business, communication should transition from discussing technology and capabilities to focusing on business value.
I recently wrote about this need for a change in attitude in my post: PLM business first. The recommended flow is nicely represented in the section “Starting from the business.”
Image: Yousef Hooshmand.
Business leaders must realize that a change is needed due to upcoming regulations, like ESG and CSRD reporting, the Digital Product Passport and the need for product Life Cycle Analysis (LCA), which is more than just a change of tools.
I have often referred to the diagram created by Mark Halpern from Gartner in 2015. Below you can see and adjusted diagram for 2024 including AI.
It looks like we are moving from Coordinated technology toward Connected technology. This seems easy to frame. However, my experience discussing this step in the past four to five years has led to the following four lessons learned:
- It is not a transition from Coordinated to Connected.
At this step, a company has to start in a hybrid mode – there will always remain Coordinated ways of working connected to Connected ways of working. This is the current discussion related to Federated PLM and the introduction of the terms System of Record (traditional systems / supporting linear ways of working) and Systems of Engagement (connected environments targeting real-time collaboration in their value chain) - It is not a matter of buying or deploying new tools.
Digital transformation is a change in ways of working and the skills needed. In traditional environments, where people work in a coordinated approach, they can work in their discipline and deliver when needed. People working in the connected approach have different skills. They work data-driven in a multidisciplinary mode. These ways of working require modern skills. Companies that are investing in new tools often hesitate to change their organization, which leads to frustration and failure. - There is no blueprint for your company.
Digital transformation in a company is a learning process, and therefore, the idea of a digital transformation project is a utopia. It will be a learning journey where you have to start small with a Minimum Viable Product approach. Proof of Concepts is a waste of time as they do not commit to implementing the solution. - The time is now!
The role of management is to secure the company’s future, which means having a long-term vision. And as it is a learning journey, the time is now to invest and learn using connected technology to be connected to coordinated technology. Can you avoid waiting to learn?
I have shared the image below several times as it is one of the best blueprints for describing the needed business transition. It originates from a McKinsey article that does not explicitly refer to PLM, again demonstrating it is first about a business strategy.
It is up to the management to master this process and apply it to their business in a timely manner. If not, the company and all its employees will be at risk for a sustainable business. Here, the word Sustainable has a double meaning – for the company and its employees/shareholders and the outside world – the planet.
Want to learn and discuss more?
Currently, I am preparing my session for the upcoming PLM Roadmap/PDT Europe conference on 23 and 24 October in Gothenburg. As I mentioned in previous years, this conference is my preferred event of the year as it is vendor-independent, and all participants are active in the various phases of a PLM implementation.
If you want to attend the conference, look here for the agenda and registration. I look forward to discussing modern PLM and its relation to sustainability with you. More in my upcoming posts till the conference.
Conclusion
Digital transformation in the PLM domain is going slow in many companies as it is complex. It is not an easy next step, as companies have to deal with different types of processes and skills. Therefore, a different organizational structure is needed. A decision to start with a different business structure always begins at the management level, driven by business goals. The technology is there—waiting for the business to lead.
In recent years, I have assisted several companies in defining their PLM strategy. The good news is that these companies are talking first about a PLM strategy and not immediately about a PLM system selection.
In addition, a PLM strategy should not be defined in isolation but rather as an integral part of a broader business strategy. One of my favorite one-liners is:
“Are we implementing the past, or are we implementing the future?”
When companies implement the past, it feels like they modernize their current ways of working with new technology and capabilities. The new environment is more straightforward to explain to everybody in the company, and even the topic of migration can be addressed as migration might be manageable.
Note: Migration should always be considered – the elephant in the room.
I wrote about Migration Migraine in two posts earlier this year, one describing the basics and the second describing the lessons learned and the path to a digital future.
Implementing PLM now should be part of your business strategy.
Threats coming from different types of competitors, necessary sustainability-related regulations (e.g., CSRD reporting), and, on the positive side, new opportunities are coming (e.g., Product as a Service), all requiring your company to be adaptable to changes in products, services and even business models.
Suppose your company wants to benefit from concepts like the Digital Twin and AI. In that case, it needs a data-driven infrastructure—
Digital Twins do not run on documents, and algorithms need reliable data.
Digital Transformation in the PLM domain means combining Coordinated and Connected working methods. In other words, you need to build an infrastructure based on Systems of Record and Systems of Engagement. Followers of my blog should be familiar with these terms.
PLM is not an R&D and Engineering solution
(any more)
One of the biggest misconceptions still made is that PLM is implemented by a single system mainly used by R&D and Engineering. These disciplines are considered the traditional creators of product data—a logical assumption at the time when PLM was more of a silo, Managing Projects with CAD and BOM data.
However, this misconception frames many discussions towards discussions about what is the best system for my discipline, more or less strengthening the silos in an organization. Being able to break the silos is one of the technical capabilities digitization brings.
Business and IT architecture are closely related. Perhaps you have heard about Conway’s law (from 1967):
“Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization’s communication structure.”
This means that if you plan to implement or improve a PLM infrastructure without considering an organizational change, you will be locked again into your traditional ways of working – the coordinated approach, which is reflected on the left side of the image (click on it to enlarge it).
An organizational change impacts middle management, a significant category we often neglect. There is the C-level vision and the voice of the end user. Middle management has to connect them and still feel their jobs are not at risk. I wrote about it some years ago: The Middle Management Dilemma.
How do we adapt the business?
The biggest challenge of a business transformation is that it starts with the WHY and should be understood and supported at all organizational levels.
If there is no clear vision for change but a continuous push to be more efficient, your company is at risk!
For over 60 years, companies have been used to working in a coordinated approach, from paper-based to electronic deliverables.
- How do you motivate your organization to move in a relatively unknown direction?
- Who in your organization are the people who can build a digital vision and Strategy?
These two questions are fundamental, and you cannot outsource ownership of it.
People in the transformation teams need to be digitally skilled (not geeks), communicators (storytellers), and, very importantly, connected to the business.
Often, the candidates come from the existing business units where they have proven skills. The challenging part is educating them and making them available for this mission.
Digital transformation is not a side job.
Education can come from the outside world. Making people available to work on the new digital infrastructure is a management decision and their sense of priority.
How to get external support?
If you are connected to the PLM world like me, a lot of information is available. In academic papers, projects and in particular on LinkedIn currently, there is an overflow of architectural debates:
Recently, I participated in the discussions below:
- How to Solve PLM & ERP (Oleg Shilovitsky)
- Last week, we finally solved PLM & ERP (Prof. Dr. Jörg W. Fischer / Martin Eigner)
- PLM and MBOM: Supply Chain Debates and Future Solution Architecture (Oleg Shilovitsky)
- Could be a Knowledge Graph resp. the Linked Data technologies the key to …. (Matthias Ahrens)
The challenge with these articles is that they are for insiders and far from shareable with business people. There is always a discussion, as we are all learning to match theory with reality. For example,Prof. Dr. Jörg W. Fischer introduced the Information Architecture as a missing link. You can read his recent post here and the quote below to get interested:
All of these methods focus either on Data Architecture or Business Architecture. And the blind spot? I am convinced that an essential layer between the two is missing. We at STZ-RIM Reshape Information Management call this Information Architecture.
Still, we remain in the expert domain, which a limited group of people understands. We need to connect to the business. Where can we find more education from the business side?
The reaction below in one of the discussions says it all, in my opinion:
Starting from the business
What I have learned from my discussions with the management is:
- Don’t mention PLM – you will be cornered in the R&D / Engineering frame.
- Don’t explain their problems, and tell them that you have the solution (on PowerPoint)
- Create curiosity about topics that are relevant to the business – What if …?
- Use storytelling to imagine a future state – Spare the details.
- Build trust and confidence that you are not selling a product. Let the company discover their needs as it is their transformation.
The diagram below, presented by Yousef Hooshmand during the PLM Roadmap/PDT Europe 2023 conference in Paris, describes it all:
It will be a continuous iterative process where, starting from business values and objectives, an implementation step is analyzed, how it fits in the PLM landscape and ultimately, how measures and actions guide the implementation of the tools and technology.
It is important to stress that this is not the guidance for a system implementation; it is the guidance for a digital transformation journey. Therefore, the message in the middle of the image is: Long-term Executive Commitment!
In addition, I want to point to articles and blogs written by Jan Bosch. Jan is an Executive, professor and consultant with more than 20 years of experience in large-scale software R&D management and business.
Although our worlds do not intersect yet, the management of mechanical products and software is different; his principles fit better and better with a modern data-driven organization. Often, I feel we are fighting the same battle to coach companies in their business transformation.
In the context of this article, I recommend reviewing the BAPO model coming from the software world.
BAPO stands for Business, Architecture, Process and Organization. As the diagram below indicates, you should start from the business, defining the needs for the architecture and then the preferred ways of working. Finally, the organization has to be established in accordance with the processes.
Often, companies use the OPAB approach, which makes them feel more comfortable (Conway’s Law). For further reading in this context, I recommend the following posts from Jan Bosch:
Business and technology
I want to conclude by discussing ways to connect business and technology as you need both.
First, I want to point to an example that we presented in the Federated PLM interest group on LinkedIn. Although the discussion initially focused on technical capabilities, we concluded by connecting them to business transformational needs. The diagram below is our characteristic image used to explain the interaction between Systems of Record (the vertical pillars) and the Systems of Engagement (the horizontal bars – modularity).
Have a look at the business discussion below:
Next, the diagram below comes from a 2017 McKinsey whitepaper: Toward an integrated technology operating model. Here, the authors describe how a company can move toward an integrated technology operating model using both coordinated and connected technologies.
They do not mention PLM; they have a business focus, and it is important to mention a company can work in different modes. This is an organizational choice, but don’t let people work in two modes,
Conclusion
With this post, I hope I moved the focus from technology and tools to an understandable business focus. Even within my 1500 words, there is much more to say, and this makes our (PLM) mission so complex and interesting. Let me know where you can connect.
Two weeks ago, I shared my first post about PDM/PLM migration challenges on LinkedIn: How to avoid Migration Migraine – part 1. Most of the content discussed was about data migrations.
Starting from moving data stored in relational databases to modern object-oriented environments – the technology upgrade. But also the challenges a company can have when merging different data siloes (CAD & BOM related) into a single PLM backbone to extend the support of product data beyond engineering.
Luckily, the post generated a lot of reactions and feedback through LinkedIn and personal interactions last week.
The amount of interaction illustrated the relevance of the topic for people; they recognized the elephant in the room, too.
Working with a partner
Data migrations and consolidation are typically not part of a company’s core business, so it is crucial to find the right partner for a migration project. The challenge with migrations is that there is potentially a lot to do technically, but only your staff can assess the quality and value of migrations.
Therefore, when planning a migration, make sure you work on it iteratively with an experienced partner who can provide a set of tools and best practices. Often, vendors or service partners have migration tools that still need to be tuned to your As-Is and To-Be environment.
To get an impression of what a PLM service partner can do and which topics or tools are relevant in the context of mid-market PLM, you can watch this xLM webinar on YouTube. So make sure you select a partner who is familiar with your PDM/PLM infrastructure and who has the experience to assess complexity.
Migration lessons learned
In my PLM coaching career I have seen many migrations. In the early days they were more related to technology upgrades, consolidation of data and system replacements. Nowadays the challenges are more related to become more data-driven. Here are 5 lessons that I learned in the past twenty years:
- A fixed price for the migration can be a significant risk as the quality of the data and the result are hard to comprehend upfront. In case of a fixed price, either you would pay for the moon (taking all the risk), or your service partner would lose a lot of money. In a sustainable business model, there should be no losers.
- Start (even now) with checking and fixing your data quality. For example, when you are aware of a mismatch between CAD assemblies and BOM data, analyze and fix discrepancies even before the migration.
- One immediate action to take when moving from CAD assemblies to BOM structures is to check or fill the properties in the CAD system to support a smooth transition. Filling properties might be a temporary action, as later, when becoming more data-driven, some of these properties, e.g., material properties or manufacturer part numbers, should not be maintained in the CAD system anymore. However, they might help migration tools to extract a richer dataset.
- Focus on implementing an environment ready for the future. Don’t let your past data quality compromise complexity. In such a case, learn to live with legacy issues that will be fixed only when needed. A 100 % matching migration is not likely to happen because the source data might also be incorrect, even after further analysis.
- The product should probably not be configured in the CAD environment, even because the CAD tool allows it. I had this experience with SolidWorks in the past. PDM became the enemy because the users managed all configuration options in the assembly files, making it hard to use it on the BOM or Product level (the connected digital thread).
The future is data-driven
In addition, these migration discussions made me aware again that so many companies are still in the early phases of creating a unified PLM infrastructure in their company and implementing the coordinated approach – an observation I shared in my report on the PDSFORUM 2024 conference.
Due to sustainability-related regulations and the need to understand product behavior in the field (Digital Twin / Product As A Service), becoming data-driven is an unavoidable target in the near future. Implementing a connected digital thread is crucial to remaining competitive and sustainable in business.
However, the first step is to gain insights about the available data (formats and systems) and its quality. Therefore, implementing a coordinated PLM backbone should immediately contain activities to improve data quality and implement a data governance policy to avoid upcoming migration issues.
Data-driven environments, the Systems of Engagement, bring the most value when connected through a digital thread with the Systems of Record (PLM. ERP and others), therefore, design your processes, even current ones, user-centric, data-centric and build for change (see Yousef Hooshmand‘s story in this post – also image below).
The data-driven Future is not a migration.
The last part of this article will focus on what I believe is a future PLM architecture for companies. To be more precise, it is not only a PLM architecture anymore. It should become a business architecture based on connected platforms (the systems of record) and inter-platform connected value streams (the systems of engagement).
The discussion is ongoing, and from the technical and business side, I recommend reading Prof Dr. Jorg Fischer’s recent articles, for example. The Crisis of Digitalization – Why We All Must Change Our Mindset! or The MBOM is the Steering Wheel of the Digital Supply Chain! A lot of academic work has been done in the context of TeamCenter and SAP.
Also, Martin Eigner recently described in The Constant Conflict Between PLM and ERP a potential digital future of enterprise within the constraints of existing legacy systems.
In my terminology, they are describing a hybrid enterprise dominated by major Systems of Record complemented by Systems of Engagement to support optimized digital value streams.
Whereas Oleg Shilovitsky, coming from the System of Engagement side with OpenBOM, describes the potential technologies to build a digital enterprise as you can read from one of his recent posts: How to Unlock the Future of Manufacturing by Opening PLM/ERP to Connect Processes and Optimize Decision Support.
All three thought leaders talk about the potential of connected aspects in a future enterprise. For those interested in the details there is a lot to learn and understand.
For the sake of the migration story I stay out of the details. However interesting to mention, they also do not mention data migration—is it the elephant in the room?
I believe moving from a coordinated enterprise to a integrated (coordinated and connected) enterprise is not a migration, as we are no longer talking about a single system that serves the whole enterprise.
The future of a digital enterprise is a federated environment where existing systems need to become more data-driven, and additional collaboration environments will have their internally connected capabilities to support value streams.
With this in mind you can understand the 2017 McKinsey article– Our insights/toward an integrated technology operating model – the leading image below:
And when it comes to realization of such a concept, I have described the Heliple-2 project a few times before as an example of such an environment, where the target is to have a connection between the two layers through standardized interfaces, starting from OSLC. Or visit the Heliple Federated PLM LinkedIn group.
Data architecture and governance are crucial.
The image above generalizes the federated PLM concept and illustrates the two different systems connected through data bridges. As data must flow between the two sides without human intervention, the chosen architecture must be well-defined.
Here, I want to use a famous quote from Youssef Housmand’s paper From a Monolithic PLM Landscape to a Federated Domain and Data Mesh. Click on the image to listen to the Share PLM podcast with Yousef.
From a Single Source of Truth towards a principle of the Nearest Source of Truth based on a Single Source of Change
- If you agree with this quote, you have a future mindset of federated PLM.
- If you still advocate the Single Source of Truth, you are still in the Monolithic PLM phase.
It’s not a problem if you are aware that the next step should be federated and you are not ready yet.
However, in particular, environmental regulations and sustainability initiatives can only be performed in data-driven, federated environments. Think about the European Green Deal with its upcoming Ecodesign for Sustainable Products Directive (ESPR), which demands digital traceability of products, their environmental impact, and reuse /recycle options, expressed in the Digital Product Passport.
Reporting, Greenhouse Gas Reporting and ESG reporting are becoming more and more mandatory for companies, either by regulations or by the customers. Only a data-driven connected infrastructure can deal with this efficiently. Sustaira, a company we interviewed with the PLM Green Global Alliance last year, delivers such a connected infrastructure.
Read the challenges they meet in their blog post: Is inaccurate sustainability data holding you back?
Finally, to perform Life Cycle Assessments for design options or Life Cycle Analyses for operational products, you need connections to data sources in real-time. The virtual design twin or the digital twin in operation does not run on documents.
Conclusion
Data migration and consolidation to modern systems is probably a painful and challenging process. However, the good news is that with the right mindset to continue and with a focus on data quality and governance, the next step to a integrated coordinated and connected enterprise will not be that painful. It can be an evolutionary process, as the McKinsey article describes it.
In the past months, I have had several discussions related to migrating PLM data, either from one system to another or from consolidating a collection of applications into a single environment. Does this sound familiar?
Let me share some experiences and lessons learned to avoid the Migration Migraine.
It is not a technical guide but a collection of experiences and thoughts that you might have missed when considering to solve the technical dream.
Halfway I realized I was too ambitious; therefore, another post will follow this introduction. Here, I will focus on the business side and the digital transformation journey.
Garbage Out – Garbage In
The Garbage Out-In statement is somehow the paradigm we are used to in our day-to-day lives. When you buy a new computer, you use backup and restore. Even easier, nowadays, the majority of the data is already in the cloud.
This simple scenario assumes that all professional systems should be easily upgrade-able. We become unaware of the amount of data we store and its relevance.
This phenomenon already has a name: “Dark Data.” Dark Data consumes storage energy in the cloud and is no longer visible. Please read all about it here: Dark Data.
TIP 1: Every migration is a moment to clean up your data. By dragging everything with you, the burden of migrating becomes bigger. In easy migrations, do a clean-up—it prevents future, more extensive issues.
Never follow the Garbage Out – Garbage in principle, even if it is easy!
Migrations in the PLM domain are different – setting the scene.
Before discussing the various scenarios, let’s examine what companies are doing. For early PLM adopters in the Automotive, Aerospace, and Defense Industries, migrations from mainframes to modern infrastructures have become impossible. The real problem is not only the changing hardware but also the changing data and data models.
For these companies, the solution is often to build an entirely new PLM infrastructure on top of the existing infrastructure, where manageable data pieces are migrated to new environments using data lakes, dashboards, and custom apps to support modern users.
Migration in this case is a journey as long as the data lives – and we can learn from them!
Follow the money
From a business perspective, migrations are considered a negative distractor. Talking about them raises awareness of their complexity, which might jeopardize enthusiasm.
For the initiator, the PLM software vendor or implementer, it might endanger the sales deal.
Traditional IT organizations strive for simplification—one CAD, one PLM or one ERP system to manage. Although this argument makes sense, an analysis should always be done comparing the benefits and the (migration) costs and risks to reach the ideal situation.
In those discussions often, migrations are downplayed
Without naming companies, I have observed the downplaying several times, even at some prominent enterprises. So, if you recognize your company in this process, you are not alone.
TIP 2: Migrations are never simple. Make migration a serious topic of your PLM project – as important as the software. This approach means analyzing the potential migration risks and their mitigation is needed.
Please read about the Xylem story in my recent post: The week after the PDSFORUM 2024
The Big Bang has the highest risk and might again lead to garbage out—garbage in.
You are responsible for your garbage.
It may sound disparaging, but it is not. Most companies are aware that people, tools and policies have changed over the years. Due to the coordinated approach to working, disciplines did not need to care about downstream or upstream usage of their initially created data – Excel and PDFs are the bridges between disciplines.
All the actual knowledge and context are stored in the heads of experienced employees who have gotten used to dealing with inconsistencies. And they will retire, so there is an urgent need for actual data quality and governance. Read more about the journey from Coordinated to Connected in these articles.
Even if you are not yet thinking about migrations, the digital transformation in the PLM domain is coming, and we should learn to work in a connected mode.
TIP 3: Create a team in your organization that assesses the current data quality and defines the potential future enterprise (data) architecture. Then, start improving the quality of the current generated data. Like the ISO 900x standard, the ISO 8000 standard already exists for data quality.
The future is data-driven; prepare yourself for the future.
Migration scenarios and their best practices
Here are some migrations scenario’s – two in this post and more in an upcoming post.
From Relational to Object-oriented
One of my earlier projects, starting in 2010 with SmarTeam, was migrating a mainframe-based application for airplane certification to a modern Microsoft infrastructure.
The goal was to create a new environment that could be used both as a replacement for the mainframe application and as the design and validation environment to implement changes to the current airplanes during a maintenance or upgrade activity.
The need was high because detailed documentation about the logic of the current application did not exist, and only one person who understood the logic was partly available.
So, internally, the relational database was a black box. The tables in the database contained a mix of item data, document data, change status and versions. The documents were stored in directories with meaningful file names but disconnected from the application.
The initial estimate was that the project would take two to three months, so a fixed price for two months was agreed upon. However, it became almost a two-year project, and in the end, the result seemed to be reliable (there was never mathematical proof).
The disadvantage was that SmarTeam ended up being so highly customized that automatic upgrades would not work for this version anymore—a new legacy was created with modern technology.
The same story, combined with the example of Ericsson’s migration attempt, is described in the 2016 post, The PLM Migration Dilemma. For me, the lesson learned from these examples leads to the following recommendation.
TIP 4: When there is a paradigm change in the data model, don’t migrate but establish a new (data-driven) infrastructure and connect to your legacy as much as possible in read-only mode.
The automotive and aerospace industries’ story is one of paradigm change.
Listen to the SharePLM podcast Revolutionizing PLM: Insights from Yousef Hooshmand, where Yousef also discusses how to address this transition process.
CAD/PDM to PLM
Another migration step happens when companies decide to implement a traditional PLM infrastructure as a System of Record, merging PDM data (mainly CAD) and ERP data (the BOM).
Some of these companies have been working file-based and have stored their final CAD files in folders; others might have a local PDM system native to the 3D CAD. The EBOM usually existed digitally in ERP, and most of the time, it is not a “pure” EBOM but more of a hybrid EBOM/MBOM.
The image above show this type of migration can be very challenging as, in the source systems, there is not necessarily a consistent 3D CAD definition matching the BOM items. As the systems have been disconnected in the past, people have potentially added missing information or fixed information on the BOM side. As in most companies, the manufacturing definition is based on drawings, and the consistency with the 3D CAD definition is not guaranteed.
To address this challenge, companies need to assess the usability of the CAD and BOM data. Is it possible to populate the CAD files with properties that are necessary for an import? For example, does the file path contain helpful information?
I have experienced a situation where a company has poorly defined 3D parts and no properties, as all the focus was on using the 3D to generate the 2D drawing.
The relevant details for manufacturing were next added to the drawing and not anymore to the parts or models – traceability was almost impossible.
In this situation, importing the 3D CAD structures into the new PLM system has limited value. An alternative is to describe and test procedures for handling legacy data when it is needed, either to implement a design change or a new order. Leave the legacy accessible, but do not migrate.
The BOM side is, in theory, stable for manufactured products, as the data should have gone through a release process. However, the company needs to revisit its part definition process for new designs and products.
Some points to consider:
- Meaningful identifiers are not desired in a PLM system as they create a legacy. Therefore, the import of parts with smart identifiers should map to relevant part properties besides the ID. Splitting the ID into properties will create a broader usage in the future. Read more in Smart Part Numbers – do we need them?
- In addition, companies should try to avoid having logistic information, such as supplier-specific part numbers to come from the CAD system. Supplier parts in your CAD environment create inefficiencies when a supplier part becomes obsolete. Concepts such as EBOM and MBOM and potentially the SBOM should be well understood during this migration.
- Concepts of EBOM and MBOM should also be introduced when moving from an ETO to a CTO approach or when modularity is a future business strategy.
Conclusion
As every company is on its PLM journey and technology is evolving, there will always be a migration discussion. Understanding and working towards the future should be the most critical driver for migration. Migrations in the PLM domain are often more than a data migration – new ways of working should be introduced in parallel. And for that reason the “big bang” is often too costly and demotivating for the future.
Jos Voskuil's Weblog 
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