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Two weeks ago, I wrote a generic post related to System Thinking, in my opinion, a must-have skill for the 21st century (and beyond). Have a look at the post on LinkedIn; in particular interesting to see the discussion related to Systems Thinking: a must-have skill for the 21st century.
I liked Remy Fannader’s remark that thinking about complexity was not something new.
This remark is understandable from his personal context. Many people enjoy thinking – it was a respected 20th-century skill.
However, I believe, as Daniel Kahneman describes in his famous book: Thinking Fast and Slow, our brain is trying to avoid thinking.
This is because thinking consumes energy, the energy the body wants to save in the case of an emergency.
So let’s do a simple test (coming from Daniel):
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A bat and a ball cost together $ 1.10 – the bat costs one dollar more than the ball. So how much does the ball cost?
Look at the answer at the bottom of this post. If you have it wrong, you are a fast thinker. And this brings me to my next point. Our brain does not want to think deeply; we want fast and simple solutions. This is a challenge in a complex society as now we hear real-time information coming from all around the world. What is true and what is fake is hard to judge.
However, according to Kahneman, we do not want to waste energy on thinking. We create or adhere to simple solutions allowing our brains to feel relaxed.
This human behavior has always been exploited by populists and dictators: avoid complexity because, in this way, you lose people. Yuval Harari builds upon this with his claim that to align many people, you need a myth. I wrote about the need for myths in the PLM space a few times, e.g., PLM as a myth? and The myth perception
And this is where my second thoughts related to Systems Thinking started. Is the majority of people able and willing to digest complex problems?
My doubts grew bigger when I had several discussions about fighting climate change and sustainability.
Both Brains required
By coincidence, I bumped on this interesting article Market-led Sustainability is a ‘Fix that Fails’…
I provided a link to the post indirectly through LinkedIn. If you are a LinkedIn PLM Global Green Alliance member, you can see below the article an interesting analysis related to market-led sustainability, system thinking and economics.
Join the PLM Global Green Alliance group to be part of the full discussion; otherwise, I recommend you visit Both Brains Required, where you can find the source article and other related content.
It is a great article with great images illustrating the need for systems thinking and sustainability. All information is there to help you realize that sustainability is not just a left-brain exercise.
The left brain is supposed to be logical and analytical. That’s systems thinking, you might say quickly. However, the other part of our brain is about our human behavior, and this side is mostly overlooked. My favorite quote from the article:
Voluntary Market-Led activities are not so much a solution to the sustainability crisis as a symptom of more profoundly unsustainable foundations of human behavior.
The article triggered my second thoughts related to systems thinking. Behavioral change is not part of systems thinking. It is another dimension harder to address and even harder to focus on sustainability.
The LinkedIn discussion below the article Market-led Sustainability is a ‘Fix that Fails’… is a great example of the talks we would like to have in our PLM Global Green Alliance group. Nina Dar, Patrick Hillberg and Richard McFall brought in several points worth discussing. Too many to discuss them all here – let’s take two fundamental issues:
1. More than economics
An interesting viewpoint in this discussion was the relation to economics. We don’t believe that economic growth is the main point to measure. Even a statement like: “Sustainable businesses will be more profitable than traditional ones” is misleading when companies are measured by shareholder value or EBIT (Earnings Before Interest or Taxes). We briefly touched on Kate Raworth’s doughnut economics.
This HBR article mentioned in the discussion: Business Schools Must Do More to Address the Climate Crisis also shows it is not just about systems thinking.
We have seen this in the Apparel industry with the horrible collapse of a factory in Bangladesh (2013). Still, the inhumane accidents happen in southeast Asia. I like to quote Chris Calverley in his LinkedIn article: Making ethical apparel supply chains achievable on a global scale.
No one gets into business because they want to behave unethically. On the contrary, a lack of ethics is usually driven by a common desire to operate more efficiently and increase profit margins.
In my last post, I shared a similar example from an automotive tier 2 supplier. Unfortunately, suppliers are not measured or rewarded for sustainability efforts; only efficiency and costs are relevant.
The seventeen Sustainability Development Goals (SDG), as defined by the United Nations, are the best guidance for sustainable drivers beyond money. Supporting the SDGs enforce systems thinking when developing a part, a product, or a solution. Many other stakeholders need to be taken care of, at least if you truly support sustainability as a company.
2. The downside of social media
The LinkedIn discussion related to Market-led Sustainability is a ‘Fix that Fails’… The thread shows that LinkedIn, like other social media, is not really interested in supporting in-depth discussions – try to navigate what has been said in chronological order. With Patrick, Nina and Richard, we agreed to organize a follow-up discussion in our PLM Global Green Alliance Group.
And although we are happy with social media as it allows each of us to reach a global audience, there seems to be a worrying contra-productive impact. If you read the book Stolen Focus. A quote:
All over the world, our ability to pay attention is collapsing. In the US, college students now focus on one task for only 65 seconds, and office workers, on average, manage only three minutes
This is worrying, returning to Remy Fannader’s remark: thinking about complexity was not something new. The main difference is that it is not new. However, our society is changing towards thinking too fast, not rewarding systems thinking.
Even scarier, if you have time, read this article from The Atlantic: about the impact of social media on the US Society. It is about trust in science and data. Are we facing the new (Trump) Tower of Babel in our modern society? As the writers state: Babel is a metaphor for what some forms of social media have done to nearly all of the groups and institutions most important to the country’s future—and to us as a people.
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Congratulations
support
The fact that you reached this part of the post means your attention span has been larger than 3 minutes, showing there is hope for people like you and me. As an experiment to discover how many people read the post till here, please answer with the “support” icon if you have reached this part of the post.
I am curious to learn how many of us who saw the post came here.
Conclusion
Systems Thinking is a must-have skill for the 21st century. Many of us working in the PLM domain focus on providing support for systems thinking, particularly Life Cycle Assessment capabilities. However, the discussion with Patrick Hillberg, Nina Darr and Richard McFall made me realize there is more: economics and human behavior. For example, can we change our economic models, measuring companies not only for the money profit they deliver? What do?
Answering this type of question will be the extended mission for PLM consultants of the future – are you ready?
The answer to the question with the ball and the bat:
A fast answer would say the price of the ball is 10 cents. However, this would make the price of the bat $1.10, giving a total cost of $1.20. So the right answer should be 5 cents. To be honest, I got tricked the first time too. Never too late to confirm you make mistakes, as only people who do not do anything make no mistakes.
In the past four weeks, I have been discussing PLM education from different angles through interviews with Peter Bilello (CIMdata), Helena Gutierrez (Share PLM), John Stark (John Stark Associates) and Dave Slawson (Quick Release). Each of these persons brought their specialized focus on PLM.
In this post, I want to conclude and put their expertise in the context of PLM – people, processes and tools.
CIMdata
Originally CIMdata became known for their CAD/CAM market analysis, later expanding into simulation and PLM vendors analysis. And they are still a reference for everyone following the PLM Market. They provide market numbers and projections related to PLM for that part. Together with ARC, they are for me the two sources to understand what is happening business-wise in the PLM market.
Thanks to the contacts with all the vendors, they have a good overview of what is happening. That makes their strategic advice and training useful for companies that want to benchmark where they are and understand the current trends, all vendor-independent.
Their PLM Roadmap conferences have been one of the few consistent vendor-independent conferences that still take place.
If you search for the term “The weekend after PLM Roadmap …..” you will find many of my reviews of these conferences.
Besides these activities, they are also facilitating industry action groups where similar companies in an industry discuss and evaluate various methodologies and how they could be implemented using various PLM systems – the most visible for me is the Aerospace & Defense PLM Action Group
Share PLM
Share PLM is still a young organization focusing on Humanizing PLM. Their focus is on the end-to-end PLM education process. Starting from an education strategy focusing on people, they can organize and help you build attractive and didactical training or elearnings related to your PLM processes and systems in use.
Besides their core offering, they are also justifying their name; they really share PLM information. So have a look at their Our Work tab with samples. In particular, as I mentioned in my interview with them, I like their podcasts.
In this post, I try to find similar people or companies to those I interviewed.
When looking at Share PLM, Action Engineering in the US comes to my mind. They are the specialists dedicated to helping organizations large and small achieve their Model-Based Definition (MBD) and Model-Based Enterprise (MBE) goals.
To refresh your memory, read my post with Jennifer Herron, the founder of Action Engineering here: PLM and Model-Based Definition
John Stark
Although John might be known as a leading writer of PLM books, he is also active in advising companies in their PLM journeys. Somehow similar to what I do, the big difference is that John takes the time to structure the information and write it down in a book. Just have a look at his list of published PLM books here.
My blog posts are less structured and reflect my observations depending on the companies and people I meet. Writing a foundational book about PLM would be challenging, as concepts are radically changing due to globalization and digitization.
John’s books are an excellent foundation for students who want to learn PLM’s various aspects during their academic years. Students can sit down and take the time to study PLM concepts. Later, suppose you want to acquire PLM knowledge relevant to your company.
In that case, you might focus on specialized training, like the ones CIMdata provides.
There are many books on PLM – have a look at this list. Which book to read depends probably a lot on your country and the university you are associated with. In my network, I have recently seen books from Martin Eigner and Uthayan Elangovan. Rosemary Astheimer’s book Model-Based Definition in the Product Lifecycle is still on my to-read list.
And then, there is a lot of research done by universities worldwide. So, if you are lucky, there is good education for PLM-related practices in your country.
Quick Release
My post with Quick Release illustrated the challenges of a PLM consultancy company. It showed their efforts to enable their consultants to be valuable for their customers and create a work environment that inspires them to grow and enjoy their work.
Quick Release aims for a competitive advantage to have their consultants participate in actual work for their customers.
Not only from the conceptual point of view but also to get their hands “dirty”.
There are many other PLM consultancy firms. Having worked with Atos, Accenture, Capgemini, Delloite, PWC, who have their PLM practices, you realize that these companies have their methodologies and preferences. The challenge of their engagements is often the translation of a vision into an affordable roadmap.
Example of Accenture Digital PLM message
Consultancy firms need to be profitable, too, and sometimes they are portrayed as a virus. Once they are in, it is hard to get rid of them.
I do not agree with that statement, as companies often keep relying on consultants because they do not invest in educating their own people. It is a lack of management prioritization or understanding of the importance. Sometimes the argument is: “We are too busy” – remember the famous cartoons.
Consultants cannot change your company; in the end, you have to own the strategy and execution.
And although large consultancy firms might have many trained resources, my experience with these companies is that success often depends on one or two senior consultants. Consultancy is also a human-centric job, being able to connect to the customer in their language and culture.
Good consultants show their value by creating awareness and clarity first. Next, by helping the customer execute their strategy without big risks or hiccups. Finally, a good consultant becomes redundant as the knowledge has been transferred and digested to the customer.
It is like growing up.
System Integrators
It is a small step from consultancy firms to system integrators, as many consultancy firms have specialists in their company that are familiar with certain vendors’ systems. And you might have discovered that the systems that require the most integration or configuration work have the largest practices globally.
So I did a “quick and dirty” search on LinkedIn, looking for people with the xxx PLM consultant role, where xxx is the name of the PLM Vendor.
This to understand how big is the job market for such a specialized PLM consultant.
The image shows the result and I let you draw your own conclusions.
System Integrators are usually the most important partners for a PLM implementation once you choose. Therefore, when I support a PLM selection process, I always look at the potential implementation partner. Their experience, culture and scale are as important as selecting the best tools.
System Integrators can benefit from their past experiences and best practices. It is a myth that every company is so unique and should be treated differently. Instead, companies are different because of historical reasons. And these differences to best practices are sometimes inhibitors instead of advantages.
Related to education, System Integrators are often focused on technical training. Still, they might also have separate experts in training or organizational change management.
PLM Vendors
For me, the PLM vendors are the ones that should inspire the customers. Have a look at the “famous” CIMdata slide illustrating the relation between vision, technology and implemented practices – there is a growing gap between the leaders and the followers.
PLM Vendors often use their unique technical capabilities as a differentiator to the competition and inspiration for C-level management. Just think about the terms: Industry 4.0, Digital Twin, Digital Thread, Digital Platform, Model-Based Enterprise and more about sustainability targeted offerings.
The challenge however is to implement these concepts in a consistent manner, allowing people in an organization to understand why and what needs to be done.
The PLM editor’s business model is based on software sales or rental. Therefore, they will focus on their benefits and what competitors fail to do. And as they have the largest marketing budgets, they are the most visible in the PLM-related media.
Of course reality is not that dramatic – education is crucial
You can compare PLM Vendors also with populists. The aim of a populist is to create an audience by claiming they can solve your problems (easily) by using simple framing sentences. However, the reality is that the world and the current digitalization in the PLM domain are not simple.
Therefore we need education, education and education from different sources to build our own knowledge. It is not about the tool first. It is people, process and then tools/technology
People, Process, Tools
Education and the right education for each aspect of PLM are crucial to making the right decision. To simplify the education message, I tried to visualize and rate each paragraph along with the People, Process and Tools assessment.
What do you think? Does this make sense related to education?
Conclusion
Education is crucial at every level of an organization and at every stage of your career. Take your time to read and digest the information you see and compare and discuss it with others. Be aware of the People, Process and Tools matrix when retrieving information. Where does it apply, and why.
I believe PLM is considered complex because we are dealing with people who all have different educational backgrounds and, therefore, an opinion. Invest in alignment to ensure the processes and tools will be used best.
In my previous post, “My PLM Bookshelf,” on LinkedIn, I shared some of the books that influenced my thinking related to PLM. As you can see in the LinkedIn comments, other people added their recommendations for PLM-related books to get inspired or more knowledgeable.
Where reading a book is a personal activity, now I want to share with you how to get educated in a more interactive manner related to PLM. In this post, I talk with Peter Bilello, President & CEO of CIMdata. If you haven’t heard about CIMdata and you are active in PLM, more to learn on their website HERE. Now let us focus on Education.
CIMdata
Peter, knowing CIMdata from its research valid for the whole PLM community, I am curious to learn what is the typical kind of training CIMdata is providing to their customers.
Jos, throughout much of CIMdata’s existence, we have delivered educational content to the global PLM industry. With a core business tenant of knowledge transfer, we began offering a rich set of PLM-related tutorials at our North American and pan-European conferences starting in the earlier 1990s.
Since then, we have expanded our offering to include a comprehensive set of assessment-based certificate programs in a broader PLM sense. For example, systems engineering and digital transformation-related topics. In total, we offer more than 30 half-day classes. All of which can be delivered in-person as a custom configuration for a specific client and through public virtual-live or in-person classes. We have certificated more than 1,000 PLM professionals since the introduction in 2009 of this PLM Leadership offering.
Based on our experience, we recommend that an organization’s professional education strategy and plans address the organization’s specific processes and enabling technologies. This will help ensure that it drives the appropriate and consistent operations of its processes and technologies.
For that purpose, we expanded our consulting offering to include a comprehensive and strategic digital skills transformation framework. This framework provides an organization with a roadmap that can define the skills an organization’s employees need to possess to ensure a successful digital transformation.
In turn, this framework can be used as an efficient tool for the organization’s HR department to define its training and job progression programs that align with its overall transformation.
The success of training
We are both promoting the importance of education to our customers. Can you share with us an example where Education really made a difference? Can we talk about ROI in the context of training?
Jos, I fully agree. Over the years, we have learned that education and training are often minimized (i.e., sub-optimized). This is unfortunate and has usually led to failed or partially successful implementations.
In our view, both education and training are needed, along with strong organizational change management (OCM) and a quality assurance program during and after the implementation.
In our terms, education deals with the “WHY” and training with the “HOW”. Why do we need to change? Why do we need to do things differently? And then “HOW” to use new tools within the new processes.
We have seen far too many failed implementations where sub-optimized decisions were made due to a lack of understanding (i.e., a clear lack of education). We have also witnessed training and education being done too early or too late.
This leads to a reduced Return on Investment (ROI).
Therefore a well-defined skills transformation framework is critical for any company that wants to grow and thrive in the digital world. Finally, a skills transformation framework needs to be tied directly to an organization’s digital implementation roadmap and structure, state of the process, and technology maturity to maximize success.
Training for every size of the company?
When CIMdata conducts PLM training, is there a difference, for example, when working with a big global enterprise or a small and medium enterprise?
You might think the complexity might be similar; however, the amount of internal knowledge might differ. So how are you dealing with that?
We basically find that the amount of training/education required mostly depends on the implementation scope. Meaning the scope of the proposed digital transformation and the current maturity level of the impacted user community.
It is important to measure the current maturity and establish appropriate metrics to measure the success of the training (e.g., are people, once trained, using the tools correctly).
CIMdata has created a three-part PLM maturity model that allows an organization to understand its current PLM-related organizational, process, and technology maturity.
The three-part PLM maturity model
The PLM maturity model provides an important baseline for identifying and/or developing the appropriate courses for execution.
This also allows us, when we are supporting the definition of a digital skills transformation framework, to understand how the level of internal knowledge might differ within and between departments, sites, and disciplines. All of which help define an organization-specific action plan, no matter its size.
Where is CIMdata training different?
Most of the time, PLM implementers offer training too for their prospects or customers. So, where is CIMdata training different?
For this, it is important to differentiate between education and training. So, CIMdata provides education (the why) and training and education strategy development and planning.
We don’t provide training on how to use a specific software tool. We believe that is best left to the systems integrator or software provider.
While some implementation partners can develop training plans and educational strategies, they often fall short in helping an organization to effectively transform its user community. Here we believe training specialists are better suited.
Digital Transformation and PLM
One of my favorite topics is the impact of digitization in the area of product development. CIMdata introduced the Product Innovation Platform concept to differentiate from traditional PDM/PLM. Who needs to get educated to understand such a transformation, and what does CIMdata contribute to this understanding.
We often start with describing the difference between digitalization and digitization. This is crucial to be understood by an organization’s management team. In addition, management must understand that digitalization is an enterprise initiative.
It isn’t just about product development, sales, or enabling a new service experience. It is about maximizing a company’s ROI in applying and leveraging digital as needed throughout the organization. The only way an organization can do this successfully is by taking an end-to-end approach.
The Product Innovation Platform is focused on end-to-end product lifecycle management. Therefore, it must work within the context of other enterprise processes that are focused on the business’s resources (i.e., people, facilities, and finances) and on its transactions (e.g., purchasing, paying, and hiring).
As a result, an organization must understand the interdependencies among these domains. If they don’t, they will ultimately sub-optimize their investment. It is these and other important topics that CIMdata describes and communicates in its education offering.
The Product Innovation Platform in a digital enterprise
More than Education?
As a former teacher, I know that a one-time education, a good book or slide deck, is not enough to get educated. How does CIMdata provide a learning path or coaching path to their customers?
Jos, I fully agree. Sustainability of a change and/or improved way of working (i.e., long-term sustainability) is key to true and maximized ROI. Here I am referring to the sustainability of the transformation, which can take years.
With this, organizational change management (OCM) is required. OCM must be an integral part of a digital transformation program and be embedded into a program’s strategy, execution, and long-term usage. That means training, education, communication, and reward systems all have to be managed and executed on an ongoing basis.
For example, OCM must be executed alongside an organization’s digital skills transformation program. Our OCM services focus on strategic planning and execution support. We have found that most companies understand the importance of OCM, often don’t fully follow through on it.
A model-based future?
During the CIMdata Roadmap & PDT conferences, we have often discussed the importance of Model-Based Systems Engineering methodology as a foundation of a model-based enterprise. What do you see? Is it only the big Aerospace and Defense companies that can afford this learning journey, or should other industries also invest? And if yes, how to start.
Jos, here I need to step back for a minute. All companies have to deal with increasing complexity for their organization, supply chain, products, and more.
So, to optimize its business, an organization must understand and employ systems thinking and system optimization concepts. Unfortunately, most people think of MBSE as an engineering discipline. This is unfortunate because engineering is only one of the systems of systems that an organization needs to optimize across its end-to-end value streams.
The reality is all companies can benefit from MBSE. As long as they consider optimization across their specific disciplines, in the context of their products and services and where they exist within their value chain.
The MBSE is not just for Aerospace and Defense companies. Still, a lot can be learned from what has already been done. Also, leading automotive companies are implementing and using MBSE to design and optimize semi- and high-automated vehicles (i.e., systems of systems).
The starting point is understanding your systems of systems environment and where bottlenecks exist.
There should be no doubt, education is needed on MBSE and how MBSE supports the organization’s Model-Based Enterprise requirements.
Published work from the CIMdata administrated A&D PLM Action Group can be helpful. Also, various MBE and systems engineering maturity models, such as one that CIMdata utilizes in its consulting work.
Want to learn more?
Thanks, Peter, for sharing your insights. Are there any specific links you want to provide to get educated on the topics discussed? Perhaps some books to read or conferences to visit?
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Jos, as you already mentioned:
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- the CIMdata Roadmap & PDT conferences have provided a wealth of insight into this market for more than 25 years.
[Jos: Search for my blog posts starting with the text: “The weekend after ….”] - In addition, there are several blogs, like yours, that are worth following, and websites, like CIMdata’s pages for education or other resources which are filled with downloadable reading material.
- Additionally, there are many user conferences from PLM solution providers and third-party conferences, such as those hosted by the MarketKey organization in the UK.
These conferences have taken place in Europe and North America for several years. Information exchange and formal training and education are offered in many events. Additionally, they provide an excellent opportunity for networking and professional collaboration.
What I learned
Talking with Peter made me again aware of a few things. First, it is important to differentiate between education and training. Where education is a continuous process, training is an activity that must take place at the right time. Unfortunately, we often mix those two terms and believe that people are educated after having followed a training.
Secondly, investing in education is as crucial as investing in hard- or software. As Peter mentioned:
We often start with describing the difference between digitalization and digitization. This is crucial to be understood by an organization’s management team. In addition, management must understand that digitalization is an enterprise initiative.
System Thinking is not just an engineering term; it will be a mandate for managing a company, a product and even a planet into the future
Conclusion
This time a quote from Albert Einstein, supporting my PLM coaching intentions:
“Education is not the learning of facts
but the training of the mind to think.”
In my previous post, I discovered that my header for this series is confusing. Although a future implementation of system lifecycle management (SLM/PLM) will rely on models, the most foundational change needed is a technical one to create a data-driven infrastructure for connected ways of working.
My previous article discussed the concept of the dataset, which led to interesting discussions on LinkedIn and in my personal interactions. Also, this time Matthias Ahrens (HELLA) shared again a relevant but very academic article in this context – how to harmonize company information.
For those who want to dive deeper into the concept of connected datasets, read this article: The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information.
The article illustrates that the topic is relevant for all larger enterprises (and it is not an easy topic).
This time I want to share my thoughts about the two statements from my introductory post, i.e.:
A model-based approach with connected datasets seems to be the way forward. Managing data in documents will become inefficient as they cannot contribute to any digital accelerator, like applying algorithms. Artificial Intelligence relies on direct access to qualified data.
A model-based approach with connected datasets
We discussed connected datasets in the previous post; now, let’s explore why models and datasets are related. In the traditional CAD-centric PLM domain, most people will associate the word model with a CAD model, to be more precise, the 3D CAD Model. However, there are many other types of models used related to product development, delivery and operations.
A model can be a:
Physical Model
- A smaller-scale object for the first analysis, e.g., a city or building model, an airplane model
Conceptual Model
- A conceptual model describes the entities and their relations, e.g., a Process Flow Diagram (PFD)
A mathematical model describes a system concept using a mathematical language, e.g., weather or climate models. Modelica and MATLAB would fall in this category- A CGI (Computer Generated Imagery) or 3D CAD model is probably the most associated model in the mind of traditional PLM practitioners
- Functional and Logical Models describing the services and components of a system are crucial in an MBSE
Operational Model
- A model providing performance analysis based on (real-time) data coming from selected data sources. It could be an operational business model, an asset performance model; even my Garmin’s training performance model is such an operating model.
The list of all models above is not extensive nor academically defined. Moreover, some model term definitions might overlap, e.g., where would we classify software models or manufacturing models?
All models are a best-so-far approach to describing reality. Based on more accurate data from observations or measurements, the model comes closer to what happens in reality.
A model and its data
Never blame the model when there is a difference between what the model predicts and the observed reality. It is still a model. That’s why we need feedback loops from the actual physical world to the virtual world to fine-tune the model.
Part of what we call Artificial Intelligence is nothing more than applying algorithms to a model. The more accurate data available, the more “intelligent” the artificial intelligence solution will be.
By using data analysis complementary to the model, the model may get better and better through self-learning. Like our human brain, it starts with understanding the world (our model) and collecting experiences (improving our model).
There are two points I would like to highlight for this paragraph:
- A model is never 100 % the same as reality – so don’t worry about deviations. There will always be a difference between virtual predicted and physical measured – most of the time because reality has much more influencing parameters.
- The more qualified data we use in the model, the closer to reality – so focus on accurate (and the right) data for your model. Although, as most of the time, it is impossible to fully model a system, focus on the most significant data sources.
The ultimate goal: THE DIGITAL TWIN
The discussion related to data-driven and the usage of models might feel abstract and complex (and that’s the case). However the term “digital twin” is well known and even used in board rooms.
The great benefits of a digital twin for business operations and for sustainability are promoted by many software vendors and consultancy firms.
My statement and reason for this series of blog posts: Digital Twins do not run on documents, you need to have a data-driven, model-based infrastructure to efficiently benefit from digital twin concepts.
Unfortunate a reliable and sustainable implementation of a digital twin requires more than software – it is a learning journey to connect the right data to the right model.
A puzzle every company has to solve as there is no 100 percent blueprint at this time.
Are Low Code platforms the answer?
I mentioned the importance of accurate data. Companies have different systems or even platforms managing enterprise data. The digital dream is that by combining datasets from different systems and platforms, we can provide to any user the needed information in real-time. My statement from my introductory post was:
I don’t believe in Low-Code platforms that provide ad-hoc solutions on demand. The ultimate result after several years might be again a new type of spaghetti. On the other hand, standardized interfaces and protocols will probably deliver higher, long-term benefits. Remember: Low code: A promising trend or a Pandora’s Box?
Let’s look into some of the low-code platform messages mentioned by Low-Code advocates:
You will have an increasingly hard time finding developers to keep up with global app development demands (reason #1 for PEGA)
This statement reminded me of the early days of SmarTeam implementations. With a Data model Wizard, a Form Designer, and a Visual Basic COM API, you could create any kind of data management application with SmarTeam. By using its built-in behaviors for document lifecycle management, item lifecycle management, and CAD integrations combined with easy customizations.
The sky was the limit to satisfy end users. No need for an experienced partner or to be a skilled programmer (this was 2003+). SmarTeam was a low-code platform the marketing department would say now.
A lot of my activities between 2003 and 2010 were related fixing the problems related to flexibility, making sense (again) of customizations. I wrote about this in a 2015 post: The importance of a (PLM) data model sharing the experiences of “fixing” issues created to flexibility.
Think first
The challenge is that an enthusiastic team creates a (low code) solution rapidly. Immediate success is celebrated by the people involved. However, the future impact of this solution is often forgotten – we did the job, right?
Documentation and a broader visibility are often lacking when implementing such a solution.
For example, suppose your product data is going to be consumed by another app. In that case, you need to make sure that the information you consume is accurate. On the other hand, perhaps the information was valid when you created the app.
However, if your friendly co-worker has moved on to another job and someone with different data standards becomes responsible for the data you consume, the reliability might fail. So how do you guarantee its quality?
Easy tools have often led to spaghetti, starting from Clipper (the old days), Visual Basic (the less old days) to highly customizable systems (like Aras is promoting) and future low-code platforms (and Aras is there again).
However, the strength of being highly flexible is also the weaknesses if not managed and understood correctly. In particular, in a digital enterprise architecture, you need skilled people who guarantee a reliable anchorage of the solution.
The HBR article When Low-Code/No-Code Development Works — and When It Doesn’t mentions the same point:
There are great benefits from LC/NC software development, but management challenges as well. Broad use of these tools institutionalizes the “shadow IT” phenomenon, which has bedeviled IT organizations for decades — and could make the problem much worse if not appropriately governed. Citizen developers tend to create applications that don’t work or scale well, and then they try to turn them over to IT. Or the person may leave the company, and no one knows how to change or support the system they developed.
The fundamental difference: from coordinated to connected
For the moment, I remain skeptical about the low-code hype, because I have seen this kind of hype before. The most crucial point companies need to understand is that the coordinated world and the connected world are incompatible.
Using new tools based on old processes and existing data is not a digital transformation. Instead, a focus on value streams and their needed (connected) data should lead to the design of a modern digital enterprise, not the optimization and connectivity between organizational siloes.
Before buying a tool (a medicine) to reduce the current pains, imagine your future ways of working, discover what is possible with your existing infrastructure and identify the gaps.
Next, you need to analyze if these gaps are so significant that it requires a technology change. Probably it does, as historically, systems were not designed to share data horizontally in an organization.
In this context, have a look at Lionel Grealou’s s article for Engineering.com:
Data Readiness in the new age of digital collaboration.
Conclusion
We discussed the crucial relation between models and data. Models have only value if they acquire the right and accurate data (exercise 1).
Next, even the simplest development platforms, like low-code platforms, require brains and a long-term strategy (exercise 2) – nothing is simple at this moment in transformational times.
The next and final post in this series will focus on configuration management – a new approach is needed. I don’t have the answers, but I will share some thoughts
A recommended event and an exciting agenda and a good place to validate and share your thoughts.
I will be there and look forward to meeting you at this conference (unfortunate still virtually)
In my last post in this series, The road to model-based and connected PLM, I mentioned that perhaps it is time to talk about SLM instead of PLM when discussing popular TLA’s for our domain of expertise. There were not so many encouraging statements for SLM so far.
SLM could mean for me, Solution Lifecycle Management, considering that the company’s offering more and more is a mix of products and services. Or SLM could mean System Lifecycle Management, in that case pushing the idea that more and more products are interacting with the outside world and therefore could be considered systems. Products are (almost) dead.
In addition, I mentioned that the typical product lifecycle and related configuration management concepts need to change as in the SLM domain. There is hardware and software with different lifecycles and change processes.
It is a topic I want to explore further. I am curious to learn more from Martijn Dullaart, who will be lecturing at the PLM Road map and PDT 2021 fall conference in November. I hope my expectations are not too high, knowing it is a topic of interest for Martijn. Feel free to join this discussion
In this post, it is time to follow up on my third statement related to what data-driven implies:
Data-driven means that we need to manage data in a much more granular manner. We have to look different at data ownership. It becomes more about data accountability per role as the data can be used and consumed throughout the product lifecycle
On this topic, I have a list of points to consider; let’s go through them.
The dataset
In this post, I will often use the term dataset (you are also allowed to write the data set I understood).
A dataset means a predefined number of attributes and values that belong logically to each other. Datasets should be defined based on the purpose and, if possible, designated for a single goal. In this way, they can be stored in a database.
Combined with other datasets, a combination can result in relevant business information. Note a dataset is not only transactional data; a dataset could also describe geometry.
Identify the dataset
In the document-based world, a lot of information could be stored in a single file. In a data-driven world, we should define a dataset that contains a specific piece of information, logically belonging together. If we are more precise, a part would have various related datasets that make up the definition of a part. These definitions could be:
- Core identification attributes like ID, Name, Type and Status
- The Type could define a set of linked information. For example, a valve would have different characteristics as a resistor. Through classification, we can link data sets to the core definition of a part.
- The part can have engineering-specific data (CAD and metadata), manufacturing-specific data, supplier-specific data, and service-specific data. Each of these datasets needs to be defined as a unique element in a data-driven environment
- CAD is a particular case as most current CAD systems don’t treat geometry as a single dataset. In a file-based world, many other datasets are stored in the file (e.g., engineering or manufacturing details). In a data-driven environment, we want to have the CAD definition to be treated like a dataset. Dassault Systèmes with their CATIA V6 and 3DEXPERIENCE platform or PTC with OnShape are examples of this approach.Having CAD as separate datasets makes sharing and collaboration so much easier, as we can see from these solutions. The concept for CAD stored in a database is not new, and this approach has been used in various disciplines. Mechanical CAD was always a challenge.
Thanks to Moore’s Law (approximate every 2 years, processor power doubled – click on the image for the details) and higher network connection speed, it starts to make sense to have mechanical CAD also stored in a database instead of a file
An important point to consider is a kind of standardization of datasets. In theory, there should be a kind of minimum agreed collection of datasets. Industry standards provide these collections in their dictionary. Whenever you optimize your data model for a connected enterprise, make sure you look first into the standards that apply to your industry.
They might not be perfect or complete, but inventing your own new standard is a guarantee for legacy issues in the future. This remark is also valid for the software vendors in this domain. A proprietary data model might give you a competitive advantage.
Still, in the long term, there is always the need to connect with outside stakeholders.
Identify the RACI
To ensure a dataset is complete and well maintained, the concept of RACI could be used. RACI is the abbreviation for Responsible Accountable Consulted and Informed and a simplification of the RASCI Model, see also a responsibility assignment matrix.
In a data-driven environment, there is no data ownership anymore like you have for documents. The main reason that data ownership can no longer be used is that datasets can be consumed by anyone in the ecosystem. No longer only your department or the manufacturing or service department.
Data sets in a data-driven environment bring value when connected with other datasets in applications or dashboards.
A dataset describing the specification attributes of a part could be used in a spare part app and a service app. Of course, the dataset will be used in a different context – still, we need to ensure we can trust the data.
Therefore, per identified dataset, there should be governed by a kind of RACI concept. The RACI concept is a way to break the siloes in an organization.
Identify Inside / outside
There is a lot of fear that a connected, data-driven environment will expose Intellectual Property (IP). It came up in recent discussions. If you like storytelling and technology, read my old SmarTeam colleague Alex Bruskin’s post: The Bilbo Baggins Threat to PLM Assets. Alex has written some “poetry” with a deep technical message behind it.
It is true that if your data set is too big, you have the challenge of exposing IP when connecting this dataset with others. Therefore, when building a data model, you should make it possible to have datasets pure for internal usage and datasets for sharing.
When you use the concept of RACI, the difference should be defined by the I(informed) – is it PLM-data or PIM-data for example?
Tracking relations
Suppose we follow up on the concept of datasets. In that case, it becomes clear that relations between the datasets are as crucial as the dataset. In traditional PLM applications, these relations are often predefined as part of the core data model/
For example, the EBOM parts have relationships between themselves and specification data – see image.
The MBOM parts have links with the supplier data or the manufacturing process.
The prepared relations in a PLM system allow people to implement the system relatively quickly to map their approaches to this taxonomy.
However, traditional PLM systems are based on a document-based (or file-based) taxonomy combined with related metadata. In a model-based and connected environment, we have to get rid of the document-based type of data.
Therefore, the datasets will be more granular, and there is a need to manage exponential more relations between datasets.
This is why you see the graph database coming up as a needed infrastructure for modern connected applications. If you haven’t heard of a graph database yet, you are probably far from technology hypes. To understand the principles of a graph database you can read this article from neo4j: Graph Databases for Beginners: Why graph technology is the future
As you can see from the 2020 Gartner Hype Cycle for Artificial Intelligence this technology is at the top of the hype and conceptually the way to manage a connected enterprise. The discussion in this post also demonstrates that besides technology there is a lot of additional conceptual thinking needed before it can be implemented.
Although software vendors might handle the relations and datasets within their platform, the ultimate challenge will be sharing datasets with other platforms to get a connected ecosystem.
For example, the digital web picture shown above and introduced by Marc Halpern at the 2018 PDT conference shows this concept. Recently CIMdata discussed this topic in a similar manner: The Digital Thread is Really a Web, with the Engineering Bill of Materials at Its Center
(Note I am not sure if CIMdata has published a recording of this webinar – if so I will update the link)
Anyway, these are signs that we started to find the right visuals to imagine new concepts. The traditional digital thread pictures, like the one below, are, for me, impressions of the past as they are too rigid and focusing on some particular value streams.
From a distance, it looks like a connected enterprise should work like our brain. We story information on different abstraction levels. We keep incredibly many relations between information elements. As the brain is a biological organ, connections degrade or get lost. Or the opposite other relationships become so strong that we cannot change them anymore. (“I know I am always right”)
Interestingly, the brain does not use the “single source of truth”-concept – there can be various “truths” inside a brain. This makes us human beings with all the good and the harmful effects of that.
As long as we realize there is no single source of truth.
In business and our technological world, we need sometimes the undisputed truth. Blockchain could be the basis for securing the right connections between datasets to guarantee the result is valid. I am curious if blockchain can scale to complex connected situations, although Moore’s Law might ultimately help us here too(if still valid).
The topic is not new – in 2014 I wrote a post with the title: PLM is doomed unless …. Where I introduced the topic of owning and sharing in the context of the human brain. In the post, I refer to the book On Intelligence by Jeff Hawkins how tries to analyze what is human-based intelligence and how could we apply it to our technology concepts. Still a fascinating book worth reading if you have the time and opportunity.
Conclusion
A data-driven approach requires a more granular definition of information, leading to the concepts of datasets and managing relations between datasets. This is a fundamental difference compared to the past, where we were operating systems with information. Now we are heading towards connected platforms that provide a filtered set of real-time data to act upon.
I am curious to learn more about how people have solved the connected challenges and in what kind of granularity. Let us know!
In my last post, I zoomed in on a preferred technical architecture for the future digital enterprise. Drawing the conclusion that it is a mission impossible to aim for a single connected environment. Instead, information will be stored in different platforms, both domain-oriented (PLM, ERP, CRM, MES, IoT) and value chain oriented (OEM, Supplier, Marketplace, Supply Chain hub).
In part 3, I posted seven statements that I will be discussing in this series. In this post, I will zoom in on point 2:
Data-driven does not mean we do not need any documents anymore. Read electronic files for documents. Likely, document sets will still be the interface to non-connected entities, suppliers, and regulatory bodies. These document sets can be considered a configuration baseline.
System of Record and System of Engagement
In the image below, a slide from 2016, I show a simplified view when discussing the difference between the current, coordinated approach and the future, connected approach. This picture might create the wrong impression that there are two different worlds – either you are document-driven, or you are data-driven.
In the follow-up of this presentation, I explained that companies need both environments in the future. The most efficient way of working for operations will be infrastructure on the right side, the platform-based approach using connected information.
For traceability and disconnected information exchanges, the left side will be there for many years to come. Systems of Record are needed for data exchange with disconnected suppliers, disconnected regulatory bodies and probably crucial for configuration management.
The System of Record will probably remain as a capability in every platform or cross-section of platform information. The Systems of Engagement will be the configured real-time environment for anyone involved in active company processes, not only ERP or MES, all execution.
Introducing SysML and SML
This summer, I received a copy of Martin Eigner’s System Lifecycle Management book, which I am reading at his moment in my spare moments. I always enjoyed Martin’s presentations. In many ways, we share similar ideas. Martin from his profession spent more time on the academic aspects of product and system lifecycle than I. But, on the other hand, I have always been in the field observing and trying to make sense of what I see and learn in a coherent approach. I am halfway through the book now, and for sure, I will come back on the book when I have finished.
A first impression: A great and interesting book for all. Martin and I share the same history of data management. Read all about this in his second chapter: Forty Years of Product Data Management
From PDM via PLM to SysLM, is a chapter that everyone should read when you haven’t lived it yourself. It helps you to understand the past (Learning for the past to understand the future). When I finish this series about the model-based and connected approach for products and systems, Martin’s book will be highly complementary given the content he describes.
There is one point for which I am looking forward to is feedback from the readers of this blog.
Should we, in our everyday language, better differentiate between Product Lifecycle Management (PLM) and System Lifecycle Management(SysLM)?
In some customer situations, I talk on purpose about System Lifecycle Management to create the awareness that the company’s offering is more than an electro/mechanical product. Or ultimately, in a more circular economy, would we use the term Solution Lifecycle Management as not only hardware and software might be part of the value proposition?
Martin uses consistently the abbreviation SysLM, where I would prefer the TLA SLM. The problem we both have is that both abbreviations are not unique or explicit enough. SysLM creates confusion with SysML (for dyslectic people or fast readers). SLM already has so many less valuable meanings: Simulation Lifecycle Management, Service Lifecycle Management or Software Lifecycle Management.
For the moment, I will use the abbreviation SLM, leaving it in the middle if it is System Lifecycle Management or Solution Lifecycle Management.
How to implement both approaches?
In the long term, I predict that more than 80 percent of the activities related to SLM will take place in a data-driven, model-based environment due to the changing content of the solutions offered by companies.
A solution will be based on hardware, the solid part of the solution, for which we could apply a BOM-centric approach. We can see the BOM-centric approach in most current PLM implementations. It is the logical result of optimizing the product lifecycle management processes in a coordinated manner.
However, the most dynamic part of the solution will be covered by software and services. Changing software or services related to a solution has completely different dynamics than a hardware product.
Software and services implementations are associated with a data-driven, model-based approach.
The management of solutions, therefore, needs to be done in a connected manner. Using the BOM-centric approach to manage software and services would create a Kafkaesque overhead.
Depending on your company’s value proposition to the market, the challenge will be to find the right balance. For example, when you keep on selling “disconnected” hardware, there is probably no need to change your internal PLM processes that much.
However, when you are moving to a “connected” business model providing solutions (connected systems / Outcome-based services), you need to introduce new ways of working with a different go-to-market mindset. No longer linear, but iterative.
A McKinsey concept, I have been promoting several times, illustrates a potential path – note the article was not written with a PLM mindset but in a business mindset.
What about Configuration Management?
The different datasets defining a solution also challenge traditional configuration management processes. Configuration Management (CM) is well established in the aerospace & defense industry. In theory, proper configuration management should be the target of every industry to guarantee an appropriate performance, reduced risk and cost of fixing issues.
The challenge, however, is that configuration management processes are not designed to manage systems or solutions, where dynamic updates can be applied whether or not done by the customer.
This is a topic to solve for the modern Connected Car (system) or Connected Car Sharing (solution)
For that reason, I am inquisitive to learn more from Martijn Dullaart’s presentation at the upcoming PLM Roadmap/PDT conference. The title of his session: The next disruption please …
In his abstract for this session, Martijn writes:
From Paper to Digital Files brought many benefits but did not fundamentally impact how Configuration Management was and still is done. The process to go digital was accelerated because of the Covid-19 Pandemic. Forced to work remotely was the disruption that was needed to push everyone to go digital. But a bigger disruption to CM has already arrived. Going model-based will require us to reexamine why we need CM and how to apply it in a model-based environment. Where, from a Configuration Management perspective, a digital file still in many ways behaves like a paper document, a model is something different. What is the deliverable? How do you manage change in models? How do you manage ownership? How should CM adopt MBx, and what requirements to support CM should be considered in the successful implementation of MBx? It’s time to start unraveling these questions in search of answers.
One of the ideas I am currently exploring is that we need a new layer on top of the current configuration management processes extending the validation to software and services. For example, instead of describing every validated configuration, a company might implement the regular configuration management processes for its hardware.
Next, the systems or solutions in the field will report (or validate) their configuration against validation rules. A topic that requires a long discussion and more than this blog post, potentially a full conference.
Therefore I am looking forward to participating in the CIMdata/PDT FALL conference and pick-up the discussions towards a data-driven, model-based future with the attendees. Besides CM, there are several other topics of great interest for the future. Have a look at the agenda here
Conclusion
A data-driven and model-based infrastructure still need to be combined with a coordinated, document-driven infrastructure. Where the focus will be, depends on your company’s value proposition.
If we discuss hardware products, we should think PLM. When you deliver systems, you should perhaps talk SysML (or SLM). And maybe it is time to define Solution Lifecycle Management as the term for the future.
Please, share your thoughts in the comments.
After a short summer break with almost no mentioning of the word PLM, it is time to continue this series of posts exploring the future of “connected” PLM. For those who also started with a cleaned-up memory, here is a short recap:
In part 1, I rush through more than 60 years of product development, starting from vellum drawings ending with the current PLM best practice for product development, the item-centric approach.
In part 2, I painted a high-level picture of the future, introducing the concept of digital platforms, which, if connected wisely, could support the digital enterprise in all its aspects. The five platforms I identified are the ERP and CRM platform (the oldest domains).
Next, the MES and PIP platform(modern domains to support manufacturing and product innovation in more detail) and the IoT platform (needed to support connected products and customers).
In part 3, I explained what is data-driven and how data-driven is closely connected to a model-based approach. Here we abandon documents (electronic files) as active information carriers. Documents will remain, however, as reports, baselines, or information containers. In this post, I ended up with seven topics related to data-driven, which I will discuss in upcoming posts.
Hopefully, by describing these topics – and for sure, there are more related topics – we will better understand the connected future and make decisions to enable the future instead of freezing the past.
Topic 1 for this post:
Data-driven does not imply, there needs to be a single environment, a single database that contains all information. As I mentioned in my previous post, it will be about managing connected datasets federated. It is not anymore about owned the data; it is about access to reliable data.
Platform or a collection of systems?
One of the first (marketing) hurdles to take is understanding what a data platform is and what is a collection of systems that work together, sold as a platform.
CIMdata published in 2017 an excellent whitepaper positioning the PIP (Product Innovation Platform): Product Innovation Platforms: Definition, Their Role in the Enterprise, and Their Long-Term Viability. CIMdata’s definition is extensive and covers the full scope of product innovation. Of course, you can find a platform that starts from a more focused process.
For example, look at OpenBOM (focus on BOM collaboration), OnShape (focus on CAD collaboration) or even Microsoft 365 (historical, document-based collaboration).
The idea behind a platform is that it provides basic capabilities connected to all stakeholders, inside and outside your company. In addition, to avoid that these capabilities are limited, a platform should be open and able to connect with other data sources that might be either local or central available.
From these characteristics, it is clear that the underlying infrastructure of a platform must be based on a multitenant SaaS infrastructure, still allowing local data to be connected and shielded for performance or IP reasons.
The picture below describes the business benefits of a Product Innovation Platform as imagined by Accenture in 2014
Link to CIMdata’s 2014 commentary of Digital PLM HERE
Sometimes vendors sell their suite of systems as a platform. This is a marketing trick because when you want to add functionality to your PLM infrastructure, you need to install a new system and create or use interfaces with the existing systems, not really a scalable environment.
In addition, sometimes, the collaboration between systems in such a marketing platform is managed through proprietary exchange (file) formats.
A practice we have seen in the construction industry before cloud connectivity became available. However, a so-called end-to-end solution working on PowerPoint implemented in real life requires a lot of human intervention.
Not a single environment
There has always been the debate:
“Do I use best-in-class tools, supporting the end-user of the software, or do I provide an end-to-end infrastructure with more generic tools on top of that, focusing on ease of collaboration?”
In the system approach, the focus was most of the time on the best-in-class tools where PLM-systems provide the data governance. A typical example is the item-centric approach. It reflects the current working culture, people working in their optimized siloes, exchanging information between disciplines through (neutral) files.
The platform approach makes it possible to deliver the optimized user interface for the end-user through a dedicated app. Assuming the data needed for such an app is accessible from the current platform or through other systems and platforms.
It might be tempting as a platform provider to add all imaginable data elements to their platform infrastructure as much as possible. The challenge with this approach is whether all data should be stored in a central data environment (preferably cloud) or federated. And what about filtering IP?
In my post PLM and Supply Chain Collaboration, I described the concept of having an intermediate hub (ShareAspace) between enterprises to facilitate real-time data sharing, however carefully filtered which data is shared in the hub.
It may be clear that storing everything in one big platform is not the future. As I described in part 2, in the end, a company might implement a maximum of five connected platforms (CRM, ERP, PIP, IoT and MES). Each of the individual platforms could contain a core data model relevant for this part of the business. This does not imply there might be no other platforms in the future. Platforms focusing on supply chain collaboration, like ShareAspace or OpenBOM, will have a value proposition too. In the end, the long-term future is all about realizing a digital tread of information within the organization.
Will we ever reach a perfectly connected enterprise or society? Probably not. Not because of technology but because of politics and human behavior. The connected enterprise might be the most efficient architecture, but will it be social, supporting all humanity. Predicting the future is impossible, as Yuval Harari described in his book: 21 Lessons for the 21st Century. Worth reading, still a collection of ideas.
Proprietary data model or standards?
So far, when you are a software vendor developing a system, there is no restriction in how you internally manage your data. In the domain of PLM, this meant that every vendor has its own proprietary data model and behavior.
I have learned from my 25+ years of experience with systems that the original design of a product combined with the vendor’s culture defines the future roadmap. So even if a PLM vendor would rewrite all their software to become data-driven, the ways of working, the assumptions will be based on past experiences.
This makes it hard to come to unified data models and methodology valid for our PLM domain. However, large enterprises like Airbus and Boeing and the major Automotive suppliers have always pushed for standards as they will benefit the most from standardization.
The recent PDT conferences were an example of this, mainly the 2020 Fall conference. Several Aerospace & Defense PLM Action groups reported their progress.
You can read my impression of this event in The weekend after PLM Roadmap / PDT 2020 – part 1 and The next weekend after PLM Roadmap PDT 2020 – part 2.
It would be interesting to see a Product Innovation Platform built upon a data model as much as possible aligned to existing standards. Probably it won’t happen as you do not make money from being open and complying with standards as a software vendor. Still, companies should push their software vendors to support standards as this is the only way to get larger connected eco-systems.
I do not believe in the toolkit approach where every company can build its own data model based on its current needs. I have seen this flexibility with SmarTeam in the early days. However, it became an upgrade risk when new, overlapping capabilities were introduced, not matching the past.
In addition, a flexible toolkit still requires a robust data model design done by experienced people who have learned from their mistakes.
The benefit of using standards is that they contain the learnings from many people involved.
Conclusion
I did not like writing this post so much, as my primary PLM focus lies on people and methodology. Still, understanding future technologies is an important point to consider. Therefore, this time a not-so-exciting post. There is enough to read on the internet related to PLM technology; see some of the recent articles below. Enjoy
Matthias Ahrens shared: Integrated Product Lifecycle Management (Google translated from German)
Oleg Shilovitsky wrote numerous articles related to technology –
in this context:
3 Challenges of Unified Platforms and System Locking and
SaaS PLM Acceleration Trends
My previous post introducing the concept of connected platforms created some positive feedback and some interesting questions. For example, the question from Maxime Gravel:
Thank you, Jos, for the great blog. Where do you see Change Management tool fit in this new Platform ecosystem?
is one of the questions I try to understand too. You can see my short comment in the comments here. However, while discussing with other experts in the CM-domain, we should paint the path forward. Because if we cannot solve this type of question, the value of connected platforms will be disputable.
It is essential to realize that a digital transformation in the PLM domain is challenging. No company or vendor has the perfect blueprint available to provide an end-to-end answer for a connected enterprise. In addition, I assume it will take 10 – 20 years till we will be familiar with the concepts.
It takes a generation to move from drawings to 3D CAD. It will take another generation to move from a document-driven, linear process to data-driven, real-time collaboration in an iterative manner. Perhaps we can move faster, as the Automotive, Aerospace & Defense, and Industrial Equipment industries are not the most innovative industries at this time. Other industries or startups might lead us faster into the future.
Although I prefer discussing methodology, I believe before moving into that area, I need to clarify some more technical points before moving forward. My apologies for writing it in such a simple manner. This information should be accessible for the majority of readers.
What means data-driven?
I often mention a data-driven environment, but what do I mean precisely by that. For me, a data-driven environment means that all information is stored in a dataset that contains a single aspect of information in a standardized manner, so it becomes accessible by outside tools.
A document is not a dataset, as often it includes a collection of datasets. Most of the time, the information it is exposed to is not standardized in such a manner a tool can read and interpret the exact content. We will see that a dataset needs an identifier, a classification, and a status.
An identifier to be able to create a connection between other datasets – traceability or, in modern words, a digital thread.
A classification as the classification identifier will determine the type of information the dataset contains and potential a set of mandatory attributes
A status to understand if the dataset is stable or still in work.
Examples of a data-driven approach – the item
The most common dataset in the PLM world is probably the item (or part) in a Bill of Material. The identifier is the item number (ID + revision if revisions are used). Next, the classification will tell you the type of part it is.
Part classification can be a topic on its own, and every industry has its taxonomy.
Finally, the status is used to identify if the dataset is shareable in the context of other information (released, in work, obsolete), allowing tools to expose only relevant information.
In a data-driven manner, a part can occur in several Bill of Materials – an example of a single definition consumed in other places.
When the part information changes, the accountable person has to analyze the relations to the part, which is easy in a data-driven environment. It is normal to find this functionality in a PDM or ERP system.
When the part would change in a document-driven environment, the effort is much higher.
First, all documents need to be identified where this part occurs. Then the impact of change needs to be managed in document versions, which will lead to other related changes if you want to keep the information correct.
Examples of a data-driven approach – the requirement
Another example illustrating the benefits of a data-driven approach is implementing requirements management, where requirements become individual datasets. Often a product specification can contain hundreds of requirements, addressing the needs of different stakeholders.
In addition, several combinations of requirements need to be handled by other disciplines, mechanical, electrical, software, quality and legal, for example.
As requirements need to be analyzed and ranked, a specification document would never be frozen. Trade-off analysis might lead to dropping or changing a single requirement. It is almost impossible to manage this all in a document, although many companies use Excel. The disadvantages of Excel are known, in particular in a dynamic environment.
The advantage of managing requirements as datasets is that they can be grouped. So, for example, they can be pushed to a supplier (as a specification).
Or requirements could be linked to test criteria and test cases, without the need to manage documents and make sure you work with them last updated document.
As you will see, also requirements need to have an Identifier (to manage digital relations), a classification (to allow grouping) and a status (in work / released /dropped)
Data-driven and Models – the 3D CAD model
3D PDF Model
When I launched my series related to the model-based approach in 2018, the first comments I got came from people who believed that model-based equals the usage of 3D CAD models – see Model-based – the confusion. 3D Models are indeed an essential part of a model-based infrastructure, as the 3D model provides an unambiguous definition of the physical product. Just look at how most vendors depict the aspects of a virtual product using 3D (wireframe) models.
Although we use a 3D representation at each product lifecycle stage, most companies do not have a digital continuity for the 3D representation. Design models are often too heavy for visualization and field services support. The connection between engineering and manufacturing is usually based on drawings instead of annotated models.
I wrote about modern PLM and Model-Based Definition, supported by Jennifer Herron from Action Engineering – read the post PLM and Model-Based Definition here.
If your company wants to master a data-driven approach, this is one of the most accessible learning areas. You will discover that connecting engineering and manufacturing requires new technology, new ways of working and much more coordination between stakeholders.
Implementing Model-Based Definition is not an easy process. However, it is probably one of the best steps to get your digital transformation moving. The benefits of connected information between engineering and manufacturing have been discussed in the blog post PLM and Model-Based Definition
Essential to realize all these exciting capabilities linked to Industry 4.0 require a data-driven, model-based connection between engineering and manufacturing.
If this is not the case, the projected game-changers will not occur as they become too costly.
Data-driven and mathematical models
To manage complexity, we have learned that we have to describe the behavior in models to make logical decisions. This can be done in an abstract model, purely based on mathematical equations and relations. For example, suppose you look at climate models, weather models or COVID infections models.
In that case, we see they all lead to discussions from so-called experts that believe a model should be 100 % correct and any exception shows the model is wrong.
It is not that the model is wrong; the expectations are false.
For less complex systems and products, we also use models in the engineering domain. For example, logical models and behavior models are all descriptive models that allow people to analyze the behavior of a product.
For example, how software code impacts the product’s behavior. Usually, we speak about systems when software is involved, as the software will interact with the outside world.
There can be many models related to a product, and if you want to get an impression, look at this page from the SEBoK wiki: Types of Models. The current challenge is to keep the relations between these models by sharing parameters.
The sharable parameters then again should be datasets in a data-driven environment. Using standardized diagrams, like SysML or UML, enables the used objects in the diagram to become datasets.
I will not dive further into the modeling details as I want to remain at a high level.
Essential to realize digital models should connect to a data-driven infrastructure by sharing relevant datasets.
What does data-driven imply?
I want to conclude this time with some statements to elaborate on further in upcoming posts and discussions
- Data-driven does not imply there needs to be a single environment, a single database that contains all information. Like I mentioned in my previous post, it will be about managing connected datasets in a federated manner. It is not anymore about owned the data; it is about access to reliable data.
- Data-driven does not mean we do not need any documents anymore. Read electronic files for documents. Likely, document sets will still be the interface to non-connected entities, suppliers, and regulatory bodies. These document sets can be considered a configuration baseline.
- Data-driven means that we need to manage data in a much more granular manner. We have to look different at data ownership. It becomes more data accountability per role as the data can be used and consumed throughout the product lifecycle.
- Data-driven means that you need to have an enterprise architecture, data governance and a master data management (MDM) approach. So far, the traditional PLM vendors have not been active in the MDM domain as they believe their proprietary data model is leading. Read also this interesting McKinsey article: How enterprise architects need to evolve to survive in a digital world
- A model-based approach with connected datasets seems to be the way forward. Managing data in documents will become inefficient as they cannot contribute to any digital accelerator, like applying algorithms. Artificial Intelligence relies on direct access to qualified data.
- I don’t believe in Low-Code platforms that provide ad-hoc solutions on demand. The ultimate result after several years might be again a new type of spaghetti. On the other hand, standardized interfaces and protocols will probably deliver higher, long-term benefits. Remember: Low code: A promising trend or a Pandora’s Box?
- Configuration Management requires a new approach. The current methodology is very much based on hardware products with labor-intensive change management. However, the world of software products has different configuration management and change procedure. Therefore, we need to merge them in a single framework. Unfortunately, this cannot be the BOM framework due to the dynamics in software changes. An interesting starting point for discussion can be found here: Configuration management of industrial products in PDM/PLM
Conclusion
Again, a long post, slowly moving into the future with many questions and points to discuss. Each of the seven points above could be a topic for another blog post, a further discussion and debate.
After my summer holiday break in August, I will follow up. I hope you will join me in this journey by commenting and contributing with your experiences and knowledge.
So far, I have been discussing PLM experiences and best practices that have changed due to introducing electronic drawings and affordable 3D CAD systems for the mainstream. From vellum to PDM to item-centric PLM to manage product designs and manufacturing specifications.
Although the technology has improved, the overall processes haven’t changed so much. As a result, disciplines could continue to work in their own comfort zone, most of the time hidden and disconnected from the outside world.
Now, thanks to digitalization, we can connect and format information in real-time. Now we can provide every stakeholder in the company’s business to have almost real-time visibility on what is happening (if allowed). We have seen the benefits of platformization, where the benefits come from real-time connectivity within an ecosystem.
Apple, Amazon, Uber, Airbnb are the non-manufacturing related examples. Companies are trying to replicate these models for other businesses, connecting the concept owner (OEM ?), with design and manufacturing (services), with suppliers and customers. All connected through information, managed in data elements instead of documents – I call it connected PLM
Vendors have already shared their PowerPoints, movies, and demos from how the future would be in the ideal world using their software. The reality, however, is that implementing such solutions requires new business models, a new type of organization and probably new skills.
The last point is vital, as in schools and organizations, we tend to teach what we know from the past as this gives some (fake) feeling of security.
The reality is that most of us will have to go through a learning path, where skills from the past might become obsolete; however, knowledge of the past might be fundamental.
In the upcoming posts, I will share with you what I see, what I deduct from that and what I think would be the next step to learn.
I firmly believe connected PLM requires the usage of various models. Not only the 3D CAD model, as there are so many other models needed to describe and analyze the behavior of a product.
I hope that some of my readers can help us all further on the path of connected PLM (with a model-based approach). This series of posts will be based on the max size per post (avg 1500 words) and the ideas and contributes coming from you and me.
What is platformization?
In our day-to-day life, we are more and more used to direct interaction between resellers and services providers on one side and consumers on the other side. We have a question, and within 24 hours, there is an answer. We want to purchase something, and potentially the next day the goods are delivered. These are examples of a society where all stakeholders are connected in a data-driven manner.
We don’t have to create documents or specialized forms. An app or a digital interface allows us to connect. To enable this type of connectivity, there is a need for an underlying platform that connects all stakeholders. Amazon and Salesforce are examples for commercial activities, Facebook for social activities and, in theory, LinkedIn for professional job activities.
The platform is responsible for direct communication between all stakeholders.
The same applies to businesses. Depending on the products or services they deliver, they could benefit from one or more platforms. The image below shows five potential platforms that I identified in my customer engagements. Of course, they have a PLM focus (in the middle), and the grouping can be made differently.
Five potential business platforms
The 5 potential platforms
The ERP platform
is mainly dedicated to the company’s execution processes – Human Resources, Purchasing, Finance, Production scheduling, and potentially many more services. As platforms try to connect as much as possible all stakeholders. The ERP platform might contain CRM capabilities, which might be sufficient for several companies. However, when the CRM activities become more advanced, it would be better to connect the ERP platform to a CRM platform. The same logic is valid for a Product Innovation Platform and an ERP platform. Examples of ERP platforms are SAP and Oracle (and they will claim they are more than ERP)
Note: Historically, most companies started with an ERP system, which is not the same as an ERP platform. A platform is scalable; you can add more apps without having to install a new system. In a platform, all stored data is connected and has a shared data model.
The CRM platform
a platform that is mainly focusing on customer-related activities, and as you can see from the diagram, there is an overlap with capabilities from the other platforms. So again, depending on your core business and products, you might use these capabilities or connect to other platforms. Examples of CRM platforms are Salesforce and Pega, providing a platform to further extend capabilities related to core CRM.
The MES platform
In the past, we had PDM and ERP and what happened in detail on the shop floor was a black box for these systems. MES platforms have become more and more important as companies need to trace and guide individual production orders in a data-driven manner. Manufacturing Execution Systems (and platforms) have their own data model. However, they require input from other platforms and will provide specific information to other platforms.
For example, if we want to know the serial number of a product and the exact production details of this product (used parts, quality status), we would use an MES platform. Examples of MES platforms (none PLM/ERP related vendors) are Parsec and Critical Manufacturing
The IoT platform
these platforms are new and are used to monitor and manage connected products. For example, if you want to trace the individual behavior of a product of a process, you need an IoT platform. The IoT platform provides the product user with performance insights and alerts.
However, it also provides the product manufacturer with the same insights for all their products. This allows the manufacturer to offer predictive maintenance or optimization services based on the experience of a large number of similar products. Examples of IoT platforms (none PLM/ERP-related vendors) are Hitachi and Microsoft.
The Product Innovation Platform (PIP)
All the above platforms would not have a reason to exist if there was not an environment where products were invented, developed, and managed. The Product Innovation Platform PIP – as described by CIMdata -is the place where Intellectual Property (IP) is created, where companies decide on their portfolio and more.
The PIP contains the traditional PLM domain. It is also a logical place to manage product quality and technical portfolio decisions, like what kind of product platforms and modules a company will develop. Like all previous platforms, the PIP cannot exist without other platforms and requires connectivity with the other platforms is applicable.
Look below at the CIMdata definition of a Product Innovation Platform.
You will see that most of the historical PLM vendors aiming to be a PIP (with their different flavors): Aras, Dassault Systèmes, PTC and Siemens.
Of course, several vendors sell more than one platform or even create the impression that everything is connected as a single platform. Usually, this is not the case, as each platform has its specific data model and combining them in a single platform would hurt the overall performance.
Therefore, the interaction between these platforms will be based on standardized interfaces or ad-hoc connections.
Standard interfaces or ad-hoc connections?
Suppose your role and information needs can be satisfied within a single platform. In that case, most likely, the platform will provide you with the right environment to see and manipulate the information.
However, it might be different if your role requires access to information from other platforms. For example, it could be as simple as an engineer analyzing a product change who needs to know the actual stock of materials to decide how and when to implement a change.
This would be a PIP/ERP platform collaboration scenario.
Or even more complex, it might be a product manager wanting to know how individual products behave in the field to decide on enhancements and new features. This could be a PIP, CRM, IoT and MES collaboration scenario if traceability of serial numbers is needed.
The company might decide to build a custom app or dashboard for this role to support such a role. Combining in real-time data from the relevant platforms, using standard interfaces (preferred) or using API’s, web services, REST services, microservices (for specialists) and currently in fashion Low-Code development platforms, which allow users to combine data services from different platforms without being an expert in coding.
Without going too much in technology, the topics in this paragraph require an enterprise architecture and vision. It is opportunistic to think that your existing environment will evolve smoothly into a digital highway for the future by “fixing” demands per user. Your infrastructure is much more likely to end up congested as spaghetti.
In that context, I read last week an interesting post Low code: A promising trend or Pandora’s box. Have a look and decide for yourself
I am less focused on technology, more on methodology. Therefore, I want to come back to the theme of my series: The road to model-based and connected PLM. For sure, in the ideal world, the platforms I mentioned, or other platforms that run across these five platforms, are cloud-based and open to connect to other data sources. So, this is the infrastructure discussion.
In my upcoming blog post, I will explain why platforms require a model-based approach and, therefore, cause a challenge, particularly in the PLM domain.
It took us more than fifty years to get rid of vellum drawings. It took us more than twenty years to introduce 3D CAD for design and engineering. Still primarily relying on drawings. It will take us for sure one generation to switch from document-based engineering to model-based engineering.
Conclusion
In this post, I tried to paint a picture of the ideal future based on connected platforms. Such an environment is needed if we want to be highly efficient in designing, delivering, and maintaining future complex products based on hardware and software. Concepts like Digital Twin and Industry 4.0 require a model-based foundation.
In addition, we will need Digital Twins to reach our future sustainability goals efficiently. So, there is work to do.
Your opinion, Your contribution?
In March 2018, I started a series of blog posts related to model-based approaches. The first post was: Model-Based – an introduction. The reactions to these series of posts can be summarized in two bullets:
- Readers believed that the term model-based was focusing on the 3D CAD model. A logical association as PLM is often associated with 3D CAD-model data management (actually PDM), and in many companies, the 3D CAD model is (yet) not a major information carrier/
- Readers were telling me that a model-based approach is too far from their day-to-day life. I have to agree here. I was active in some advanced projects where the product’s behavior depends on a combination of hardware and software. However, most companies still work in a document-driven, siloed discipline manner merging all deliverables in a BOM.
More than 3 years later, I feel that model-based approaches have become more and more visible for companies. One of the primary reasons is that companies start to collaborate in the cloud and realize the differences between a coordinated and a connected manner.
Initiatives as Industry 4.0 or concepts like the Digital Twin demand a model-based approach. This post is a follow-up to my recent post, The Future of PLM.
History has shown that it is difficult for companies to change engineering concepts. So let’s first look back at how concepts slowly changed.
The age of paper drawings
In the sixties of the previous century, the drawing board was the primary “tool” to specify a mechanical product. The drawing on its own was often a masterpiece drawn on special paper, with perspectives, details, cross-sections.
All these details were needed to transfer the part or assembly information to manufacturing. The drawing set should contain all information as there were no computers.
Making a prototype was, depending on the complexity of the product, the interpretation of the drawings and manufacturability of a product, not always that easy. After a first release, further modifications to the product definition were often marked on the manufacturing drawings using a red pencil. Terms like blueprint and redlining come from the age of paper drawings.
There are still people talking nostalgically about these days as creating and interpreting drawings was an important skill. However, the inefficiencies with this approach were significant.
- First, updating drawings because there was redlining in manufacturing was often not done – too much work.
- Second, drawing reuse was almost impossible; you had to start from scratch.
- Third, and most importantly, you needed to be very skilled in interpreting a drawing set. In particular, when dealing with suppliers that might not have the same skillset and the knowledge of which drawing version was actual.
However, paper was and still is the cheapest neutral format to distribute designs. The last time I saw companies still working with paper drawings was at the end of the previous century.
Curious to learn if they are now extinct?
The age of electronic drawings (CAD)
With the introduction of AutoCAD and personal computers around 1982, more companies started to look into drafting with the computer. There was already the IBM drafting system in 1965, but it was Autodesk that pushed the 2D drafting business with their slogan:
“80 percent of the functionality for 20 percent of the price (Autodesk 1982)”
A little later, I started to work for an Autodesk distributor/reseller. People would come to the showroom to see how a computer drawing could be plotted in the finest quality at the end. But, of course, the original draftsman did not like the computer as the screen was too small.
However, the enormous value came from making changes, the easy way of sharing drawings and the ease of reuse. The picture on the left is me in 1989, demonstrating AutoCAD with a custom-defined tablet and PS/2 computer.
The introduction of electronic drawings was not a disruption, more optimization of the previous ways of working.
The exchange with suppliers and manufacturing could still be based on plotted drawings – the most neutral format. And thanks to the filename, there was better control of versions between all stakeholders.
Aren’t we all happy?
The introduction of mainstream 3D CAD
In 1995, 3D CAD became available for the mid-market, thanks to SolidWorks, Solid Edge and a little later Inventor. Before that working with 3D CAD was only possible for companies that could afford expensive graphic stations, provided by IBM, Silicon Graphics, DEC and SUN. Where are they nowadays? The PC is an example of disruptive innovation, purely based on technology. See Clayton Christensen’s famous book: The Innovator’s Dilemma.
The introduction of 3D CAD on PCs in the mid-market did not lead directly to new ways of working. Designing a product in 3D was much more efficient if you mastered the skills. 3D brought a better understanding of the product dimensions and shape, reducing the number of interpretation errors.
Still, (electronic) drawings were the contractual deliverable when interacting with suppliers and manufacturing. As students were more and more trained with the 3D CAD tools, the traditional art of the draftsman disappeared.
3D CAD introduced some new topics to solve.
- First of all, a 3D CAD Assembly in the system was a collection of separate files, subassemblies, parts, and drawings that relate to each other with a specific version. So how to ensure the final assembly drawings were based on the correct part revisions? Companies were solving this by either using intelligent filenames (with revisions) or by using a PDM system where the database of the PDM system managed all the relations and their status.
- The second point was that the 3D CAD assembly also introduced a new feature, the product structure, or the “Bill of Materials”. This logical structure of the assembly up resembled a lot of the Bill of Material of the product. You could even browse deeper levels, which was not the case in the traditional Bill of Material on a drawing.
Note: The concept of EBOM and MBOM was not known in most companies. People were talking about the BOM as a one-level definition of parts or subassemblies in the assembly. See my Where is the MBOM? Post from July 2008 when this topic was still under discussion.
- The third point that would have a more significant impact later is that parts and assemblies could be reused in other products. This introduced the complexity of configuration management. For example, a 3D CAD part or assembly file could contain several configurations where only one configuration would be valid for the given product. Managing this in the 3D CAD system lead to higher productivity of the designer, however downstream when it came to data management with PDM systems, it became a nightmare.
I experienced these issues a lot when discussing with companies and implementers, mainly the implementation of SmarTeam combined with SolidWorks and Inventor. Where to manage the configuration constraints? In the PDM system or inside the 3D CAD system.
These environments were not friends (image above), and even if they came from the same vendor, it felt like discussing with tribes.
The third point also covered another topic. So far, CAD had been the first step for the detailed design of a product. However, companies now had an existing Bill of Material in the system thanks to the PDM systems. It could be a Bill of Material of a sub-assembly that is used in many other products.
Configuring a product no longer started from CAD; it started from a Product or Bill of Material structure. Sales and Engineers identified the changes needed on the BoM, keeping as much as possible released information untouched. This led to a new best practice.
The item-centric approach
Around 2005, five years after introducing the term Product Lifecycle Management, slowly, a new approach became the standard. Product Lifecycle Management was initially introduced to connect engineering and manufacturing, driven by the automotive and aerospace industry.
It was with PLM that concepts as EBOM and MBOM became visible.
In particular, the EBOM was closely linked to engineering practices, i.e., modularity and reuse. The EBOM and its related information represented the product as it was specified. It is essential to realize that the parts in the EBOM could be generic specified purchase parts to be resolved when producing the product or that the EBOM contained Make-parts specified by drawings.
At that time, the EBOM was often used as the foundation for the ERP system – see image above. The BOM was restructured and organized according to the manufacturing process specifying materials and resources needed in the ERP system. Therefore, although it was an item-like structure, this BOM (the MBOM) always had a close relation to the Bill of Process.
For companies with a single manufacturing site, the notion of EBOM and MBOM was not that big, as the ERP system would be the source of the MBOM. However, the complexity came when companies have several manufacturing sites. That was when a generic MBOM in the PLM system made more sense to centralize all product information in a single system.
The EBOM-MBOM approach has become more and more a standard practice since 2010. As a result, even small and medium-sized enterprises realized a need to manage the EBOM and the MBOM.
There were two disadvantages introduced with this EBOM-MBOM approach.
- First, the EBOM and the MBOM as information structures require a lot of administrative maintenance if information needs to be always correct (and that is the CM target). Some try to simplify this by keeping the EBOM part the same as the MBOM part, meaning the EBOM specification already targets a single supplier or manufacturer.
- The second disadvantage of making every item in the BOM behave like a part creates inefficiencies in modern environments. Products are a mix of hardware(parts) and software(models/behavior). This BOM-centric view does not provide the proper infrastructure for a data-driven approach as part specifications are still done in drawings. We need 3D annotated models related to all kinds of other behavior and physical models to specify a product that contains hard-and software.
A new paradigm is needed to manage this mix efficiently, the enabling foundation for Industry 4.0 and efficient Digital Twins; there is a need for a model-based approach based on connected data elements.
More next week.
Conclusion
| The age of paper drawings | 1960 – now dead |
| The age of electronic drawings | 1982 – potentially dead in 2030 |
| The mainstream 3D CAD | 1995 – to be evolving through MBD and MBSE to the future – not dead shortly |
| Item-centric approach | 2005 – to be evolving to a connected model-based approach – not dead shortly |
Jos Voskuil's Weblog 
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