You are currently browsing the category archive for the ‘Data centric’ category.

In February, the PLM Global Green Alliance published our first interview discussing the relationship between PLM and Sustainability with the main vendors. We talked with Darren West from SAP.

You can find the interview here: PLM and Sustainability: talking with SAP. We spoke with Darren about SAP’s Responsible Design and Production module, allowing companies to understand their environmental and economic impact by calculating fees and taxes and implement measures to reduce regulatory costs. The high reliance on accurate data was one of the topics in our discussion.

In March,  we interviewed Zoé Bezpalko and Jon den Hartog from Autodesk. Besides Autodesk’s impressive sustainability program, we discussed Autodesk’s BIM technology helping the construction industry to become greener and their Generative Design solution to support the designer in making better material usage or reuse decisions.

The discussion ended with discussing Life Cycle Assessment tools to support the engineer in making sustainable decisions.

In my last blog post, the Innovation Dilemma, I explored the challenges of a Life Cycle Assessment. As it appears, it is not about just installing a tool. The concepts of a data-driven PLM infrastructure and digital twins are strong transformation prerequisites combined with the Inner Development Goals (IDG).

The IDGs are a human attitude needed besides the Sustainability Development Goals.

Therefore we were happy to discuss last week with Florence Verzelen, Executive Vice President Industry, Marketing & Sustainability and Xavier Adam, Worldwide Sustainability Senior Manager from Dassault Systemes. We discussed Dassault Systemes’ business sustainability goals and product offerings based on the 3DEXPERIENCE platform.

Have a look at the discussion below:


The slides shown in the recording can be found HERE.

What I learned

Dassault Systemes’ purpose has been to help their customers imagine sustainable innovations capable of harmonizing product, nature, and life for many years. A statement that now is slowly bubbling up in other companies too. Dassault Systemes has set a clear and interesting target for themselves in 2025. In that year two/thirds of their sales should come from solutions that make their customers more sustainable.

Their Eco-design solution is one of the first offerings to reach this objective. Their Life Cycle Assessment solution can govern your (virtual) product design on multiple criteria, not only greenhouse gas emissions.  It will be interesting to follow up on this topic to see how companies make the change internally by relying on data and virtual twins of a product or a manufacturing process.

Want to learn more?

Conclusion

80 % of the environmental impact of products is decided during the design phase. A Lifecycle Assessment Solutions combined with a virtual product model, the virtual design twin, allows you to decide on trade-offs in the virtual space before committing to the physical solution. Creating a data-driven, closed-loop between design, engineering, manufacturing and operations based on accurate data is the envisioned infrastructure for a sustainable future.

Yes, it is not a typo. Clayton Christensen famous book written in 1995 discussed the Innovator’s Dilemma when new technologies cause great firms to fail. This was the challenge two decades ago. Existing prominent companies could become obsolete quickly as they were bypassed by new technologies.

The examples are well known. To mention a few: DEC (Digital Equipment Corporation), Kodak, and Nokia.

Why the innovation dilemma?

This decade the challenge has become different. All companies are forced to become more sustainable in the next ten years. Either pushed by global regulations or because of their customer demands. The challenge is this time different. Besides the priority of reducing greenhouse gas emissions, there is also the need to transform our society from a linear, continuous growth economy into a circular doughnut economy.

The circular economy makes the creation, the usage and the reuse of our products more complex as the challenge is to reduce the need for raw materials and avoid landfills.

The circular economy concept – the regular product lifecycle in the middle

The doughnut economy makes the values of an economy more complex as it is not only about money and growth, human and environmental factors should also be considered.

Doughnut Economics: Trying to stay within the green boundaries

To manage this complexity, I wrote SYSTEMS THINKING – a must-have skill in the 21st century, focusing on the logical part of the brain. In my follow-up post, Systems Thinking: a second thought, I looked at the human challenge. Our brain is not rational and wants to think fast to solve direct threats. Therefore, we have to overcome our old brains to make progress.

An interesting and thought-provoking was shared by Nina Dar in this discussion, sharing the video below. The 17 Sustainability Development Goals (SDGs) describe what needs to be done. However, we also need the Inner Development Goals (IDGs) and the human side to connect. Watch the movie:

Our society needs to change and innovate; however, we cannot. The Innovation Dilemma.

The future is data-driven and digital.

What is clear to me is that companies developing products and services have only one way to move forward: becoming data-driven and digital.

Why data-driven and digital?

Let’s look at something companies might already practice, REACH (Registration, Evaluation, Authorization and Restriction of Chemicals). This European directive, introduced in 2007, had the aim to protect human health and protect the environment by communicating information on chemicals up and down the supply chain. This would ensure that manufacturers, importers, and their customers are aware of information relating to the health and safety of the products supplied.

The regulation is currently still suffering in execution as most of the reporting and evaluation of chemicals is done manually. Suppliers report their chemicals in documents, and companies report the total of chemicals in their summary reports. Then, finally, authorities have to go through these reports.

Where the scale of REACH is limited, the manual effort to have end-to-end reporting is relatively high. In addition, skilled workers are needed to do the job because reporting is done in a document-based manner.

Life Cycle Assessments (LCA)

Where you might think REACH is relatively simple, the real new challenges for companies are the need to perform Life Cycle Assessments for their products. In a Life Cycle Assessment. The Wiki definition of LCA says:

Life cycle assessment or LCA (also known as life cycle analysis) is a methodology for assessing environmental impacts associated with all the stages of the life cycle of a commercial product, process, or service. For instance, in the case of a manufactured product, environmental impacts are assessed from raw material extraction and processing (cradle), through the product’s manufacture, distribution and use, to the recycling or final disposal of the materials composing it (grave)

This will be a shift in the way companies need to define products. Much more thinking and analysis are required in the early design phases. Before committing to a physical solution, engineers and manufacturing engineers need to simulate and calculate the impact of their design decisions in the virtual world.

This is where the digital twin of the design and the digital twin of the manufacturing process becomes relevant. And remember: Digital Twins do not run on documents – you need connected data and various types of models to calculate and estimate the environmental impact.

LCA done in a document-based manner will make your company too slow and expensive.

I described this needed transformation in my series from last year: The road to model-based and connected PLM – nine posts exploring the technology and concept of a model-based, data-driven PLM infrastructure.

Digital Product Passport (DPP)

The European Commission has published an action plan for the circular economy, one of the most important building blocks of the European Green Deal. One of the defined measures is the gradual introduction of a Digital Product Passport (DPP). As the quality of an LCA depends on the quality and trustworthy information about products and materials, the DPP is targeting to ensure circular economy metrics become reliable.

This will be a long journey. If you want to catch a glimpse of the complexity, read this Medium article: The digital product passport and its technical implementation related to the DPP for batteries.

The innovation dilemma

Suppose you agree with my conclusion that companies need to change their current product or service development into a data-driven and model-based manner. In that case, the question will come up: where to start?

Becoming data-driven and model-based, of course, is not the business driver. However, this change is needed to be able to perform Life Cycle Assessments and comply with current and future regulations by remaining competitive.

A document-driven approach is a dead-end.

Now let’s look at the real dilemmas by comparing a startup (clean sheet / no legacy) and an existing enterprise (experience with the past/legacy). Is there a winning approach?

The Startup

Having lived in Israel – the nation where almost everyone is a startup – and working with startups afterward in the past 10 years, I always get inspired by these people’s energy in startup companies. They have a unique value proposition most of the time, and they want to be visible on the market as soon as possible.

This approach is the opposite of systems thinking. It is often a very linear process to deliver this value proposition without exploring the side effects of such an approach.

For example, the new “green” transportation hype. Many cities now have been flooded with “green” scooters and electric bikes to promote transportation as a service. The idea behind this concept is that citizens do not require to own polluting motorbikes or cars anymore, and transportation means will be shared. Therefore, the city will be cleaner and greener.

However, these “green” vehicles are often designed in the traditional linear way. Is there a repair plan or a plan to recycle the batteries? Reuse of materials used.? Most of the time, not. Please, if you have examples contradicting my observations, let me know. I like to hear good news.

When startup companies start to scale, they need experts to help them grow the company. Often these experts are seasoned people, perhaps close to retirement. They will share their experience and what they know best from the past:  traditional linear thinking.

As a result, even though startup companies can start with a clean sheet, their focus on delivering the product or service blocks further thinking. Instead, the seasoned experts will drive the company towards ways of working they know from the past.

Out of curiosity: Do you know or work in a startup that has started with a data-driven and model-based vision from scratch?  Please add the name of this company in the comments, and let’s learn how they did it.

The Existing company

Working in an established company is like being on board a big tanker. Changing its direction takes a clear eye on the target and navigation skills to come there. Unfortunately, most of the time, these changes take years as it is impossible to switch the PLM infrastructure and the people skills within a short time.

From the bimodal approach in 2015 to the hybrid approach for companies, inspired by this 2017 McKinsey article: Toward an integrated technology operating model, I discovered that this is probably the best approach to ensure a change will happen. In this approach – see image – the organization keeps running on its document-driven PLM infrastructure. This type of infrastructure becomes the system of record. Nothing different from what PLM currently is in most companies.

In parallel, you have to start with small groups of people who independently focus on a new product, a new service. Using the model-based approach, they work completely independently from the big enterprise in a data-driven approach. Their environment can be considered the future system of engagement.

The data-driven approach allows all disciplines to work in a connected, real-time manner. Mastering the new ways of working is usually the task of younger employees that are digital natives. These teams can be completed by experienced workers who behave as coaches. However, they will not work in the new environment; these coaches bring business knowledge to the team.

People cannot work in two modes, but organizations can. As you can see from the McKinsey chart, the digital teams will get bigger and more important for the core business over time. In parallel, when their data usage grows, more and more data integration will occur between the two operation modes. Therefore, the old PLM infrastructure can remain a System of Record and serve as a support backbone for the new systems of engagement.

The Innovation Dilemma conclusion

The upcoming ten years will push organizations to innovate their ways of working to become sustainable and competitive. As discussed before, they must learn to work in a data-driven, connected manner. Both startups and existing enterprises have challenges – they need to overcome the “thinking fast and acting slow” mindset. Do you see the change in your company?

 

Note: Before publishing this post, I read this interesting and complementary post from Jan Bosch Boost your digitalization: instrumentation.

It is in the air – grab it.

 

In the past four weeks, I have been writing about the various aspects related to PLM Education. First, starting from my bookshelf, zooming in on the strategic angle with CIMdata (Part 1).

Next, I was looking at the educational angle and motivational angle with Share PLM (Part 2).

And the last time,  I explored with John Stark the more academic view of PLM education. How do you – students and others – learn and explore the full context of PLM (Part 3)?

Now I am talking with Dave Slawson from Quick Release_ , exploring their onboarding and educational program as a consultancy firm.

How do they ensure their consultants bring added value to PLM-related activities, and can we learn something from that four our own practices?

Quick Release

Dave, can you tell us something more about Quick Release, further abbreviated to QR, and your role in the organization?
.

Quick Release is a specialist PDM and PLM consultancy working primarily in the automotive sector in Europe, North America, and Australia. Robust data management and clear reporting of complex subjects are essential.

Our sole focus is connecting the data silos within our client’s organizations, reducing program or build delays through effective change management.

Quick Release promise – PDT 2019

I am QR’s head of Learning and Development, and I’ve been with the company since late 2014.

I’ve always had a passion for developing people and giving them a platform to push themselves to realize their potential. QR wants to build talent from within instead of just hiring experienced people.

However, with our rapid growth, it became necessary to have dedicated full-time resources to faster onboarding and upskilling our employees. This is combined with having an ongoing development strategy and execution.

QRs Learning & Development approach

Let’s focus on Learning & Development internally at QR first. What type of effort and time does it take to onboard a new employee, and what is their learning program?
.

We have a six-month onboarding program for new employees. Most starters join one of our “boot camps”, a three-week intensive program where a cohort of between 6 and 14 new starters receive classroom-style sessions led by our subject matter experts.

During this, new starters learn about technical PDM and PLM and high-performance business skills that will help them deliver excellence for or clients and feel confident in their work.

Quick Release BoB track process – click to enlarge

While the teams spend a lot of time with the program coordinator, we also bring in our various Subject Matter Experts (SMEs) to ensure the highest quality and variety in these sessions. Some of these sessions are delivered by our founders and directors.

As a business, we believe in investing senior leadership time to ensure quality training and give our team members access to the highest levels of the company.

Since the Covid-19 pandemic started, we moved our training program to be primarily distance learning. However, some sessions are in person, with new starters attending workshops in our regional offices. Our sessions focus on engagement and “doing” instead of just watching a presentation. New starters have fed back that they are still just as enjoyable via distance learning.

Following boot camp, team members will start work on their client projects, supported by a Project Manager and a mentor. During this period, their mentor will help them use the on-the-job experience to build up their technical knowledge on top of their bootcamp learning. The mentor is also there to help them cope with what we know is a steep learning curve. Towards the end of the six months program, each new starter will carry out a self-evaluation designed to help them recognize their achievements to date and identify areas of focus for ongoing personal development.

We gather feedback from the trainers and trainees throughout the onboarding programs, ensuring that the former is shared with their mentors to help with coaching.

The latter is used to help us continuously improve our offering. Our trainers are subject matter experts, but we encourage them to evolve their content and approach based on feedback.

 

The learning journey

Some might say you only learn on the job – how do you relate to this statement? Where does QR education take place? Can you make a statement on ROI for Learning & Development?

It is important to always be curious related to your work. We encourage our team members to challenge themselves to learn new things and dig deeper. Indeed, constant curiosity is one of our core values. We encourage people to challenge the status quo, challenge themselves, and adopt a growth mindset through all development and feedback cycles.

The learning curve in PDM and PLM can be steep; therefore, we must give people the tools and feedback that they can use to grow. At QR, this starts with our onboarding program and flows into an employee’s full career with us. In addition, at the end of every quarter, team members receive performance feedback from their managers, which feeds into their development target setting.

We have a wealth of internal resources to support development, from structured training materials to our internally compiled PDM Wiki and our suite of development “playbooks” (curated learning journeys catering to a range of learning styles).

On-the-job learning is critically important. So after the boot camp, we put our team members straight into projects to make sure they apply and build on their baseline knowledge through real-world experience. Still, they are supported with formal training and ongoing access to development resources.

Regarding Return on Investment, while it is impossible to give a specific number, we would say that quality training is invaluable to our clients and us. In seven years, the company has grown from 60 to 300 employees. In addition, it now operates in three other continents, illustrating that our clients trust the quality of how we train our consultants!

We also carried out internal studies regarding the long-term retention of team members relative to onboarding quality. These studies show that team members who experience a more controlled and structured onboarding program are mostly more successful in roles.

Investing in education?

I understood some of your customers also want to understand PLM processes better and ask for education from your side. Would the investment in education be similar? Would they be able to afford such an effort?

Making a long-term and tangible impact for our clients is the core foundation of what QR are trying to achieve. We do not want to come in to resolve a problem, only for it to resurface once we’ve left. Nor do we want to do work that our clients could easily hire someone to do themselves.

Therefore the idea of delivering a version of our training and onboarding program to clients is very attractive to us. We offer clients a shortened version of our bootcamp (focused on technical PDM, PLM and complexity management without the consultancy skills to our clients).

This is combined with an ongoing support program that transitions the responsibilities within the client team away from our consultants towards the client’s own staff.

We’d look to run that program over approximately 6 months so that the client can be confident that their staff has reached the level of technical expertise. There would be an upfront cost to the client to manage this.

However, the program is designed to support quality skills development within their organization.

 

PLM and Digital Transformation?

Education and digital transformation is a question I always ask. Although QR is already established in the digital era, your customers are not. What are the specific parts of digital transformation that you are teaching your employees and customers

The most inefficient thing we see in the PDM space is the reliance on offline, “analog” data and the inability to establish one source of truth across a complex organization. To support business efficiency through digital transformation, we promote a few simple core tenets in everything we do:

  • Establish a data owner who not only holds the single reference point but also is responsible for its quality
  • Right view reporting – clearly communicate exactly what people need to know, recognizing that different stakeholders need to know different things and that no one has time to waste
  • Clear communications – using the right channels of communication to get the job done faster (including more informal channels such as instant messaging or collaborative online working documents)
  • Smart, data-led decision making – reviewing processes using accurate data that is analyzed thoroughly, and justifying recommendations based on a range of evidence
  • Getting your hands dirty! – Digital Transformation is not just a “systems” subject but relies on people and human interaction. So we encourage all of our consultants to actually understand how teams work. Not be afraid to roll up their sleeves and get stuck in instead of just analyzing from the outside!

Want to learn more?

Dave, Could you point us to relevant Learning & Development programs and resources that are valuable for the readers of this blog?
.

If you are interested in learning within the PDM and PLM space, follow Quick Release on LinkedIn as we publish thought leadership articles designed to support industry development.

For those interested in Learning & Development strategy, there is lots of UK and Ireland guidance available from the Chartered Institute of Personnel and Development (CIPD). Similar organizations exist in other countries, such as the Society for Human Resource Management (SHRM) in the USA) which are great resources for building Learning & Development specific skills.

In my research, I often find really thought-provoking articles that shape my approach and thinking regarding Learning & Development, HR and a business approach published by Forbes and Harvard Business Review.

 

What I learned

When I first discovered Quick Release as a company during one of the PLM Roadmap & PDT conferences (see “The weekend after PLM Roadmap & PDT 2019″) I was impressed by their young and energetic approach combined with being pragmatic and focused on making the data “flow”.  Their customers were often traditional automotive companies having the challenge to break the silos. You could say QR was working on the “connected” enterprise as I would name it.

PLM consultancy must change

Besides their pragmatic approach, I discovered through interactions with QR that they are a kind of management consultancy firm you would expect in the future. As everything is going to be faster experience counts. Instead of remaining conceptual and strategic, they do not fear being with their feet in the mud.

This requires a new type of consultant and training, as employees need to be able to connect both to specialists at their customers and also be able to communicate with management. These types of people are hard to get as this is the ideal profile of a future employee.

The broad profile

What I learned from Dave is that QR invests seriously in meaningful education and coaching programs for their employees – to give them a purpose and an environment where they feel valued. I would imagine this applies actually to every company of the future, therefore I am curious if you could share your experiences from the field, either through the comments to this post or contact me personally.

Conclusion

We have seen now four dimensions of PLM education and I wish they gave you insights into what is possible. For each of the companies, I interviewed there might be others with the same skills. What is important is to realize the domain of PLM needs those four dimensions. In my next (short) post I will provide a summary of what I learned and what I believe is the PLM education of the future. Stay connected!

And a bonus you might have seen before – the digital plumber:

After two quiet weeks of spending time with my family in slow motion, it is time to start the year.

First of all, I wish you all a happy, healthy, and positive outcome for 2022, as we need energy and positivism together. Then, of course, a good start is always cleaning up your desk and only leaving the relevant things for work on the desk.

Still, I have some books at arm’s length, either physical or on my e-reader, that I want to share with you – first, the non-obvious ones:

The Innovators Dilemma

A must-read book was written by Clayton Christensen explaining how new technologies can overthrow established big companies within a very short period. The term Disruptive Innovation comes up here. Companies need to remain aware of what is happening outside and ready to adapt to your business. There are many examples even recently where big established brands are gone or diminished in a short period.

In his book, he wrote about DEC (Digital Equipment Company)  market leader in minicomputers, not having seen the threat of the PC. Or later Blockbuster (from video rental to streaming), Kodak (from analog photography to digital imaging) or as a double example NOKIA (from paper to market leader in mobile phones killed by the smartphone).

The book always inspired me to be alert for new technologies, how simple they might look like, as simplicity is the answer at the end. I wrote about in 2012: The Innovator’s Dilemma and PLM, where I believed cloud, search-based applications and Facebook-like environments could disrupt the PLM world. None of this happened as a disruption; these technologies are now, most of the time, integrated by the major vendors whose businesses are not really disrupted. Newcomers still have a hard time to concur marketspace.

In 2015 I wrote again about this book, The Innovator’s dilemma and Generation change. – image above. At that time, understanding disruption will not happen in the PLM domain. Instead, I predict there will be a more evolutionary process, which I would later call: From Coordinated to Connected.

The future ways of working address the new skills needed for the future. You need to become a digital native, as COVID-19 pushed many organizations to do so. But digital native alone does not bring success. We need new ways of working which are more difficult to implement.

Sapiens

The book Sapiens by Yuval Harari made me realize the importance of storytelling in the domain of PLM and business transformation. In short, Yuval Harari explains why the human race became so dominant because we were able to align large groups around an abstract theme. The abstract theme can be related to religion, the power of a race or nation, the value of money, or even a brand’s image.

The myth (read: simplified and abstract story) hides complexity and inconsistencies. It allows everyone to get motivated to work towards one common goal. A Yuval says: “Fiction is far more powerful because reality is too complex”.

Too often, I have seen well-analyzed PLM projects that were “killed” by management because it was considered too complex. I wrote about this in 2019  PLM – measurable or a myth? claiming that the real benefits of PLM are hard to predict, and we should not look isolated only to PLM.

My 2020 follow-up post The PLM ROI Myth, eludes to that topic. However, even if you have a soundproof business case at the management level, still the myth might be decisive to justify the investment.

That’s why PLM vendors are always working on their myths: the most cost-effective solution, the most visionary solution, the solution most used by your peers and many other messages to influence your emotions, not your factual thinking. So just read the myths on their websites.

If you have no time to read the book, look at the above 2015 Ted to grasp the concept and use it with a PLM -twisted mind.

Re-use your CAD

In 2015, I read this book during a summer holiday (meanwhile, there is a second edition). Although it was not a PLM book, it was helping me to understand the transition effort from a classical document-driven enterprise towards a model-based enterprise.

Jennifer Herron‘s book helps companies to understand how to break down the (information) wall between engineering and manufacturing.

At that time, I contacted Jennifer to see if others like her and Action Engineering could explain Model-Based Definition comprehensively, for example, in Europe- with no success.

As the Model-Based Enterprise becomes more and more the apparent future for companies that want to be competitive or benefit from the various Digital Twin concepts. For that reason, I contacted Jennifer again last year in my post: PLM and Model-Based Definition.

As you can read, the world has improved, there is a new version of the book, and there is more and more information to share about the benefits of a model-based approach.

I am still referencing Action Engineering and their OSCAR learning environment for my customers. Unfortunately, many small and medium enterprises do not have the resources and skills to implement a model-based environment.

Instead, these companies stay on their customers’ lowest denominator: the 2D Drawing. For me, a model-based definition is one of the first steps to master if your company wants to provide digital continuity of design and engineering information towards manufacturing and operations. Digital twins do not run on documents; they require model-based environments.

The book is still on my desk, and all the time, I am working on finding the best PLM practices related to a Model-Based enterprise.

It is a learning journey to deal with a data-driven, model-based environment, not only for PLM but also for CM experts, as you might have seen from my recent dialogue with CM experts: The future of Configuration Management.

Products2019

This book was an interesting novelty published by John Stark in 2020. John is known for his academic and educational books related to PLM. However, during the early days of the COVID-pandemic, John decided to write a novel. The novel describes the learning journey of Jane from Somerset, who, as part of her MBA studies, is performing a research project for the Josef Mayer Maschinenfabrik. Her mission is to report to the newly appointed CEO what happens with the company’s products all along the lifecycle.

Although it is not directly a PLM book, the book illustrates the complexity of PLM. It Is about people and culture; many different processes, often disconnected. Everyone has their focus on their particular discipline in the center of importance. If you believe PLM is all about the best technology only, read this book and learn how many other aspects are also relevant.

I wrote about the book in 2020: Products2019 – a must-read if you are new to PLM if you want to read more details. An important point to pick up from this book is that it is not about PLM but about doing business.

PLM is not a magical product. Instead, it is a strategy to support and improve your business.

System Lifecycle Management

Another book, published a little later and motivated by the extra time we all got during the COVID-19 pandemic, was Martin Eigner‘s book System Lifecycle Management.

A 281-page journey from the early days of data management towards what Martin calls System Lifecycle Management (SysLM). He was one of the first to talk about System Lifecycle Management instead of PLM.

I always enjoyed Martin’s presentations at various PLM conferences where we met. In many ways, we share similar ideas. However, during his time as a professor at the University of Kaiserslautern (2003-2017), he explored new concepts with his students.

I briefly mentioned the book in my series The road to model-based and connected PLM (Part 5) when discussing SLM or SysLM. His academic research and analysis make this book very valuable. It takes you in a very structured way through the times that mechatronics becomes important, next the time that systems (hardware and software) become important.

We discussed in 2015 the applicability of the bimodal approach for PLM. However, as many enterprises are locked in their highly customized PDM/PLM environments, their legacy blocks the introduction of modern model-based and connected approaches.

Where John Stark’s book might miss the PLM details, Martin’s book brings you everything in detail and with all its references.

It is an interesting book if you want to catch up with what has happened in the past 20 years.

More Books …..

More books on my desk have helped me understand the past or that helped me shape the future. As this is a blog post, I will not discuss more books this time reaching my 1500 words.

Still books worthwhile to read – click on their images to learn more:

I discussed this book two times last year. An introduction in PLM and Modularity and a discussion with the authors and some readers of the book: The Modular Way – a follow-up discussion

x

x

A book I read this summer contributed to a better understanding of sustainability. I mentioned this book in my presentation for the Swedish CATIA Forum in October last year – slide 29 of The Challenges of model-based and traditional plm. So you could see it as an introduction to System Thinking from an economic point of view.

System Thinking becomes crucial for a sustainable future, as I addressed in my post PLM and Sustainability.

Sustainability is my area of interest at the PLM Green Global Alliance, an international community of professionals working with Product Lifecycle Management (PLM) enabling technologies and collaborating for a more sustainable decarbonized circular economy.

Conclusion

There is a lot to learn. Tell us something about your PLM bookshelf – which books would you recommend. In the upcoming posts, I will further focus on PLM education. So stay tuned and keep on learning.

As promised in my early November post – The road to model-based and connected PLM (part 9 – CM), I come back with more thoughts and ideas related to the future of configuration management. Moving from document-driven ways of working to a data-driven and model-based approach fundamentally changes how you can communicate and work efficiently.

Let’s be clear: configuration management’s target is first of all about risk management. Ensuring your company’s business remains sustainable, efficient, and profitable.

By providing the appropriate change processes and guidance,  configuration management either avoids costly mistakes and iterations during all phases of a product lifecycle or guarantees the quality of the product and information to ensure safety.

Companies that have not implemented CM practices probably have not observed these issues. Or they have not realized that the root cause of these issues is a lack of CM.

Similar to what is said in smaller companies related to PLM, CM is often seen as an overhead, as employees believe they thoroughly understand their products. In addition, CM is seen as a hurdle to innovation because of the standardization of practices. So yes, they think it is normal that there are sometimes problems. That’s life.

I already wrote about this topic in 2010 PLM, CM and ALM – not sexy 😦 – where ALM means Asset Lifecycle Management – my focus at that time.

Hear it from the experts

To shape the discussion related to the future of Configuration Management, I had a vivid discussion with three thought leaders in this field: Lisa Fenwick, Martijn Dullaart and Maxime Gravel. A short introduction of the three of them:

Lisa Fenwick, VP Product Development at CMstat, a leading company in Configuration Management and Data Management software solutions and consulting services for aviation, aerospace & defense, marine, and other high-tech industries. She has over 25 years of experience with CM and Deliverables Management, including both government and commercial environments.

Ms. Fenwick has achieved CMPIC SME, CMPIC CM Assessor, and CMII-C certifications. Her experience includes implementing CM software products, CM-related consulting and training, and participation in the SAE and IEEE standards development groups

Martijn Dullaart is the Lead Architect for Enterprise Configuration Management at ASML (Our Dutch national pride) and chairperson of the Industry 4.0 committee of the Institute  Process Excellence (IPX) Congress. Martijn has his own blog mdux.net, and you might have seen him recently during the PLM Roadmap & PDT Fall conference in November – his thoughts about the CM future can be found on his blog here

Maxime Gravel, Manager Model-Based Engineering at Moog Inc., a worldwide designer, manufacturer, and integrator of advanced motion control products. Max has been the director of the model-based enterprise at the Institute for Process Excellence (IPX) and Head of Configuration and Change Management at Gulfstream Aerospace which certified the first aircraft in a 3D Model-Based Environment.

What we discussed:

We had an almost one-hour discussion related to the following points:

  • The need for Enterprise Configuration Management – why and how
  • The needed change from document-driven to model-based – the impact on methodology and tools
  • The “neural network” of data – connecting CM to all other business domains, a similar view as from the PLM domain,

I kept from our discussion the importance of planning – as seen in the CMstat image on the left.

To plan which data you need to manage and how you will manage the data. How often are you doing this in your company’s projects?

Next, all participants stressed the importance of education and training on this topic – get educated. Configuration Management is not a topic that is taught at schools. Early next year, I will come back on education as the benefits of education are often underestimated. Not everything can be learned by “googling.”

 Conclusion

The journey towards a model-based and data-driven future is not a quick one to be realized by new technologies. However, it is interesting to learn that the future of connected data (the “neural network”) allows organizations to implement both CM and PLM in a similar manner, using graph databases and automation. When executed at the enterprise level, the result will be that CM and PLM become natural practices instead of other siloed system-related disciplines.

Most of the methodology is there; the implementation to make it smooth and embedded in organizations will be the topics to learn. Join us in discussing and learning!

 

This week I attended the PLM Roadmap & PDT Fall 2021 with great expectations based on my enthusiasm last year. Unfortunately, the excitement was less this time, and I will explain in my conclusions why. This time it was unfortunate again a virtual event which makes it hard to be interactive, something I realize I am missing a lot.

Over two hundred attendees connected for the two days, and you can find the agenda here. Typically I would discuss the relevant sessions; now, I want to group some of them related to a theme, as there was complementary information in these sessions.

Disruption

Again like in the spring, the theme was focusing on DISRUPTION. The word disruption can give you an uncomfortable feeling when you are not in power. It is more fun to disrupt than to be disrupted, as I mentioned in my spring presentation. Read The week after PLM Roadmap & PDT Spring 2021

In his keynote speech Peter Bilello (CIMdata) kicked off with: The Critical Dozen: 12 familiar, evolving trends and enablers of digital transformation that you cannot or should not live without.

You can see them on the slide below:

I believe many of them should be familiar to you as these themes have been “in the air” already for quite some time. Vendors first and slowly companies start to investigate them when relevant. You will find many of them back in my recent series: The road to model-based and connected PLM, where I explored the topics that would cross your path on that journey.

Like Peter said: “For most of the topics you cannot pick and choose as they are all connected.”

Another interesting observation was that we are more and more moving away from the concept of related structures (digital thread) but more to connected datasets (digital web). Marc Halpern first introduced this topic last year at the 2020 conference and has become an excellent image to frame what we should imagine in a connected world.

Digital web also has to do with the uprise of the graph database mentioned by Peter Bilello as a potentially disruptive technology during the fireside chat. Relational databases can be seen as rigid, associated with PLM structures. On the other hand, graph databases can be associated with flexible relations between different types of data – the image of the digital web.

Where Peter was mainly telling WHAT was happening, two presentations caught my attention because of the HOW.

First of all, Dr. Rodney Ewing (Cummins) ‘s session: A Balanced Strategy to Reap Continuous Business Value from Digital PLM was a great story of a transformational project. It contained both having a continuous delivery of business value in mind while moving to the connected enterprise.

As Rodney mentioned, the contribution of TCS was crucial here, which I can imagine. It is hard for a company to understand what is happening in the outside (PLM) world when applying it to your company. Their transformation roadmap is an excellent example of having the long-term vision in mind, meanwhile delivering value during the transformation.

Talking about the right partner and synergy, the second presentation I liked in this context of disruption was Ian Quest’s presentation (Quick Release): Open-source Disruption in Support of Audacious Goals. As a sponsor of the conference, they had ten minutes to pitch their area of expertise.

After Ian’s presentation, focused on audacious goals (for non-English natives translated as “brave” goals), there was only one word that stuck to my mind: pragmatic.

Instead of discussions about the complexity, Ian gave examples of where a pragmatic data-centric approach could lead to great benefits, as you can see from one of the illustrated benefits below:

Standards

A characteristic topic of this conference is that we always talk about standards. Torbjörn Holm (Eurostep) gave an excellent overview of where standards have led to significant benefits. For example, the containerization of goods has dramatically improved transportation of goods (we all benefit) while killing proprietary means of transport (trains, type of ships, type of unloading).  See the image below:

Torbjörn rightfully expanded this story to the current situation in the construction industry or the challenges for asset operators. Unfortunately, in these practices, many content suppliers remain focusing on their unique capabilities, reluctantly neglecting the demand for interoperability among the whole value chain.

It is a topic Marc Halpern also mentioned last year as an outcome of their Gartner PLM benefits survey. Gartner’s findings:

Time to Market is not so much improved by using PLM as the inefficient interaction with suppliers is the impediment.

Like transport before containerization, the exchange of information is not standardized and designed for digital exchange. Torbjorn believes that more and more companies will insist on exchange standards –  like CHIFOS – an ISO1596-derived exchange standard in the process industry. It is a user-driven standard, the best standard.

In this context, the presentation from Kenny Swope (Boeing) and Jean Yves Delaunay (Airbus) The Business Value of Standards-based Information Interoperability for Aerospace & Defense illustrated this fact.

While working for competitors, the Aerospace industry understands the criticality of standards to become more efficient and less vendor-dependent.  In the aerospace & defense group, they discuss these themes. The last year’s 2020 Fall sessions showed the results. You can read their publications here

The A&D PLM action group uses the following framework when evaluating standards – as you can see on the image below:

The result – and this is a combined exercise of many participating experts from the field; this is their recommendation:

To conclude:
People often complain about standards, framed by proprietary data format vendors, that they lead to a rigid environment, blocking agility.

In reality, standards allow companies to be more agile as the (proprietary) data flow is less an issue. Remember the containerization example.

Sustainability and System Thinking

This conference has always been known for its attention to the circular economy and green thinking. In the past, these topics might have been considered disconnected from our PLM practices; now, they have become a part of everyone’s mission.

Two presentations stood out on this topic for me. First, Ken Webster, with his keynote speech: In the future, you will own nothing and you will be happy was a significant oversight of how we as consumers currently are disconnected from the circular economy. His plea, as shown below, for making manufacturers responsible for the legal ownership of the materials in the products they deliver would impact consumer behavior.

Product as a Service (PaaS) and new ways to provide a service is becoming essential. For example, buildings as power stations, as they are a place to collect solar or wind energy?

His thoughts are aligned with what is happening in Europe related to the European Green Deal (not in his presentation). There is a push for a PaaS model for all products as this would be an excellent stimulant for the circular economy.  PaaS combined with a Digital Product Passport – more on that next year.

Making upgrades to your products has less impact on the environment than creating new products to sell (and creating waste of the old product).  Ken Webster was an interesting statement about changing the economy – do we want to own products or do we want to benefit from the product and leave the legal ownership to the manufacturer.

A topic I discussed in the PLM Roadmap & PDT Conference Spring 2021 – look here at slide 11

Patrick Hillberg‘s presentation Rising to the challenge of engineering and optimizing . . . what?  was the one closest to my heart. We discussed Sustainability and Systems Thinking with Patrick in our PLM Global Green Alliance, being pretty aligned on this topic.  Patrick started by explaining the difference between Systems Engineering and Systems Thinking. Looking at the product go-to-market of an organization is more than the traditional V-model. Economic pressure and culture will push people to deviate from the ideal technological plan due to other priorities.

Expanding on this observation, Partick stated that there are limits to growth, a topic discussed by many people involved in the sustainable economy. Economic growth is impossible on a limited planet, and we have to take more dimensions into account. Patrick gave some examples of that, including issues related to the infamous Boeing 737 Max example.

For Patrick, the COVID-pandemic is the end of the old 2nd Industrial Revolution and a push for a new Fourth Industrial Revolution, which is not only technical, as the slide below indicates.

With Patrick, I believe we are at a decisive moment to disrupt ourselves, reconsider many things we do and are used to doing. Even for PLM practitioners, this is a new path to go.

Data

There were two presentations related to digitization and the shift from document-based to a data-driven approach.

First, there was Greg Weaver (Gulfstream) with his presentation Indexing Content – Finding Your Needle in the Haystack. Greg explained that by using indexation of existing document-based information combined with a specific dashboard, they could provide fast access to information that otherwise would have been hidden in so many document or even paper archives.

It was a pragmatic solution, making me feel nostalgic seeing the SmarTeam profile cards. It was an excellent example of moving to a digital enterprise, and Gulfstream has always been a front runner on this topic.

Warning: Don’t use this by default at home (your company). The data in a regulated industry like Aerospace is expected to be of high quality due to the configuration management processes in place. If your company does not have a strong CM practice, the retrieved data might be inaccurate.

Martijn Dullaart (ASML)’s presentation The Next disruption, please…..  was the next step into the future. With his statement “No CM = No Trust,” he made an essential point for data-driven environments.

There is a need for Configuration Management, and I touched on this topic in my last post: The road to model-based and connected PLM (part 9 – CM).

Martijn’s presentation can also be found on his blog here, and I encourage you to read it (saving me copy & paste text). It was interesting to see that Martijn improved his CM pyramid, as you can see, more discipline and activity-oriented instead of a system view. With Martijn and others, I will elaborate on this topic soon.

Conclusion

This has been an extremely long post, and thanks for reading until the end. Many interesting topics were presented at the conference. I was less excited this time because many of these topics are triggers for a discussion. Innovation comes from meeting people with different backgrounds. In a live conference, you would meet during the break or during the famous dinner. How can we ensure we follow up on all this interesting information.

Your thoughts? Contact me for a Corona Friday discussion.

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)

This week I attended the SCAF conference in Jonkoping. SCAF is an abbreviation of the Swedish CATIA User Group. First of all, I was happy to be there as it was a “physical” conference, having the opportunity to discuss topics with the attendees outside the presentation time slot.

It is crucial for me as I have no technical message. Instead, I am trying to make sense of the future through dialogues. What is sure is that the future will be based on new digital concepts, completely different from the traditional approach that we currently practice.

My presentation, which you can find here on SlideShare, was again zooming in on the difference between a coordinated approach (current) and a connected approach (the future).

The presentation explains the concepts of datasets, which I discussed in my previous blog post. Now, I focussed on how this concept can be discovered in the Dassault Systemes 3DExperience platform, combined with the must-go path for all companies to more systems thinking and sustainable products.

It was interesting to learn that the concept of connected datasets like the spider’s web in the image reflected the future concept for many of the attendees.

One of the demos during the conference illustrated that it is no longer about managing the product lifecycle through structures (EBOM/MBOM/SBOM).

Still, it is based on a collection of connected datasets – the path in the spider’s web.

It was interesting to talk with the present companies about their roadmap. How to become a digital enterprise is strongly influenced by their legacy culture and ways of working. Where to start to be connected is the main challenge for all.

A final positive remark.  The SCAF had renamed itself to SCAF (3DX), showing that even CATIA practices no longer can be considered as a niche – the future of business is to be connected.

Now back to the thread that I am following on the series The road to model-based. Perhaps I should change the title to “The road to connected datasets, using models”. The statement for this week to discuss is:

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

Reliable data

If you have been following my story related to PLM transition: From a connected to a coordinated infrastructure might have seen the image below:

The challenge of a connected enterprise is that you want to connect different datasets, defined in various platforms, to support any type of context. We called this a digital thread or perhaps even better framed a digital web.

This is new for most organizations because each discipline has been working most of the time in its own silo. They are producing readable information in neutral files – pdf drawings/documents. In cases where a discipline needs to deliver datasets, like in a PDM-ERP integration, we see IT-energy levels rising as integrations are an IT thing, right?

Too much focus on IT

In particular, SAP has always played the IT card (and is still playing it through their Siemens partnership). Historically, SAP claimed that all parts/items should be in their system. Thus, there was no need for a PDM interface, neglecting that the interface moment was now shifted to the designer in CAD. And by using the name Material for what is considered a Part in the engineering world, they illustrated their lack of understanding of the actual engineering world.

There is more to “blame” to SAP when it comes to the PLM domain, or you can state PLM vendors did not yet understand what enterprise data means. Historically ERP systems were the first enterprise systems introduced in a company; they have been leading in a transactional  “digital” world. The world of product development never has been a transactional process.

SAP introduced the Master Data Management for their customers to manage data in heterogeneous environments. As you can imagine, the focus of SAP MDM was more on the transactional side of the product (also PIM) than on the engineering characteristics of a product.

I have no problem that each vendor wants to see their solution as the center of the world. This is expected behavior. However, when it comes to a single system approach, there is a considerable danger of vendor lock-in, a lack of freedom to optimize your business.

In a modern digital enterprise (to be), the business processes and value streams should be driving the requirements for which systems to use. I was tempted to write “not the IT capabilities”; however, that would be a mistake. We need systems or platforms that are open and able to connect to other systems or platforms. The technology should be there, and more and more, we realize the future is based on connectivity between cloud solutions.

In one of my first posts (part 2), I referred to five potential platforms for a connected enterprise.  Each platform will have its own data model based on its legacy design, allowing it to service its core users in an optimized environment.

When it comes to interactions between two or more platforms, for example, between PLM and ERP, between PLM and IoT, but also between IoT and ERP or IoT and CRM, these interactions should first be based on identified business processes and value streams.

The need for Master Data Management

Defining horizontal business processes and value streams independent of the existing IT systems is the biggest challenge in many enterprises. Historically, we have been thinking around a coordinated way of working, meaning people shifting pieces of information between systems – either as files or through interfaces.

In the digital enterprise, the flow should be leading based on the stakeholders involved. Once people agree on the ideal flow, the implementation process can start.

Which systems are involved, and where do we need a connection between the two systems. Is the relationship bidirectional, or is it a push?

The interfaces need to be data-driven in a digital enterprise; we do not want human interference here, slowing down or modifying the flow. This is the moment Master Data Management and Data Governance comes in.

When exchanging data, we need to trust the data in its context, and we should be able to use the data in another context. But, unfortunately, trust is hard to gain.

I can share an example of trust when implementing a PDM system linked to a Microsoft-friendly ERP system. Both systems we able to have Excel as an interface medium – the Excel columns took care of the data mapping between these two systems.

In the first year, engineers produced the Excel with BOM information and manufacturing engineering imported the Excel into their ERP system. After a year, the manufacturing engineers proposed to automatically upload the Excel as they discovered the exchange process did not need their attention anymore – they learned to trust the data.

How often have you seen similar cases in your company where we insist on a readable exchange format?

When you trust the process(es), you can trust the data. In a digital enterprise, you must assume that specific datasets are used or consumed in different systems. Therefore a single data mapping as in the Excel example won’t be sufficient

Master Data Management and standards?

Some traditional standards, like the ISO 15926 or ISO 10303, have been designed to exchange process and engineering data – they are domain-specific. Therefore, they could simplify your master data management approach if your digitalization efforts are in that domain.

To connect other types of data, it is hard to find a global standard that also encompasses different kinds of data or consumers. Think about the GS1 standard, which has more of a focus on the consumer-side of data management.  When PLM meets PIM, this standard and Master Data Management will be relevant.

Therefore I want to point to these two articles in this context:

How enterprise architects need to evolve to survive in a digital world focusing on the transition of a coordinated enterprise towards a connected enterprise from the IT point of view.  And a recent LinkedIn post, Web Ontology Language as a common standard language for Engineering Networks? by Matthias Ahrens exploring the concepts I have been discussing in this post.

To me, it seems that standards are helpful when working in a coordinated environment. However, in a connected environment, we have to rely on master data management and data governance processes, potentially based on a clever IT infrastructure using graph databases to be able to connect anything meaningful and possibly artificial intelligence to provide quality monitoring.

Conclusion

Standards have great value in exchange processes, which happen in a coordinated business environment. To benefit from a connected business environment, we need an open and flexible IT infrastructure supported by algorithms (AI) to guarantee quality. Before installing the IT infrastructure, we should first have defined the value streams it should support.

What are your experiences with this transition?

In my last post in this series, The road to model-based and connected PLM, I mentioned that perhaps it is time to talk about SLM instead of PLM when discussing popular TLA’s for our domain of expertise. There were not so many encouraging statements for SLM so far.

SLM could mean for me, Solution Lifecycle Management, considering that the company’s offering more and more is a mix of products and services. Or SLM could mean System Lifecycle Management, in that case pushing the idea that more and more products are interacting with the outside world and therefore could be considered systems. Products are (almost) dead.

In addition, I mentioned that the typical product lifecycle and related configuration management concepts need to change as in the SLM domain. There is hardware and software with different lifecycles and change processes.

It is a topic I want to explore further. I am curious to learn more from Martijn Dullaart, who will be lecturing at the  PLM Road map and PDT 2021 fall conference in November. I hope my expectations are not too high, knowing it is a topic of interest for Martijn. Feel free to join this discussion

In this post, it is time to follow up on my third statement related to what data-driven implies:

Data-driven means that we need to manage data in a much more granular manner. We have to look different at data ownership. It becomes more about data accountability per role as the data can be used and consumed throughout the product lifecycle

On this topic, I have a list of points to consider; let’s go through them.

The dataset

In this post, I will often use the term dataset (you are also allowed to write the data set I understood).

A dataset means a predefined number of attributes and values that belong logically to each other. Datasets should be defined based on the purpose and, if possible, designated for a single goal. In this way, they can be stored in a database.

Combined with other datasets, a combination can result in relevant business information. Note a dataset is not only transactional data; a dataset could also describe geometry.

Identify the dataset

In the document-based world, a lot of information could be stored in a single file. In a data-driven world, we should define a dataset that contains a specific piece of information, logically belonging together. If we are more precise, a part would have various related datasets that make up the definition of a part. These definitions could be:

  • Core identification attributes like ID, Name, Type and Status
  • The Type could define a set of linked information. For example, a valve would have different characteristics as a resistor. Through classification, we can link data sets to the core definition of a part.
  • The part can have engineering-specific data (CAD and metadata), manufacturing-specific data, supplier-specific data, and service-specific data. Each of these datasets needs to be defined as a unique element in a data-driven environment
  • CAD is a particular case as most current CAD systems don’t treat geometry as a single dataset. In a file-based world, many other datasets are stored in the file (e.g., engineering or manufacturing details). In a data-driven environment, we want to have the CAD definition to be treated like a dataset. Dassault Systèmes with their CATIA V6 and 3DEXPERIENCE platform or PTC with OnShape are examples of this approach.Having CAD as separate datasets makes sharing and collaboration so much easier, as we can see from these solutions. The concept for CAD stored in a database is not new, and this approach has been used in various disciplines. Mechanical CAD was always a challenge.

Thanks to Moore’s Law (approximate every 2 years, processor power doubled – click on the image for the details) and higher network connection speed, it starts to make sense to have mechanical CAD also stored in a database instead of a file

An important point to consider is a kind of standardization of datasets. In theory, there should be a kind of minimum agreed collection of datasets. Industry standards provide these collections in their dictionary. Whenever you optimize your data model for a connected enterprise, make sure you look first into the standards that apply to your industry.

They might not be perfect or complete, but inventing your own new standard is a guarantee for legacy issues in the future. This remark is also valid for the software vendors in this domain. A proprietary data model might give you a competitive advantage.

Still, in the long term, there is always the need to connect with outside stakeholders.

 

Identify the RACI

To ensure a dataset is complete and well maintained, the concept of RACI could be used. RACI is the abbreviation for Responsible Accountable Consulted and Informed and a simplification of the RASCI Model, see also a responsibility assignment matrix.

In a data-driven environment, there is no data ownership anymore like you have for documents. The main reason that data ownership can no longer be used is that datasets can be consumed by anyone in the ecosystem. No longer only your department or the manufacturing or service department.

Data sets in a data-driven environment bring value when connected with other datasets in applications or dashboards.

A dataset describing the specification attributes of a part could be used in a spare part app and a service app. Of course, the dataset will be used in a different context – still, we need to ensure we can trust the data.

Therefore, per identified dataset, there should be governed by a kind of RACI concept. The RACI concept is a way to break the siloes in an organization.

Identify Inside / outside

There is a lot of fear that a connected, data-driven environment will expose Intellectual Property (IP). It came up in recent discussions. If you like storytelling and technology, read my old SmarTeam colleague Alex Bruskin’s post: The Bilbo Baggins Threat to PLM Assets. Alex has written some “poetry” with a deep technical message behind it.

It is true that if your data set is too big, you have the challenge of exposing IP when connecting this dataset with others. Therefore, when building a data model, you should make it possible to have datasets pure for internal usage and datasets for sharing.

When you use the concept of RACI, the difference should be defined by the I(informed) – is it PLM-data or PIM-data for example?

Tracking relations

Suppose we follow up on the concept of datasets. In that case, it becomes clear that relations between the datasets are as crucial as the dataset. In traditional PLM applications, these relations are often predefined as part of the core data model/

For example, the EBOM parts have relationships between themselves and specification data – see image.

The MBOM parts have links with the supplier data or the manufacturing process.

The prepared relations in a PLM system allow people to implement the system relatively quickly to map their approaches to this taxonomy.

However, traditional PLM systems are based on a document-based (or file-based) taxonomy combined with related metadata. In a model-based and connected environment, we have to get rid of the document-based type of data.

Therefore, the datasets will be more granular, and there is a need to manage exponential more relations between datasets.

This is why you see the graph database coming up as a needed infrastructure for modern connected applications. If you haven’t heard of a graph database yet, you are probably far from technology hypes. To understand the principles of a graph database you can read this article from neo4j:  Graph Databases for Beginners: Why graph technology is the future

As you can see from the 2020 Gartner Hype Cycle for Artificial Intelligence this technology is at the top of the hype and conceptually the way to manage a connected enterprise. The discussion in this post also demonstrates that besides technology there is a lot of additional conceptual thinking needed before it can be implemented.

Although software vendors might handle the relations and datasets within their platform, the ultimate challenge will be sharing datasets with other platforms to get a connected ecosystem.

For example, the digital web picture shown above and introduced by Marc Halpern at the 2018 PDT conference shows this concept. Recently CIMdata discussed this topic in a similar manner: The Digital Thread is Really a Web, with the Engineering Bill of Materials at Its Center
(Note I am not sure if CIMdata has published a recording of this webinar – if so I will update the link)

Anyway, these are signs that we started to find the right visuals to imagine new concepts. The traditional digital thread pictures, like the one below, are, for me, impressions of the past as they are too rigid and focusing on some particular value streams.

From a distance, it looks like a connected enterprise should work like our brain. We story information on different abstraction levels. We keep incredibly many relations between information elements. As the brain is a biological organ, connections degrade or get lost. Or the opposite other relationships become so strong that we cannot change them anymore. (“I know I am always right”)

Interestingly, the brain does not use the “single source of truth”-concept – there can be various “truths” inside a brain. This makes us human beings with all the good and the harmful effects of that.

As long as we realize there is no single source of truth.

In business and our technological world, we need sometimes the undisputed truth. Blockchain could be the basis for securing the right connections between datasets to guarantee the result is valid. I am curious if blockchain can scale to complex connected situations, although Moore’s Law might ultimately help us here too(if still valid).

The topic is not new – in 2014 I wrote a post with the title: PLM is doomed unless ….   Where I introduced the topic of owning and sharing in the context of the human brain.  In the post, I refer to the book On Intelligence by Jeff Hawkins how tries to analyze what is human-based intelligence and how could we apply it to our technology concepts. Still a fascinating book worth reading if you have the time and opportunity.

 

Conclusion

A data-driven approach requires a more granular definition of information, leading to the concepts of datasets and managing relations between datasets. This is a fundamental difference compared to the past, where we were operating systems with information. Now we are heading towards connected platforms that provide a filtered set of real-time data to act upon.

I am curious to learn more about how people have solved the connected challenges and in what kind of granularity. Let us know!

 

 

In my last post, I zoomed in on a preferred technical architecture for the future digital enterprise. Drawing the conclusion that it is a mission impossible to aim for a single connected environment. Instead, information will be stored in different platforms, both domain-oriented (PLM, ERP, CRM, MES, IoT) and value chain oriented (OEM, Supplier, Marketplace, Supply Chain hub).

In part 3, I posted seven statements that I will be discussing in this series. In this post, I will zoom in on point 2:

Data-driven does not mean we do not need any documents anymore. Read electronic files for documents. Likely, document sets will still be the interface to non-connected entities, suppliers, and regulatory bodies. These document sets can be considered a configuration baseline.

 

System of Record and System of Engagement

In the image below, a slide from 2016,  I show a simplified view when discussing the difference between the current, coordinated approach and the future, connected approach.  This picture might create the wrong impression that there are two different worlds – either you are document-driven, or you are data-driven.

In the follow-up of this presentation, I explained that companies need both environments in the future. The most efficient way of working for operations will be infrastructure on the right side, the platform-based approach using connected information.

For traceability and disconnected information exchanges, the left side will be there for many years to come. Systems of Record are needed for data exchange with disconnected suppliers, disconnected regulatory bodies and probably crucial for configuration management.

The System of Record will probably remain as a capability in every platform or cross-section of platform information. The Systems of Engagement will be the configured real-time environment for anyone involved in active company processes, not only ERP or MES, all execution.

Introducing SysML and SML

This summer, I received a copy of Martin Eigner’s System Lifecycle Management book, which I am reading at his moment in my spare moments. I always enjoyed Martin’s presentations. In many ways, we share similar ideas. Martin from his profession spent more time on the academic aspects of product and system lifecycle than I. But, on the other hand, I have always been in the field observing and trying to make sense of what I see and learn in a coherent approach. I am halfway through the book now, and for sure, I will come back on the book when I have finished.

A first impression: A great and interesting book for all. Martin and I share the same history of data management. Read all about this in his second chapter: Forty Years of Product Data Management

From PDM via PLM to SysLM, is a chapter that everyone should read when you haven’t lived it yourself. It helps you to understand the past (Learning for the past to understand the future). When I finish this series about the model-based and connected approach for products and systems, Martin’s book will be highly complementary given the content he describes.

There is one point for which I am looking forward to is feedback from the readers of this blog.

Should we, in our everyday language, better differentiate between Product Lifecycle Management (PLM) and System Lifecycle Management(SysLM)?

In some customer situations, I talk on purpose about System Lifecycle Management to create the awareness that the company’s offering is more than an electro/mechanical product. Or ultimately, in a more circular economy, would we use the term Solution Lifecycle Management as not only hardware and software might be part of the value proposition?

Martin uses consistently the abbreviation SysLM, where I would prefer the TLA SLM. The problem we both have is that both abbreviations are not unique or explicit enough. SysLM creates confusion with SysML (for dyslectic people or fast readers). SLM already has so many less valuable meanings: Simulation Lifecycle Management, Service Lifecycle Management or Software Lifecycle Management.

For the moment, I will use the abbreviation SLM, leaving it in the middle if it is System Lifecycle Management or Solution Lifecycle Management.

 

How to implement both approaches?

In the long term, I predict that more than 80 percent of the activities related to SLM will take place in a data-driven, model-based environment due to the changing content of the solutions offered by companies.

A solution will be based on hardware, the solid part of the solution, for which we could apply a BOM-centric approach. We can see the BOM-centric approach in most current PLM implementations. It is the logical result of optimizing the product lifecycle management processes in a coordinated manner.

However, the most dynamic part of the solution will be covered by software and services. Changing software or services related to a solution has completely different dynamics than a hardware product.

Software and services implementations are associated with a data-driven, model-based approach.

The management of solutions, therefore, needs to be done in a connected manner. Using the BOM-centric approach to manage software and services would create a Kafkaesque overhead.

Depending on your company’s value proposition to the market, the challenge will be to find the right balance. For example, when you keep on selling 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.

 

Translate

Email subscription to this blog

Categories

%d bloggers like this: