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As I promised I would be enjoying my holidays in the upcoming month there as still a few points I want to share with you.

Not a real blog post, more an agenda and a set of questions for potential follow-up.

Here are five topics for the upcoming months, potentially also relevant and interesting for you. Have a look.

 

Peer Check

This week the discussion I had with Adam Keating, Colab’s CEO and founder, was published on their podcast channel, Peer Check. As I slowly discovered the content, I mentioned their podcast in my last blog post.  I was impressed by the first episodes I could listen to and listened to all of them last week.

Digesting the content from these episodes, I have the impression that we are following Adam’s or Collab’s lifecycle. From understanding the market, the people, and the industry towards the real collaboration topics, like MBD, their product offering and ultimately the connection with PLM. I am curious about what is next.

For me discovering their podcast and being able to participate was an exciting and learning moment. I am still waiting for the readers of this blog to mention their favorite podcasts.

Let us know in the comments.

PLM Global Green Alliance

With the PLM Global Green Alliance (PGGA), we plan to have monthly ZOOM discussions with our LinkedIn members, moderated by one of the PGGA core team members.

The idea of these sessions is that we pick a topic, the moderator sets the scene and then it is up to the members to discuss.

Participants can ask questions and bring in their points. In our understanding, many companies believe they have to do something about sustainability beyond writing it in their mission, but where and how to start?

So the PGGA discussion will be a place to get inspired and act.

Potential topics for the discussion are: What technologies must I master to become more sustainable? How can I motivate my company to become real sustainable? What is a lifecycle assessment (LCA), and how to introduce it in my company? What is the circular economy, and what is needed to become more circular in the context of PLM?

If you like one of the topics, let us know in the comments or add your favorite discussion topic. More on the agenda in early September

 

PGGA meets ….

In this series with PLM vendors and solution providers, we try to understand their sustainability drivers, their solutions, their roadmap and their perception of what is happening in the field. So far, SAP, Autodesk and Dassault Systèmes have contributed to these series. After the summer, we continue with two interviews:

Early in September, the PGGA will discuss sustainability with Sustaira. Sustaira is a Siemens partner, and they offer an all-in-one Sustainability platform, domain-specific Sustainability app templates, and custom Sustainability web and mobile initiatives. Expect the interview to be published early in September.

In the last week of September, the PGGA will have a meeting with Aras in our series related to sustainability. Aras is one of the main PLM providers and we will discuss sustainability even more with them as you can read further on in this agenda. Expect the interview to be released by the end of September.

No actions here for you, just stay tuned in September with the PGGA.

 

CIMdata PLM Roadmap and PDT

On 18 and 19 October, the CIMdata PLM Road Map and PDT 2022 Conference is scheduled as an in-person event in Gothenburg.

The agenda is almost secured and can be found here.

It will be a conference with guidance from CIMdata and Eurostep completed with major Aerospace, Defense and Automotive companies sharing their experience towards a model-based and digital enterprise.

So no marketing but real content; however, there will also be forward-looking presentations related to new PLM paradigms and the relation to data and sustainability.

So if you are curious, come to his conference as you will be triply rewarded: by the content, the keynotes and discussions with your peers.

Register before September 12 to benefit from a 15 % Early Bird discount, which you can spend for the dinner after day 1. The conference dinner has always been a good moment for networking and discussion.

 

A Sustainable Future – Seize Opportunities When Someone Else Sees Costs

Last part of this agenda.

On  October 25th, I will participate as a PGGA member in a webinar with Aras, discussing sustainability in more depth compared to our earlier mentioned standard PGGA interview.

Here I will be joined by Patrick Willemsen from Aras. Patrick is the technical director of the Aras EMEA community, and together we will explore how companies aiming to deliver profitable products and solutions also can contribute to a more sustainable future for our planet.

Feel free to subscribe to this free webinar and discuss your thoughts with us in the Q&A session – here is the registration link.

 

Conclusion

No conclusion this time – all thinking is in progress and I hope to see your feedback or contribution to one of these events in person or through social media.

In the last weeks, I had several discussions related to sustainability. What can companies do to become sustainable and prove it? But, unfortunately, there is so much greenwashing at this moment.

Look at this post: 10 Companies and Corporations Called Out For Greenwashing.

Therefore I thought about which practical steps a company should take to prepare for a sustainable future, as the change will not happen overnight. It reminds me of the path towards a digital, model-based enterprise (my other passion). In my post Why Model-Based definition is important for all, I mentioned that MBD (Model-Based Definition) could be considered the first stepping-stone toward a Model-Based enterprise.

The analogy for Material Compliance came after an Aras seminar I watched a month ago. The webinar How PLM Paves the Way for Sustainability with  Insensia (an Aras implementer) demonstrates how material compliance is the first step toward sustainable product development.

Let’s understand why

The first steps

Companies that currently deliver solutions mostly only focus on economic gains. The projects or products they sell need to be profitable and competitive, which makes sense if you want a future.

And this would not have changed if the awareness of climate impact has not become apparent.

First, CFKs and hazardous materials lead to new regulations. Next global agreements to fight climate change – the Paris agreement and more to come – have led and will lead to regulations that will change how products will be developed. All companies will have to change their product development and delivery models when it becomes a global mandate.

A required change is likely going to happen. In Europe, the Green Deal is making stable progress. However, what will happen in the US will be a mystery as even their supreme court becomes a political entity against sustainability (money first).

Still, compliance with regulations will be required if a company wants to operate in a global market.

What is Material Compliance?

In 2002, the European Union published a directive to restrict hazardous substances in materials. The directive, known as RoHS (Restriction of Hazardous Substances), was mainly related to electronic components. In the first directive, six hazardous materials were restricted.

The most infamous are Cadmium(Cd), Lead(Pb), and Mercury (Hg). In 2006 all products on the EU market must pass RoHS compliance, and in 2011 was now connected the CE marking of products sold in the European market was.

In 2015 four additional chemical substances were added, most softening PVC but also affecting the immune system. Meanwhile, other countries have introduced similar RoHS regulations; therefore, we can see it as a global restricting. Read more here: The RoHS guide.

Consumers buying RoHS-compliant products now can be assured that none of the threshold values of the substances is reached in the product. The challenge for the manufacturer is to go through each of the components of the MBOM. To understand if it contains one of the ten restricted substances and, if yes, in which quantity.

Therefore, they need to get that information from each relevant supplier a RoHS declaration.

Besides RoHS, additional regulations protect the environment and the consumer. For example, REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) compliance deals with the regulations created to improve the environment and protect human health. In addition, REACH addresses the risks associated with chemicals and promotes alternative methods for the hazard assessment of substances.

The compliance process in four steps

Material compliance is most of all the job of engineers. Therefore around 2005, some of my customers started to add RoHS support to their PLM environment.

 

Step 1

The image below shows the simple implementation – the PDF-from from the supplier was linked to the (M)BOM part.

An employee had to manually add the substances into a table and ensure the threshold values were not reached. But, of course, there was already a selection of preferred manufacturer parts during the engineering phase. Therefore RoHS compliance was almost guaranteed when releasing the EBOM.

But this process could be done more cleverly.

 

Step 2

So the next step was that manufacturers started to extend their PLM data model with the additional attributes for RoHS compliance. Again, this could be done cleverly or extremely generic, adding the attributes to all parts.

So now, when receiving the material declaration, a person just has to add the substance values to the part attributes. Then, through either standard functionality or customization, a compliance report could be generated for the (M)BOM. So this already saves some work.

 

Step 3

The next step was to provide direct access to these attributes to the supplier and push the supplier to do the work.

Now the overhead for the manufacturer has been reduced again. This is because only the supplier needs to do the job for his customer.

 

Step 4

In step 4, we see a real connected environment, where information is stored only once, referenced by manufacturers, and kept actual by the part suppliers.

Who will host the RoHS databank? From some of my customer projects, I recall IHS as a data provider – it seems they are into this business when you look at their website HERE.

 

Where is your company at this moment?

Having seen the four stepping-stones leading towards efficient RoHS compliance, you see the challenge of moving from a document-driven approach to a data-driven approach.

Now let’s look into the future. Concepts like Life Cycle Assessment (LCA) or a Digital Product Passport (DPP) will require a fully connected approach.

Where is your company at this moment – have you reached RoHS compliance step 3 or 4? A first step to learn and work connected and data-driven.

 

Life Cycle Assessment – the ultimate target

A lifecycle assessment, or lifecycle analysis (two times LCA again), is a methodology to assess the environmental impact of a product (or solution) through its whole lifecycle. From materials sourcing, manufacturing, transportation, usage, service, and decommissioning. And by assessing, we mean a clear, verifiable, and shareable manner, not just guessing.

Traditional engineering education is not bringing these skills, although LCA is not new, as this 10-years old YouTube movie from Autodesk illustrates:

What is new is that due to global understanding, we are reaching the limits of what our planet can endure; we must act now. Upcoming international regulations will enforce life cycle analysis reporting for manufacturers or service providers. This will happen gradually.

Meanwhile, we all should work on a circular economy, the major framework for a sustainable planet- click on the image on the left.

In my post, I wrote about these combined topics: SYSTEMS THINKING – a must-have skill in the 21st century.

 

Life Cycle Analysis – Digital Twin – Digitization

The big elephant in the room is that when we talk about introducing LCA in your company, it has a lot to do with the digitization of your company. Assessment data in a document can require too much human effort to maintain the data at the right quality. The costs are not affordable if your competitor is more efficient.

When coming to the Analysis part, here, a model-based, data-driven infrastructure is the most efficient way to run virtual analysis, using digital twin concepts at each stage of the product lifecycle.

Virtual models for design, manufacturing and operations allow your company to make trade-off studies with low cost before committing to the physical world. 80 % of the environmental impact of a product comes from decisions in the virtual world.

Once you have your digital twins for each phase of the product lifecycle, you can benchmark your models with data reported from the physical world. All these interactions can be found in the beautiful Boeing diamond below, which I discussed before – Read A digital twin for everybody.

 

Conclusion

Efficient and sustainable life cycle assessment and analysis will come from connected information sources. The old document-driven paradigm is too costly and too slow to maintain. In particular, when the scope is not only a subset of your product, it is your full product and its full lifecycle with LCA. Another stepping stone towards the near future. Where are you?

 

Stepping-stone 1:            From Model-Based Definition to an efficient Model-Based, Data-driven Enterprise

Stepping-stone 2:            For RoHS compliance to an efficient and sustainable Model-Based, data-driven enterprise.

Once and a while, the discussion pops up if, given the changes in technology and business scope, we still should talk about PLM. John Stark and others have been making a point that PLM should become a profession.

In a way, I like the vagueness of the definition and the fact that the PLM profession is not written in stone. There is an ongoing change, and who wants to be certified for the past or framed to the past?

However, most people, particularly at the C-level, consider PLM as something complex, costly, and related to engineering. Partly this had to do with the early introduction of PLM, which was a little more advanced than PDM.

The focus and capabilities made engineering teams happy by giving them more access to their data. But unfortunately, that did not work, as engineers are not looking for more control.

Old (current) PLM

Therefore, I would like to suggest that when we talk about PLM, we frame it as Product Lifecycle Data Management (the definition). A PLM infrastructure or system should be considered the System of Record, ensuring product data is archived to be used for manufacturing, service, and proving compliance with regulations.

In a modern way, the digital thread results from building such an infrastructure with related artifacts. The digital thread is somehow a slow-moving environment, connecting the various as-xxx structures (As-Designed, As-Planned, As-Manufactured, etc.). Looking at the different PLM vendor images, Aras example above, I consider the digital thread a fancy name for traceability.

I discussed the topic of Digital Thread in 2018:  Document Management or Digital Thread. One of the observations was that few people talk about the quality of the relations when providing traceability between artifacts.

The quality of traceability is relevant for traditional Configuration Management (CM). Traditional CM has been framed, like PLM, to be engineering-centric.

Both PLM and CM need to become enterprise activities – perhaps unified.

Read my blog post and see the discussion with Martijn Dullaart, Lisa Fenwick and Maxim Gravel when discussing the future of Configuration Management.

New digital PLM

In my posts, I talked about modern PLM. I described it as data-driven, often in relation to a model-based approach. And as a result of the data-driven approach, a digital PLM environment could be connected to processes outside the engineering domain. I wrote a series of posts related to the potential of such a new PLM infrastructure (The road to model-based and connected PLM)

Digital PLM, if implemented correctly, could serve people along the full product lifecycle, from marketing/portfolio management until service and, if relevant, decommissioning). The bigger challenge is even connecting eco-systems to the same infrastructure, in particular suppliers & partners but also customers. This is the new platform paradigm.

Some years ago, people stated IoT is the new PLM  (IoT is the new PLM – PTC 2017). Or MBSE is the foundation for a new PLM (Will MBSE be the new PLM instead of IoT? A discussion @ PLM Roadmap conference 2018).

Even Digital Transformation was mentioned at that time. I don’t believe Digital Transformation is pointing to a domain, more to an ongoing process that most companies have t go through. And because it is so commonly used, it becomes too vague for the specifics of our domain. I liked Monica Schnitger‘s LinkedIn post: Digital Transformation? Let’s talk. There is enough to talk about; we have to learn and be more specific.

 

What is the difference?

The challenge is that we need more in-depth thinking about what a “digital transformed” company would look like. What would impact their business, their IT infrastructure, and their organization and people? As I discussed with Oleg Shilovitsky, a data-driven approach does not necessarily mean simplification.

I just finished recording a podcast with Nina Dar while writing this post. She is even more than me, active in the domain of PLM and strategic leadership toward a digital and sustainable future. You can find the pre-announcement of our podcast here (it was great fun to talk), and I will share the result later here too.

What is clear to me is that a new future data-driven environment becomes like a System of Engagement. You can simulate assumptions and verify and qualify trade-offs in real-time in this environment. And not only product behavior, but you can also simulate and analyze behaviors all along the lifecycle, supporting business decisions.

This is where I position the digital twin. Modern PLM infrastructures are in real-time connected to the business. Still, PLM will have its system of record needs; however, the real value will come from the real-time collaboration.

The traditional PLM consultant should transform into a business consultant, understanding technology. Historically this was the opposite, creating friction in companies.

Starting from the business needs

In my interactions with customers, the focus is no longer on traditional PLM; we discuss business scenarios where the company will benefit from a data-driven approach. You will not obtain significant benefits if you just implement your serial processes again in a digital PLM infrastructure.

Efficiency gains are often single digit, where new ways of working can result in double-digit benefits or new opportunities.

Besides traditional pressure on companies to remain competitive, there is now a new additional driver that I have been discussing in my previous post, the Innovation Dilemma. To survive on our planet, we and therefore also companies, need to switch to sustainable products and business models.

This is a push for innovation; however, it requires a coordinated, end-to-end change within companies.

Be the change

When do you decide to change your business model from pushing products to the marker into a business model of Product as a Service? When do you choose to create repairable and upgradeable products? It is a business need. Sustainability does not start with the engineer. It must be part of the (new) DNA of a company.

Interesting to read is this article from Jan Bosch that I read this morning: Resistance to Change. Read the article as it makes so much sense, but we need more than sense – we need people to get involved. My favorite quote from the article:

“The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore, all progress depends on the unreasonable man”.

Conclusion

PLM consultants should retrain themselves in System Thinking and start from the business. PLM technology alone is no longer enough to support companies in their (digital/sustainable) transformation. Therefore, I would like to introduce BLM (Business Lifecycle Management) as the new TLA.

However, BLM has been already framed as Black Lives Matter. I agree with that, extending it to ALM (All Lives Matter).

What do you think should we leave the comfortable term PLM behind us for a new frame?

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?

x
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.”

 

When I started this series in July, I expected to talk mostly about new ways of working, enabled through a data-driven and model-based approach. However, when analyzing what is needed for such a future (part 3), it became apparent that many of these new ways of working are dependent on technology.

From coordinated to connected sounds like a business change;

however, it all depends on technology. And here I have to thank Marc Halpern (Gartner’s Research VP, Engineering and Design Technologies)  again, who came with this brilliant scheme below:

So now it is time to address the last point from my starting post:

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 procedures. Therefore, we need to merge them into a single framework. Unfortunately, this cannot be the BOM framework due to the dynamics in software changes.

Configuration management at this moment

PLM and CM are often considered overlapping. My March 2019 post: PLM and Configuration Management – a happy marriage? shares some thoughts related to this point

Does having PLM or PDM installed mean you have implemented CM? There is this confusion because revision management is considered the same as configuration management. Read my March 2020 post: What the FFF is happening? Based on a vivid discussion launched by  Yoann Maingon, CEO and founder of Ganister, an example of a modern, graph database-based, flexible PLM solution.

To hear it from a CM-side,  I discussed it with Martijn Dullaart in my February 2021 post: PLM and Configuration Management. We also zoomed in on CM2 in this post as a methodology.

Martijn is the Lead Architect for Enterprise Configuration Management at ASML (Our Dutch national pride) and chairperson of the Industry 4.0 committee of the Integrated Process Excellence (IPX) Congress.

As mentioned before in a previous post (part 6), he will be speaking at the PLM Roadmap & PDT Fall conference starting this upcoming week.

In this post, I want to talk about the CM future. For understanding the current situation, you can find a broad explanation here on Wikipedia. Have a look at CM in the context of the product lifecycle, ensuring that the product As-Specified and As-Designed information matches the As-Built and As-Operated product information.

A mismatch or inconsistency between these artifacts can lead to costly errors, particularly in later lifecycle stages. CM originated from the Aerospace and Defense industry for that reason. However, companies in other industries might have implemented CM practices too. Either due to regulations or thanks to the understanding that configuration mistakes can cause significant damage to the company.

Historically configuration management addresses the needs of “slow-moving” products. For example, the design of an airplane could take years before manufacturing started. Tracking changes and ensuring consistency of all referenced datasets was often a manual process.

On purpose, I wrote “referenced datasets,” as the information was not connected in a single environment most of the time. The identifier of a dataset ( an item or a document) was the primary information carrier used for mentally connecting other artifacts to keep consistency.

The Institute of Process Excellence (IPX) has been one of the significant contributors to configuration management methodology. They have been providing (and still offer) CM2 training and certification.

As mentioned before, PLM vendors or implementers suggest that a PLM system could fully support Configuration Management. However, CM is more than change management, release management and revision management.

As the diagram from Martijn Dullaart shows, PLM is one facet of configuration management.

Of course, there are also (a few) separate CM tools focusing on the configuration management process. CMstat’s EPOCH CM tool is an example of such software. In addition, on their website, you can find excellent articles explaining the history and their future thoughts related to CM.

The future will undoubtedly be a connected, model-based, software-driven environment. Naturally, therefore, configuration management processes will have to change. (Impressive buzz word sentence, still I hope you get the message).

From coordinated to connected has a severe impact on CM. Let’s have a look at the issues.

Configuration Management – the future

The transition to a data-driven and model-based infrastructure has raised the following questions:

  • How to deal with the granularity of data – each dataset needs to be validated. For example, a document (a collection of datasets) needs to be validated in the document-based approach. How to do this efficiently?
  • The behavior of a product (or system) will more and more dependent on software. Product CM practices have been designed for the hardware domain; now, we need a mix of hardware and software CM practices.
  • Due to the increased complexity of products (or systems) and the rapid changes due to software versions, how do we guarantee the As-Operated product is still matching the As-Designed / As-Certified definitions.

I don’t have answers to these questions. I only share observations and trends I see in my actual world.

Granularity of data

The concept of datasets has been discussed in my post (part 6). Now it is about how to manage the right sets of connected data.

The image on the left, borrowed from Erik Herzog’s presentation at the PDM Roadmap & PDT Fall conference in 2020, is a good illustration of the challenge.

At that time, Erik suggested that OSLC could be the enabler of a digital CM backbone for an enterprise. Therefore, it was a pleasure to see Erik providing an update at the yearly OSLC Fest conference this week.

You can find the agenda and Erik’s presentation here on day 2.

OSLC as a framework seems to be a good candidate for supporting modern CM scenarios. It allows a company to build full traceability between all relevant artifacts (if digital available). I can see the beauty of the technical infrastructure.

Still, it is about people and processes first. Therefore, I am curious to learn from my readers who believe and experiment with such a federated infrastructure.

More software

Traditional working companies might believe that software should be treated as part of the Bill of Materials. In this theory, you treat software code as a part, with a part number and revision. In this way, you might believe configuration management practices do not have to change. However, there are some fundamental differences in why we should decouple hardware and software.

First, for the same hardware solution, there might be a whole collection of valid software codes. Just like your computer. How many valid software codes, even from the same application, can you run on this hardware? Managing a computer system and its software through a Bill of Materials is unimaginable.

A computer, of course, is designed for running all kinds of software versions. However, modern products in the field, like cars, machines, electrical devices, all will have a similar type of software-driven flexibility.

For that reason, I believe that companies that deliver software-driven products should design a mechanism to check if the combination of hardware and software is valid. For a computer system, a software mismatch might not be costly or painful; for an industrial system, it might be crucial to ensure invalid combinations can exist. Click on the image to learn more.

Solutions like Configit or pure::variants might lead to a solution. In Feb 2021, I discussed in PLM and Configuration Lifecycle Management with Henrik Hulgaard, the CTO from Configit, the unique features of their solution.

I hope to have a similar post shortly with Pure Systems to understand their added value to configuration management.

Software change management is entirely different from hardware change management. The challenge is to have two different change management approaches under one consistent umbrella without creating needless overhead.

Increased complexity – the digital twin?

With the increased complexity of products and many potential variants of a solution, how can you validate a configuration? Perhaps we should investigate the digital twin concept, with a twin for each instance we want to validate.

Having a complete virtual representation of a product, including the possibility to validate the software behavior on the virtual product, would allow you to run (automated) validation tests to certify and later understand a product in the field.

No need for inspection on-site or test and fix upgrades in the physical world. Needed for space systems for sure, but why not for every system in the long term. When we are able to define and maintain a virtual twin of our physical product (on-demand), we can validate.

I learned about this concept at the 2020 Digital Twin conference in the Netherlands. Bart Theelen from Canon Production Printing explained that they could feed their simulation models with actual customer data to simulate and analyze the physical situation. In some cases, it is even impossible to observe the physical behavior. By tuning the virtual environment, you might understand what happens in the physical world.

An eye-opener and an advocate for the model-based approach. Therefore, I am looking forward to the upcoming PLM Roadmap & PDT Fall conference. Hopefully, Martijn Dullaart will share his thoughts on combining CM and working in a model-based environment. See you there?

Conclusion

Finally, we have reached in this series the methodology part, particularly the one related to configuration management and traceability in a very granular, digital environment.  

After the PLM Roadmap & PDT fall conference, I plan to follow up with three thought leaders on this topic: Martijn Dullaart (ASML), Maxime Gravel (Moog) and Lisa Fenwick (CMstat).  What would you ask them?

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, 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 disconnectedhardware, 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.

 

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

  1. 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.
  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.
  3. 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.
  4. 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
  5. 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.
  6. 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?
  7. 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?

 

 

 

 

 

 

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