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Last week I attended the long-awaited joined conference from CIMdata and Eurostep in Stuttgart. As I mentioned in earlier blog posts. I like this conference because it is a relatively small conference with a focused audience related to a chosen theme.

Instead of parallel sessions, all attendees follow the same tracks and after two days there is a common understanding for all. This time there were about 70 people discussing the themes:  Digitalizing Reality—PLM’s role in enabling the digital revolution (CIMdata) and Collaboration in the Engineering and Manufacturing Supply Chain –the Extended Digital Thread and Smart Manufacturing (EuroStep)

As you can see all about Digital. Here are my comments:

The State of the PLM Industry:
The Digital Revolution

Peter Bilello kicked off with providing an overview of the PLM industry. The PLM market showed an overall growth of 7.3 % toward 43.6 Billion dollars. Zooming in into the details cPDM grew with 2.9 %. The significant growth came from the PLM tools (7.7 %). The Digital Manufacturing sector grew at 6.2 %. These numbers show to my opinion that in particular, managing collaborating remains the challenging part for PLM. It is easier to buy tools than invest in cPDM.

Peter mentioned that at the board level you cannot sell PLM as this acronym is too much framed as an engineering tool. Also, people at the board have been trained to interpret transactional data and build strategies on that. They might embrace Digital Transformation. However, the Product innovation related domain is hard to define in numbers. What is the value of collaboration? How do you measure and value innovation coming from R&D? Recently we have seen more simplified approaches how to get more value from PLM. I agree with Peter, we need to avoid the PLM-framing and find better consumable value statements.

Nothing to add to Peter’s closing remarks:

 

An Alternative View of the Systems Engineering “V”

For me, the most interesting presentation of Day 1 was Don Farr’s presentation. Don and his Boeing team worked on depicting the Systems Engineering process for a Model-Based environment. The original “V” looks like a linear process and does not reflect the multi-dimensional iterations at various stages, the concept of a virtual twin and the various business domains that need to be supported.

The result was the diamond symbol above. Don and his team have created a consistent story related to the depicted diamond which goes too far for this blog post. Current the diamond concept is copyrighted by Boeing, but I expect we will see more of this in the future as the classical systems engineering “V” was not design for our model-based view of the virtual and physical products to design AND maintain.

 

Sponsor vignette sessions

The vignette sponsors of the conference, Aras, ESI,-group, Granta Design, HCL, Oracle and TCS all got a ten minutes’ slot to introduce themselves, and the topics they believed were relevant for the audience. These slots served as a teaser to come to their booth during a break. Interesting for me was Granta Design who are bringing a complementary data service related to materials along the product lifecycle, providing a digital continuity for material information. See below.

 

The PLM – CLM Axis vital for Digitalization of Product Process

Mikko Jokela, Head of Engineering Applications CoE, from ABB, completed the morning sessions and left me with a lot of questions. Mikko’s mission is to provide the ABB companies with an information infrastructure that is providing end-to-end digital services for the future, based on apps and platform thinking.

Apparently, the digital continuity will be provided by all kind of BOM-structures as you can see below.In my post, Coordinated or Connected, related to a model-based enterprise I call this approach a coordinated approach, which is a current best practice, not an approach for the future. There we want a model-based enterprise instead of a BOM-centric approach to ensure a digital thread. See also Don Farr’s diamond. When I asked Mikko which data standard(s) ABB will use to implement their enterprise data model it became clear there was no concept yet in place. Perhaps an excellent opportunity to look at PLCS for the product related schema.

A general comment: Many companies are thinking about building their own platform. Not all will build their platform from scratch. For those starting from scratch have a look at existing standards for your industry. And to manage the quality of data, you will need to implement Master Data Management, where for the product part the PLM system can play a significant role. See Master Data Management and PLM.

 

Systems of Systems Approach to Product Design

Professor Martin Eigner keynote presentation was about the concepts how new products and markets need a Systems of Systems approach combined with Model-Based Systems Engineering (MBSE) and Product Line Engineering (PLE) where the PLM system can be the backbone to support the MBSE artifacts in context. All these concepts require new ways of working as stated below:

And this is a challenge. A quick survey in the room (and coherent with my observations from the field) is the fact that most companies (95 %) haven’t even achieved to work integrated for mechatronics products. You can imagine the challenge to incorporate also Software, Simulation, and other business disciplines. Martin’s presentations are always an excellent conceptual framework for those who want to dive deeper a start point for discussion and learning.

Additive Manufacturing (Enabled Supply) at Moog

Moog Inc, a manufacturer of precision motion controls for various industries have made a strategic move towards Additive Manufacturing. Peter Kerl, Moog’s Engineering Systems Manager, gave a good introduction what is meant by Additive Manufacturing and how Moog is introducing Additive Manufacturing in their organization to create more value for their customer base and attract new customers in a less commodity domain. As you can image delivering products through Additive Manufacturing requires new skills (Design / Materials), new processes and a new organizational structure. And of course a new PLM infrastructure.

Jim van Oss, Moog’s PLM Architect and Strategist, explained how they have been involved in a technology solution for digital-enabled parts leveraging blockchain technology.  Have a look at their VeriPart trademark. It was interesting to learn from Peter and Jim that they are actively working in a space that according to the Gartner’s hype curve is in the early transform phase.  Peter and Jim’s presentation were very educational for the audience.

For me, it was also interesting to learn from Jim that at Moog they were really practicing the modes for PLM in their company. Two PLM implementations, one with the legacy data and the wrong data for the future and one with the new data model for the future. Both implementations build on the same PLM vendor’s release. A great illustration showing the past and the future data for PLM are not compatible

Value Creation through Synergies between PLM & Digital Transformation

Daniel Dubreuil, Safran’s CDO for Products and Services gave an entertaining lecture related to Safran’s PLM journey and the introduction of new digital capabilities, moving from an inward PLM system towards a digital infrastructure supporting internal (model-based systems engineering / multiple BOMs) and external collaboration with their customers and suppliers introducing new business capabilities. Daniel gave a very precise walk-through with examples from the real world. The concluding slide: KEY SUCCESS FACTORS was a slide that we have seen so many times at PLM events.

Apparently, the key success factors are known. However, most of the time one or more of these points are not possible to address due to various reasons. Then the question is: How to mitigate this risk as there will be issues ahead?

 

Bringing all the digital trends together. What’s next?

The day ended with a virtual Fire Place session between Peter Bilello and Martin Eigner, the audience did not see a fireplace however my augmented twitter feed did it for me:

Some interesting observations from this dialogue:

Peter: “Having studied physics is a good base for understanding PLM as you have to model things you cannot see” – As I studied physics I can agree.

Martin: “Germany is the center of knowledge for Mechanical, the US for Electronics and now China becoming the center for Electronics and Software” Interesting observation illustrating where the innovation will come from.

Both Peter and Martin spent serious time on the importance of multidisciplinary education. We are teaching people in silos, faculties work in silos. We all believe these silos must be broken down. It is hard to learn and experiment skills for the future. Where to start and lead?

Conclusion:

The PLM roadmap had some exciting presentations combined with CIMdata’s PLM update an excellent opportunity to learn and discuss reality. In particular for new methodologies and technologies beyond the hype. I want to thank CIMdata for the superb organization and allowing me to take part. Next week I will follow-up with a review of the PDT Europe conference part (Day 2)

 

 

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Ontology example: description of the business entities and their relationships

In my recent posts, I have talked a lot about the model-based enterprise and already after my first post: Model-Based – an introduction I got a lot of feedback where most of the audience was automatically associating the words Model-Based to a 3D CAD Model.
Trying to clarify this through my post: Why Model-Based – the 3D CAD Model stirred up the discussion even more leading into: Model- Based: The confusion.

A Digital Twin of the Organization

At that time, I briefly touched on business models and business processes that also need to be reshaped and build for a digital enterprise. Business modeling is necessary if you want to understand and streamline large enterprises, where nobody can overview the overall company. This approach is like systems engineering where we try to understand and simulate complex systems.

With this post, I want to close on the Model-Based series and focus on the aspects of the business model. I was caught by this catchy article: How would you like a digital twin of your organization? which provides a nice introduction to this theme.  Also, I met with Steve Dunnico, Creator and co-founder of Clearvision, a Swedish startup company focusing on modern ways of business modeling.

 

Introduction

 Jos (VirtualDutchman):  Steve can you give us an introduction to your company and the which parts of the model-based enterprise you are addressing with Clearvision?

Steve (Clearvision):  Clearvision started as a concept over two decades ago – modeling complex situations across multiple domains needed a simplistic approach to create a copy of the complete ecosystem. Along the way, technology advancements have opened up big-data to everyone, and now we have Clearvision as a modeling tool/SaaS that creates a digital business ecosystem that enables better visibility to deliver transformation.

As we all know, change is constant, so we must transition from the old silo projects and programs to a business world of continuous monitoring and transformation.
Clearvision enables this by connecting the disparate parts of an organization into a model linking people, competence, technology services, data flow, organization, and processes.
Complex inter-dependencies can be visualized, showing impact and opportunity to deliver corporate transformation goals in measured minimum viable transformation – many small changes, with measurable benefit, delivered frequently.  This is what Clearvision enables!

Jos: What is your definition of business modeling?

Steve: Business modeling historically, has long been the domain of financial experts – taking the “business model” of the company (such as production, sales, support) and looking at cost, profit, margins for opportunity and remodeling to suit. Now, with the availability of increased digital data about many dimensions of a business, it is possible to model more than the financials.

This is the business modeling that we (Clearvision) work with – connecting all the entities that define a business so that a change is connected to process, people, data, technology and other dimensions such as cost, time, quality.  So if we change a part, all of the connected parts are checked for impact and benefit.

Jos: What are the benefits of business modeling?

Steve: Connecting the disparate entities of a business opens up limitless opportunities to analyze “what is affected if I change this?”.  This can be applied to simple static “as-is” gap analyses, to the more advanced studies needed to future forecast and move into predictive planning rather than reactive.

 The benefits of using a digital model of the business ecosystem are applicable to the whole organization.  The “C-suite” team get to see heat-maps for not only technology-project deliveries but can use workforce-culture maps to assess the company’s understanding and adoption of new ways of working and achievement of strategic goals.  While at an operational level, teams can collaborate more effectively knowing which parts of the ecosystem help or hinder their deliveries and vice-versa.

Jos: Is business modeling applicable for any type or size of the company?

The complexity of business has driven us to silo our way of working, to simplify tasks to achieve our own goals, and it is larger organizations which can benefit from modeling their business ecosystems.  On that basis, it is unlikely that a standalone small business would engage in its own digital ecosystem model.  However, as a supplier to a larger organization, it can be beneficial for the larger organizations to model their smaller suppliers to ensure a holistic view of their ecosystem.

The core digital business ecosystem model delivers integrated views of dependencies, clashes, hot-spots to support transformation

Jos: How is business modeling related to digital transformation?

Digital transformation is an often heard topic in large corporations, by implication we should take advantage of the digital data we generate and collect in our businesses and connect it, so we benefit from the whole not work in silos.  Therefore, using a digital model of a business ecosystem will help identify areas of connectivity and collaboration that can deliver best benefit but through Minimum Viable Transformation, not a multi-year program with a big-bang output (which sometimes misses its goals…).

Today’s digital technology brings new capabilities to businesses and is driving competence changes in organizations and their partner companies.  So another use of business modeling is to map competence of internal/external resources to the needed capabilities of digital transformation.  Mapping competence rather than roles brings a better fit for resources to support transformation.  Understanding which competencies we have and what the gaps are pr-requisite to plan and deliver transformation.

Jos: Then perhaps close with your Clearvision mission where you fit (uniquely)?

Having worked on early digital business ecosystem models in the late 90’s, we’ve cut our teeth on slow processing time, difficult to change data relationships and poor access to data, combined with a very silo’d work mentality.  Clearvision is now positioned to help organizations realize that the value of the whole of their business is greater than the sum of their parts (silos) by enabling a holistic view of their business ecosystem that can be used to deliver measured transformation on a continual basis.

 Jos: Thanks Steve for your contribution and with this completing the series of post related to a model-based enterprise with its various facets. I am aware this post the opinion from one company describing the importance of a model-based business in general. There are no commercial relations between the two of us and I recommend you to explore this topic further in case relevant for your situation.

Conclusion

Companies and their products are becoming more and more complex, most if it happening now, a lot more happening in the near future. In order to understand and manage this complexity models are needed to virtually define and analyze the real world without the high costs of making prototypes or changes in the real world. This applies for organizations, for systems, engineering and manufacturing coordination and finally in-field operating systems.  They all can be described by – connected – models. This is the future of a model-based enterprise

Coming up next time: CIMdata PDM Roadmap Europe and PDT Europe. You can still register and meet a large group of people who care about the details of aspects of a digital enterprise

 

The digital thread according to GE

In my earlier posts, I have explored the incompatibility between current PLM practices and future needs for digital PLM.  Digital PLM is one of the terms I am using for future concepts. Actually, in a digital enterprise, system borders become vague, it is more about connected platforms and digital services. Current PLM practices can be considered as Coordinated where the future for PLM is aiming at Connected information. See also Coordinated or Connected.

Moving from current PLM practices towards modern ways of working is a transformation for several reasons.

  • First, because the scope of current PLM implementation is most of the time focusing on engineering. Digital PLM aims to offer product information services along the product lifecycle.
  • Second, because the information in current PLM implementations is mainly stored in documents – drawings still being the leading In advanced PLM implementations BOM-structures, the EBOM and MBOM are information structures, again relying on related specification documents, either CAD- or Office files.

So let’s review the transformation challenges related to moving from current PLM to Digital PLM

Current PLM – document management

The first PLM implementations were most of the time advanced cPDM implementations, targeting sharing CAD models and drawings. Deployments started with the engineering department with the aim to centralize product design information. Integrations with mechanical CAD systems had the major priority including engineering change processes. Multidisciplinary collaboration enabled by introducing the concept of the Engineering Bill of Materials (EBOM).  Every discipline, mechanical, electrical and sometimes (embedded) software teams, linked their information to the EBOM. The product release process was driven by the EBOM. If the EBOM is released, the product is fully specified and can be manufactured.

Although people complain implementing PLM is complex, this type of implementation is relatively simple. The only added mental effort you are demanding from the PLM user is to work in a structured way and have a more controlled (rigid) way of working compared to a directory structure approach. For many people, this controlled way of working is already considered as a limitation of their freedom. However, companies are not profitable because their employees are all artists working in full freedom. They become successful if they can deliver in some efficient way products with consistent quality. In a competitive, global market there is no room anymore for inefficient ways of working as labor costs are adding to the price.

The way people work in this cPDM environment is coordinated, meaning based on business processes the various stakeholders agree to offer complete sets of information (read: documents) to contribute to the full product definition. If all contributions are consistent depends on the time and effort people spent to verify and validate its consistency. Often this is not done thoroughly and errors are only discovered during manufacturing or later in the field. Costly but accepted as it has always been the case.

Next Step PLM – coordinated document management / item-centric

When the awareness exists that data needs to flow through an organization is a consistent manner, the next step of PLM implementations come into the picture. Here I would state we are really talking about PLM as the target is to share product data outside the engineering department.

The first logical extension for PLM is moving information from an EBOM view (engineering) towards a Manufacturing Bill of Materials (MBOM) view. The MBOM is aiming to represent the manufacturing definition of the product and becomes a placeholder to link with the ERP system and suppliers directly. Having an integrated EBOM / MBOM process with your ERP system is already a big step forward as it creates an efficient way of working to connect engineering and manufacturing.

As all the information is now related to the EBOM and MBOM, this approach is often called the item-centric approach. The Item (or Part) is the information carrier linked to its specification documents.

 

Managing the right version of the information in relation to a specific version of the product is called configuration management. And the better you have your configuration management processes in place, the more efficient and with high confidence you can deliver and support your products.  Configuration Management is again a typical example where we are talking about a coordinated approach to managing products and documents.

Implementing this type of PLM requires already more complex as it needs different disciplines to agree on a collective process across various (enterprise) systems. ERP integrations are technically not complicated, it is the agreement on a leading process that makes it difficult as the holistic view is often failing.

Next, next step PLM – the Digital Thread

Continuing reading might give you the impression that the next step in PLM evolution is the digital thread. And this can be the case depending on your definition of the digital thread. Oleg Shilovitsky recently published an article: Digital Thread – A new catchy phrase to replace PLM? related to his observations from  ConX18 illustrate that there are many viewpoints to this concept. And of course, some vendors promote their perfect fit based on their unique definition. In general, I would classify the idea of Digital Thread in two approaches:

The Digital Thread – coordinated

In the Digital Thread – coordinated approach we are not revolutionizing the way of working in an enterprise. In the coordinated approach, the PLM environment is connected with another overlay, combining data from various disciplines into an environment where the dependencies are traceable. This can be the Aras overlay approach (here explained by Oleg Shilovitsky), the PTC Navigate approach or others, using a new extra layer to connect the various discipline data and create traceability in a more or less non-intrusive way. Similar concepts, but less intrusive can be done through Business Intelligence applications, although they are more read-only than a system approach.

The Digital Thread – connected

In the Digital Thread – connected approach the idea is that information is stored in an extreme granular way and shared among disciplines. Instead of the coordinated way, where every discipline can have their own data sources, here the target is to be data-driven (neutral/standard formats). I described this approach in the various aspects of the model-based enterprise. The challenge of a connected enterprise is the standardized data definition to make it available for all stakeholders.

Working in a connected enterprise is extremely difficult, in particular for people educated in the old-fashioned ways of working. If you have learned to work with shared documents, like Google Docs or Office documents in sharing mode, you will understand the mental change you have to go through. Continuous sharing the information instead of waiting until you feel your part is complete.

In the software domain, companies are used to work this way and to integrate data in a continuous stream. We have to learn to apply these practices also to a complete product lifecycle, where the product consists of hardware and software.

Still, the connect way if working is the vision where digital enterprises should aim for as it dramatically reduces the overhead of information conversion, overhead, and ambiguity. How we will implement in the context of PLM / Product Innovation is a learning process, where we should not be blocked by our echo chamber as Jan Bosch states it in his latest post: Don’t Get Stuck In Your Company’s Echo Chamber

Jan Bosch is coming from the software world, promoting the Software-Centric Systems conference SC2 as a conference to open up your mind. I recommend you to take part in upcoming PLM related events: CIMdata’s PLM roadmap Europe combined with PDT Europe on 24/25th October in Stuttgart, or if you are living in the US there is the upcoming PI PLMx CHICAGO 2018 on Nov 5/6th.

Conclusion

Learning and understanding are crucial and takes time. A digital transformation has many aspects to learn – keep in mind the difference between coordinated (relatively easy) and connected (extraordinarily challenging but promising). Unfortunate there is no populist way to become digital.

Note:
If you want to continue learning, please read this post – The True Impact of Industry 4.0 Revealed  -and its internal links to reference information from Martijn Dullaart – so relevant.

 

During my holiday I have read some interesting books. Some for the beauty of imagination and some to enrich my understanding of the human brain.

Why the human brain? It is the foundation and motto of my company: The Know-How to Know Now.
In 2012 I wrote a post: Our brain blocks PLM acceptance followed by a post in 2014  PLM is doomed, unless …… both based on observations and inspired by the following books (must read if you are interested in more than just PLM practices and technology):

In 2014, Digital Transformation was not so clear. We talked about disruptors, but disruption happened outside our PLM comfort zone.

Now six years later disruption or significant change in the way we develop and deliver solutions to the market has become visible in the majority of companies. To stay competitive or meaningful in a global market with changing customer demands, old ways of working no longer bring enough revenue to sustain.  The impact of software as part of the solution has significantly changed the complexity and lifecycle(s) of solutions on the market.

Most of my earlier posts in the past two years are related to these challenges.

What is blocking Model-Based Definition?

This week I had a meeting in the Netherlands with three Dutch peers all interested and involved in Model-Based Definition – either from the coaching point of view or the “victim” point of view.  We compared MBD-challenges with Joe Brouwer’s AID (Associated Information Documents) approach and found a lot of commonalities.

No matter which method you use it is about specifying unambiguously how a product should be manufactured – this is a skill and craftsmanship and not a technology. We agreed that a model-based approach where information (PMI) is stored as intelligent data elements in a Technical Data Package (TPD) will be crucial for multidisciplinary usage of a 3D Model and its associated information.

If we would store the information again as dumb text in a view, it will need human rework leading to potential parallel information out of sync, therefore creating communication and quality issues. Unfortunate as it was a short meeting, the intention is to follow-up this discussion in the Netherlands to a broader audience. I believe this is what everyone interested in learning and understanding the needs and benefits of a model-based approach (unavoidable) should do. Get connected around the table and share/discuss.

We realized that human beings indeed are often the blocking reason why new ways of working cannot be introduced. Twenty-five years ago we had the discussion moving from 2D to 3D for design. Now due to the maturity of the solutions and the education of new engineers this is no longer an issue. Now we are in the next wave using the 3D Model as the base for manufacturing definition, and again a new mindset is needed.

There are a few challenges here:

  • MBD is still in progress – standards like AP242 still needs enhancements
  • There is a lack of visibility on real reference stories to motivate others.
    (Vendor-driven stories often are too good to be true or too narrow in scope)
  • There is no education for (modern) business processes related to product development and manufacturing. Engineers with new skills are dropped in organizations with traditional processes and silo thinking.

Educate, or our brain will block the future!

The above points need to be addressed, and here the human brain comes again into the picture.  Our unconscious, reptile brain is continuously busy to spend a least amount of energy as described in Thinking, Fast and Slow. Currently, I am reading the Idiot Brain: What Your Head Is Really Up To by Dean Burnett, another book confirming that our brain is not a logical engine making wise decisions

And then there is the Dunning-Kruger effect, explaining that the people with the lowest skills often have the most outspoken opinion and not even aware of this flaw. We see this phenomenon in particular now in social media where people push their opinion as if they are facts.

So how can we learn new model-based approaches and here I mean all the model-based aspects I have discussed recently, i.e., Model-Based Systems Engineering, Model-Based Definition/ Model-Based Enterprise and the Digital Twin? We cannot learn it from a book, as we are entering a new era.

First, you might want to understand there is a need for new ways of working related to complex products. If you have time, listen to Xin Guo Zhang’s opening keynote with the title: Co-Evolution of Complex Aeronautical Systems & Complex SE. It takes 30 minutes so force yourself to think slow and comprehend the message related to the needed paradigm shift for systems engineering towards model-based systems engineering

Also, we have to believe that model-based is the future. If not, we will find for every issue on our path a reason not to work toward the ultimate goal.

You can see this in the comments of my earlier post on LinkedIn, where Sami Grönstrand writes:

I warmly welcome the initiative to “clean up” these concepts  (It is time to clean up our model-based problem and above all, await to see live examples of transformations — even partial — coupled with reasonable business value identification. 

There are two kinds of amazing places: those you have first to see before you can believe they exist.
And then those kinds that you have to believe in first before you can see them…

And here I think we need to simplify en enhance the Model-Based myth as according to Yuval Harari in his book Sapiens, the power of the human race came from creating myths to align people to have long-term, forward-looking changes accepted by our reptile brain. We are designed to believe in myths. Therefore, the need for a Model-based myth.In my post PLM as a myth? from 2017, I discussed this topic in more detail.

Conclusion

There are so many proof points that our human brain is not as reliable as we think it is.  Knowing less about these effects makes it even harder to make progress towards a digital future. This post with all its embedded links can keep your brain active for a few hours. Try it, avoid to think fast and avoid assuming you know it all. Your thoughts?

 

Learning & Discussing more?
Still time to register for CIMdata PLM Roadmap and PDT Europe

 

 

 

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At this moment we are in the middle of the year. Usually for me a quiet time and a good time to reflect on what has happened so far and to look forward.

Three themes triggered me to write this half-year:

  • The changing roles of (PLM) consultancy
  • The disruptive effect of digital transformation on legacy PLM
  • The Model-driven approaches

A short summary per theme here with links to the original posts for those who haven’t followed the sequence.

The changing roles of (PLM) consultancy

Triggered by Oleg Shilovitsky’s post Why traditional PLM ranking is dead. PLM ranking 2.0 a discussion started related to the changing roles of PLM choice and the roles of a consultant.  Oleg and I agreed that using the word dead in a post title is a way to catch extra attention. And as many people do not read more than the introduction, this is a way to frame ideas (not invented by us, look at your newspaper and social media posts).  Please take your time and read this post till the end.

Oleg and I concluded that the traditional PLM status reports provided by consultancy firms are no longer is relevant. They focus on the big vendors, in a status-quo and most of them are 80 % the same on their core PLM capabilities. The challenge comes in how to select a PLM approach for your company.

Here Oleg and I differ in opinion. I am more looking at PLM from a business transformation point of view, how to improve your business with new ways of working. The role of a consultant is crucial here as the consultant can help to formalize the company’s vision and areas to focus on for PLM. The value of the PLM consultant is to bring experience from other companies instead of inventing new strategies per company. And yes, a consultant should get paid for this added value.

Oleg believes more in the bottom-up approach where new technology will enable users to work differently and empower themselves to improve their business (without calling it PLM). More or less concluding there is no need for a PLM consultant as the users will decide themselves about the value of the selected technology. In the context of Oleg’s position as CEO/Co-founder of OpenBOM, it is a logical statement, fighting for the same budget.

The discussion ended during the PLMx conference in Hamburg, where Oleg and I met with an audience recorded by MarketKey. You can find the recording Panel Discussion: Digital Transformation and the Future of PLM Consulting here.
Unfortunate, like many discussions, no conclusion. My conclusion remains the same – companies need PLM coaching !

The related post to this topic are:

 

The disruptive effect of digital transformation on legacy PLM

A topic that I have discussed the past two years is that current PLM is not compatible with a modern data-driven PLM. Note: data-driven PLM is still “under-development”. Where in most companies the definition of the products is stored in documents / files, I believe that in order to manage the complexity of products, hardware and software in the future, there is a need to organize data related to models not to files. See also: From Item-centric to model-centric ?

For a company it is extremely difficult to have two approaches in parallel as the first reaction is: “let’s convert the old data to the new environment”.

This statement has been proven impossible in most of the engagements I am involved in and here I introduced the bimodal approach as a way to keep the legacy going (mode 1) and scale-up for the new environment (mode 2).

A bimodal approach is sometimes acceptable when the PLM software comes from two different vendors. Sometimes this is also called the overlay approach – the old system remains in place and a new overlay is created to connect the legacy PLM system and potentially other systems like ALM or MBSE environments. For example some of the success stories for Aras complementing Siemens PLM.

Like the bimodal approach the overlay approach creates the illusion that in the near future the old legacy PLM will disappear. I partly share that illusion when you consider the near future a period of 5 – 10+ years depending on the company’s active products. Faster is not realistic.

And related to bimodal, I now prefer to use the terminology used by McKinsey: our insights/toward an integrated technology operating model in the context of PLM.

The challenge is that PLM vendors are reluctant to support a bimodal approach for their own legacy PLM as then suddenly this vendor becomes responsible for all connectivity between mode 1 and mode 2 data – every vendors wants to sell only the latest.

I will elaborate on this topic during the PDT Europe conference in Stuttgart – Oct 25th . No posts on this topic this year (yet) as I am discussing, learning and collecting examples from the field. What kept me relative busy was the next topic:

The Model-driven approaches

Most of my blogging time I spent on explaining the meaning behind a modern model-driven approach and its three main aspects: Model-Based Systems Engineering, Model-Based Definition and Digital Twins. As some of these aspects are still in the hype phase, it was interesting to see the two different opinions are popping up. On one side people claiming the world is still flat (2D), considering model-based approaches just another hype, caused by the vendors. There is apparently no need for digital continuity. If you look into the reactions from certain people, you might come to the conclusion it is impossible to have a dialogue, throwing opinions is not a discussion..

One of the reasons might be that people reacting strongly have never experienced model-based efforts in their life and just chime in or they might have a business reason not to agree to model-based approached as it does not align with their business? It is like the people benefiting from the climate change theory – will the vote against it when facts are known ? Just my thoughts.

There is also another group, to which I am connected, that is quite active in learning and formalizing model-based approaches. This in order to move forward towards a digital enterprise where information is connected and flowing related to various models (behavior models, simulation models, software models, 3D Models, operational models, etc., etc.) . This group of people is discussing standards and how to use and enhance them. They discuss and analyze with arguments and share lessons learned. One of the best upcoming events in that context is the joined CIMdata PLM Road Map EMEA and the PDT Europe 2018 – look at the agenda following the image link and you should get involved too – if you really care.

 

And if you are looking into your agenda for a wider, less geeky type of conference, consider the PI PLMx CHICAGO 2018 conference on Nov 5 and 6. The agenda provides a wider range of sessions, however I am sure you can find the people interested in discussing model-based learnings there too, in particular in this context Stream 2: Supporting the Digital Value Chain

My related posts to model-based this year were:

Conclusion

I spent a lot of time demystifying some of PLM-related themes. The challenge remains, like in the non-PLM world, that it is hard to get educated by blog posts as you might get over-informed by (vendor-related) posts all surfing somewhere on the hype curve. Do not look at the catchy title – investigate and take time to understand HOW things will this work for you or your company. There are enough people explaining WHAT they do, but HOW it fit in a current organization needs to be solved first. Therefore the above three themes.

This is my concluding post related to the various aspects of the model-driven enterprise. We went through Model-Based Systems Engineering (MBSE) where the focus was on using models (functional / logical / physical / simulations) to define complex product (systems). Next we discussed Model Based Definition / Model-Based Enterprise (MBD/MBE), where the focus was on data continuity between engineering and manufacturing by using the 3D Model as a master for design, manufacturing and eventually service information.

And last time we looked at the Digital Twin from its operational side, where the Digital Twin was applied for collecting and tuning physical assets in operation, which is not a typical PLM domain to my opinion.

Now we will focus on two areas where the Digital Twin touches aspects of PLM – the most challenging one and the most over-hyped areas I believe. These two areas are:

  • The Digital Twin used to virtually define and optimize a new product/system or even a system of systems. For example, defining a new production line.
  • The Digital Twin used to be the virtual replica of an asset in operation. For example, a turbine or engine.

Digital Twin to define a new Product/System

There might be some conceptual overlap if you compare the MBSE approach and the Digital Twin concept to define a new product or system to deliver. For me the differentiation would be that MBSE is used to master and define a complex system from the R&D point of view – unknown solution concepts – use hardware or software?  Unknown constraints to be refined and optimized in an iterative manner.

In the Digital Twin concept, it is more about a defining a system that should work in the field. How to combine various systems into a working solution and each of the systems has already a pre-defined set of behavioral / operational parameters, which could be 3D related but also performance related.

You would define and analyze the new solution virtual to discover the ideal solution for performance, costs, feasibility and maintenance. Working in the context of a virtual model might take more time than traditional ways of working, however once the models are in place analyzing the solution and optimizing it takes hours instead of weeks, assuming the virtual model is based on a digital thread, not a sequential process of creating and passing documents/files. Virtual solutions allow a company to optimize the solution upfront instead of costly fixing during delivery, commissioning and maintenance.

Why aren’t we doing this already? It takes more skilled engineers instead of cheaper fixers downstream. The fact that we are used to fixing it later is also an inhibitor for change. Management needs to trust and understand the economic value instead of trying to reduce the number of engineers as they are expensive and hard to plan.

In the construction industry, companies are discovering the power of BIM (Building Information Model) , introduced to enhance the efficiency and productivity of all stakeholders involved. Massive benefits can be achieved if the construction of the building and its future behavior and maintenance can be optimized virtually compared to fixing it in an expensive way in reality when issues pop up.

The same concept applies to process plants or manufacturing plants where you could virtually run the (manufacturing) process. If the design is done with all the behavior defined (hardware-in-the-loop simulation and software-in-the-loop) a solution has been virtually tested and rapidly delivered with no late discoveries and costly fixes.

Of course it requires new ways of working. Working with digital connected models is not what engineering learn during their education time – we have just started this journey. Therefore organizations should explore on a smaller scale how to create a full Digital Twin based on connected data – this is the ultimate base for the next purpose.

Digital Twin to match a product/system in the field

When you are after the topic of a Digital Twin through the materials provided by the various software vendors, you see all kinds of previews what is possible. Augmented Reality, Virtual Reality and more. All these presentations show that clicking somewhere in a 3D Model Space relevant information pops-up. Where does this relevant information come from?

Most of the time information is re-entered in a new environment, sometimes derived from CAD but all the metadata comes from people collecting and validating data. Not the type of work we promote for a modern digital enterprise. These inefficiencies are good for learning and demos but in a final stage a company cannot afford silos where data is collected and entered again disconnected from the source.

The main problem: Legacy PLM information is stored in documents (drawings / excels) and not intended to be shared downstream with full quality.
Read also: Why PLM is the forgotten domain in digital transformation.

If a company has already implemented an end-to-end Digital Twin to deliver the solution as described in the previous section, we can understand the data has been entered somewhere during the design and delivery process and thanks to a digital continuity it is there.

How many companies have done this already? For sure not the companies that are already a long time in business as their current silos and legacy processes do not cater for digital continuity. By appointing a Chief Digital Officer, the journey might start, the biggest risk the Chief Digital Officer will be running another silo in the organization.

So where does PLM support the concept of the Digital Twin operating in the field?

For me, the IoT part of the Digital Twin is not the core of a PLM. Defining the right sensors, controls and software are the first areas where IoT is used to define the measurable/controllable behavior of a Digital Twin. This topic has been discussed in the previous section.

The second part where PLM gets involved is twofold:

  • Processing data from an individual twin
  • Processing data from a collection of similar twins

Processing data from an individual twin

Data collected from an individual twin or collection of twins can be analyzed to extract or discover failure opportunities. An R&D organization is interested in learning what is happening in the field with their products. These analyses lead to better and more competitive solutions.

Predictive maintenance is not necessarily a part of that.  When you know that certain parts will fail between 10.000 and 20.000 operating hours, you want to optimize the moment of providing service to reduce downtime of the process and you do not want to replace parts way too early.


The R&D part related to predictive maintenance could be that R&D develops sensors inside this serviceable part that signal the need for maintenance in a much smaller time from – maintenance needed within 100 hours instead of a bandwidth of 10.000 hours. Or R&D could develop new parts that need less service and guarantee a longer up-time.

For an R&D department the information from an individual Digital Twin might be only relevant if the Physical Twin is complex to repair and downtime for each individual too high. Imagine a jet engine, a turbine in a power plant or similar. Here a Digital Twin will allow service and R&D to prepare maintenance and simulate and optimize the actions for the physical world before.

The five potential platforms of a digital enterprise

The second part where R&D will be interested in, is in the behavior of similar products/systems in the field combined with their environmental conditions. In this way, R&D can discover improvement points for the whole range and give incremental innovation. The challenge for this R&D organization is to find a logical placeholder in their PLM environment to collect commonalities related to the individual modules or components. This is not an ERP or MES domain.

Concepts of a logical product structure are already known in the oil & gas, process or nuclear industry and in 2017 I wrote about PLM for Owners/Operators mentioning Bjorn Fidjeland has always been active in this domain, you can find his concepts at plmPartner here  or as an eLearning course at SharePLM.

To conclude:

  • This post is way too long (sorry)
  • PLM is not dead – it evolves into one of the crucial platforms for the future – The Product Innovation Platform
  • Current BOM-centric approach within PLM is blocking progress to a full digital thread

More to come after the holidays (a European habit) with additional topics related to the digital enterprise

 

(Image courtesy of Loginworks.com)

This is almost my last planned post related to the concepts of model-based. After having discussed Model-Based Systems Engineering (needed to develop complex products/systems including hardware and software) and Model-Based Definition (creating an efficient connection between Engineering and Manufacturing), my last post will be related to the most over-hyped topic: The Digital Twin

There are several reasons why the Digital Twin is over-hyped. One of the reasons is that the Digital Twin is not necessarily considered as a PLM-related topic. Other vendors like SAP (the network of digital twins), Oracle (Digital Twins for IoT applications)  and GE with their Predix-platform also contributed to the hype related to the digital twin. The other reason is that the concept of Digital Twin is a great idea for marketers to shine above the clouds. Are recent comment from Monica Schnitger says it all in her post 5 quick takeaways from Siemens Automation summit. Monica’s take away related to Digital Twin:

The whole digital twin concept is just starting to gain traction with automation users. In many cases, they don’t have a digital representation of the equipment on their lines; they may have some data from the equipment OEM or their automation contractors but it’s inconsistent and probably incomplete. The consensus seemed to be that this is a great idea but out of many attendees’ immediate reach. [But it is important to start down this path: model something critical, gather all the data you can, prove benefit then move on to a bigger project.]

Monica is aiming to the same point I have been mentioning several times. There is no digital representation and the existing data is inconsistent. Don’t wait: The importance of accurate data – act now !

What is a digital twin?

I think there are various definitions of the digital twin and I do not want to go in a definition debate like we had before with the acronyms MBD/MBE (Model Based Definition/Enterprise – the confusion) or even the acronym PLM (classical PLM or digital PLM ?). Let’s agree on the following high-level statements:

  • A digital twin is a virtual representation of a physical product
  • The virtual part of the digital twin is defined by what you want to analyze, simulate, predict related to the physical product
  • One physical product can have multiple digital twins, only in the ideal world there is potentially a unique digital twin for every physical product in the world
  • When a product interacts with the environment, based on inputs and outputs, we normally call them systems. When I use Product, it will be most of the time a System, in particular in the context of a digital twin

Given the above statements, I will give some examples of digital twin concepts:

As a cyclist I am active on platforms like Garmin and Strava, using a tracking device, heart monitor and a power meter. During every ride my device plus the sensors measure my performance and all the data is uploaded to the platform, providing me with a report where I drove, how fast, my heartbeat, cadence and power during the ride. On Strava I can see the Flybys (other digital twins that crossed my path and their performances) and I can see per segment how I performed considered to others and I can filter by age, by level etc.)

This is the easiest part of a digital twin. Every individual can monitor and analyze their personal behavior and discover trends. Additionally, the platform owner has all the intelligence about all cyclists around the world, how they perform and what would be the best performance per location. And based on their Premium offering (where you pay) they can give you advanced advise on how you can improve. This is the Strava business model bringing value to the individual meanwhile learning from the behavior of thousands. Note in this scenario there is no 3D involved.

Another known digital twin story is related to plants in operation. In the past 10 years I have been advocating for Plant Lifecycle Management (PLM for Owner/Operators), describing the value of a virtual plant model using PLM capabilities combined with Maintenance, Repair and Overhaul (MRO) in order to reduce downtime. In a nuclear environment the usage of 3D verification, simulation and even control software in a virtual environment, can bring great benefit due to the fact that the physical twin is not always accessible and downtime can be up to several million per week.

The above examples provide two types of digital twins. I will discuss some characteristics in the next paragraphs.

Digital Twin – performance focus

Companies like GE and SAP focus a lot on the digital twin in relation to the asset performance. Measuring the performance of assets, compare their performance with other similar assets and based on performance characteristics the collector of the data can sell predictive maintenance analysis, performance optimization guidance and potentially other value offerings to their customers.

Small improvements in the range of a few percents can have a big impact on the overall net results. The digital twin is crucial in this business model to build-up knowledge, analyze and collect it and sell the knowledge again. This type of scenario is the easiest one. You need products with sensors, you need an infrastructure to collect the data and extract and process information in a manner that it can be linked to a behavior model with parameters that influence the model.

Image SAP blogs

This is the model-based part of the digital twin. For a single product there can be different models related to the parameters driving your business. E.g. performance parameters for output, parameters for optimal up-time (preventive maintenance – usage optimization) or parameters related to environmental impact, etc..) Building and selling the results of such a model is an add-on business, creating more value for your customer combined with creating more loyalty. Using the digital twin in the context of performance focus does not require a company to change the way they are working totally.  Yes, you need new skills, data collection and analysis, and more sensor technology but a lot of the product development activities can remain the same (for the moment).

As a conclusion for this type of digital twin I would state, yes there is some PLM involved, but the main focus is on business execution.

Due to the fact that I already reach more than 1000 words, I will focus in my next post on the most relevant digital twin for PLM. Here all disciplines come together. The 3D Mechanical model, the behavior models, the embedded and control software, (manufacturing) simulation and more. All to create an almost perfect virtual copy of a real product or system in the physical world. And there we will see that this is not as easy, as concepts depend on accurate data and reliable models, which is not the case currently in most companies in their engineering environment.

 

Conclusion

Digital Twin is a marketing hype however when you focus on only performance monitoring and tuning it becomes a reality as it does not require a company to align in a digital manner across the whole lifecycle. However this is just the beginning of a real digital twin.

Where are you in your company with the digital twin journey?

I was planning to complete the model-based series with a post related to the digital twin. However, I did not find the time to structure my thoughts to write it up in a structured story. Therefore, this time some topics I am working on that I would like to share.

Executive days at CADCAM Group

Last week I supported the executive days organized by the CADCAM Group in Ljubljana and Zagreb. The CADCAM is a large PLM Solution and Services Provider (60+ employees) in the region of South-East Europe with offices in Croatia, Slovenia, Serbia and Bosnia and Herzegovina. They are operating in a challenging region, four relative young countries with historically more an inside focus than a global focus. Many of CADCAM Group customers are in the automotive supply chain and to stay significant for the future they need to understand and develop a strategy that will help them to move forward.

My presentation was related to the learning path each company has to go through to understand the power of digital combined with the observation that current and future ways of working are not compatible therefore requiring a scaled and bimodal approach (see also PDT Europe further down this post).

This presentation matched nicely with Oscar Torres’s presentation related to strategy. You need to decide on the new things you are going to do, what to keep and what to stop. Sounds easy and of course the challenge is to define the what to start, stop and keep. There you need good insights into your current and future business.

Pierre Aumont completed the inspiring session by explaining how the automotive industry is being disrupted and it is not only Tesla. So many other companies are challenging the current status quo for the big automotive OEMs. Croatia has their innovator for electrical vehicles too, i.e. Rimac. Have a look here.

The presentations were followed by a (long) panel discussion. The common theme in both discussions is that companies need to educate and organize themselves to become educated for the future. New technologies, new ways of working need time and resources which small and medium enterprises often do not have. Therefore, universities, governments and interest groups are crucial.

A real challenge for countries that do not have an industrial innovation culture (yet).

CADCAM Group as a catalyst for these countries understands this need by organizing these executive days. Now the challenge is after these inspiring days to find the people and energy to follow-up.

Note: CADCAM Group graciously covered my expenses associated with my participation in these events but did not in any way influence the content of this paragraph.

 

The MBD/MBE discussion

In my earlier post, Model-Based: Connecting Engineering and Manufacturing,  I went deeper into the MBD/MBE topic and its potential benefits, closing with the request to readers to add their experiences and/or comments to MBD/MBE. Luckily there was one comment from Paul van der Ree, who had challenging experiences with MBD in the Netherlands. Together with Paul and a MBD-advocate (to be named) I will try to have discussion analyzing pro’s and con’s from all viewpoints and hopefully come to a common conclusion.

This to avoid that proponents and opponents of MBD just repeat their viewpoints without trying to converge. Joe Brouwer is famous for his opposition to MBD. Is he right or is he wrong I cannot say as there has never been a discussion. Click on the above image to see Joe’s latest post yourself. I plan to come back with a blog post related to the pro’s and con’s

 

The Death of PLM Consultancy

Early this year Oleg Shilovitsky and I had a blog debate related to the “Death of PLM Consultancy”. The discussion started here: The Death of PLM Consultancy ? and a follow-up post was PLM Consultants are still alive and have an exit strategy. It could have been an ongoing blog discussion for month where the value would be to get response from readers from our blogs.

Therefore I was very happy that MarketKey, the organizers behind the PLMx conferences in Europe and the US, agreed on a recorded discussion session during PLMx 2018 in Hamburg.  Paul Empringham was the moderator of this discussion with approx. 10 – 12 participants in the room to join the discussion. You can view the discussion here through this link: PLMx Hamburg debate

I want to thank MarketKey for their support and look forward to participating in their upcoming PLMx European event and if you cannot wait till next year, there is the upcoming PLMx conference in North America on November 5th and 6th – click on the image on the left to see the details.

 

 

PDT Europe call for papers

As you might have noticed I am a big supporter of the joint CIMdata/PDT Europe conference. This year the conference will be in Stuttgart on October 24th (PLM Roadmap) and October 25th (PDT).

I believe that this conference has a more “geeky” audience and goes into topics of PLM that require a good base understanding of what’s happening in the field. Not a conference for a newcomer in the world of PLM, more a conference for an experienced PLM person (inside a company or from the outside) that has experience challenging topics, like changing business processes, deciding on new standards, how to move to a modern digital business platform.

It was at these events where concepts as Model-Based were discussed in-depth, the need for Master Data Management, Industry standards for data exchange and two years ago the bimodal approach, also valid for PLM.

I hope to elaborate on experiences related to this bimodal or phased approach during the conference. If you or your company wants to contribute to this conference, please let the program committee know. There is already a good set of content planned. However, one or two inspiring presentations from the field are always welcome.
Click on this link to apply for your contribution

Conclusion

There is a lot on-going related to PLM as you can see. As I mentioned in the first topic it is about education and engagement. Be engaged and I am looking forward to your response and contribution in one or more of the topics discussed.

A month ago I announced to write a series of posts related to the various facets of Model-Based. As I do not want to write a book for a limited audience, I still believe blog posts are an excellent way to share knowledge and experience to a wider audience. Remember PLM is about sharing!

There are three downsides to this approach:

  • you have to chunk the information into pieces; my aim is not to exceed 1000 words per post
  • Isolated posts can be taken out of context (in a positive or negative way)
  • you do not become rich and famous for selling your book

Model-Based ways of working are a hot topic and crucial for a modern digital enterprise.  The modern digital enterprise does not exist yet to my knowledge, but the vision is there. Strategic consultancy firms are all active exploring and explaining the potential benefits – I have mentioned McKinsey / Accenture / Capgemini before.

In the domain of PLM, there is a bigger challenge as here we are suffering from the fact that the word “Model” immediately gets associated with a 3D Model. In addition to the 3D CAD Model, there is still a lot of useful legacy data that does not match with the concepts of a digital enterprise. I wrote and spoke about this topic a year ago. Among others at PI 2017 Berlin and you can  check this presentation on SlideShare: How digital transformation affects PLM

Back to the various aspects of Model-Based

My first post: Model-Based – an introduction described my intentions what I wanted to explain.  I got some interesting feedback and insights from my readers . Some of the people who responded understood that the crucial characteristic of the model-based enterprise is to use models to master a complex environment. Business Models, Mathematical Models, System Models are all part of a model-based enterprise, and none of them have a necessary relation to the 3D CAD model.

Why Model-Based?

Because this is an approach to master complex environments ! If you are studying the concepts for a digital enterprise model, it is complex. Artificial intelligence, predictive actions all need a model to deliver. The interaction and response related to my first blog post did not show any problems – only a positive mindset to further explore. For example, if you read this blog post from Contact, you will see the message came across very well: Model-Based in  Model-Based Systems Engineering – what’s up ?

Where the confusion started

My second post: Why Model-Based? The 3D CAD Model  was related to model-based, focusing on the various aspects related to the 3D CAD model, without going into all the details. In particular, in the PLM world, there is a lot of discussion around Model-Based Design or Model-Based Definition, where new concepts are discussed to connect engineering and manufacturing in an efficient and modern data-driven way. Lifecycle Insights, Action Engineering, Engineering.com, PTC,   Tech-Clarity and many more companies are publishing information related to the model-based engineering phase.

Here is was surprised by Oleg’s blog with his post Model-Based Confusion in 3D CAD and PLM.

If you read his post, you get the impression that the model-based approach is just a marketing issue instead of a significant change towards a digital enterprise. I quote:

Here is the thing… I don’t see much difference between saying PLM-CAD integration sharing data and information for downstream processes and “model-driven” data sharing. It might be a terminology thing, but data is managed by CAD-PLM tools today and accessed by people and other services. This is how things are working today. If model-driven is an approach to replace 2D drawings, I can see it. However, 2D replacement is something that I’ve heard 20 years ago. However, 2D drawings are still massively used by manufacturing companies despite some promises made by CAD vendors long time ago.

I was surprised by the simplicity of this quote. As if CAD vendors are responsible for new ways of working. In particular, automotive and aerospace companies are pushing for a model-based connection between engineering and manufacturing to increase quality, time to market and reduced handling costs. The model-based definition is not just a marketing issue as you can read from benefits reported by Jennifer Herron (Re-use your CAD – the model-based CAD handbook – describing practices and benefits already in 2013) or Tech-Clarity (The How-To Guide for adopting model-based definition – describing practices and benefits – sponsored by SolidWorks)

Oleg’s post unleashed several reactions of people who shared his opinion (read the comments here). They are all confused, t is all about marketing / let’s not change / too complex. Responses you usually hear from a generation that does not feel and understand the new approaches of a digital enterprise. If you are in the field working with multiple customers trying to understand the benefits of model-based definition, you would not worry about terminology – you would try to understand it and make it work.

Model-Based – just marketing?

In his post, Oleg refers to CIMdata’ s explanation of the various aspects of model-based in the context of PLM. Instead of referring to the meaning of the various acronyms, Peter Bilello (CIMdata) presented at the latest PDT conference (Oct 2017 – Gothenburg) an excellent story related to the various aspects of the model-based aspects, actually the whole conference was dedicated to the various aspects of a Model-Based Enterprise illustrates that it is not a vendor marketing issue. You can read my comments from the vendor-neutral conference here: The weekend after PDT Europe 2017 Part 1 and Part 2.

There were some dialogues on LinkedIn this weekend, and I promised to publish this post first before continuing on the other aspects of a model-based enterprise.  Just today Oleg published a secondary post related to this topic: Model-Based marketing in CAD and PLM, where again the tone and blame is to the PLM/CAD vendors, as you can see from his conclusion:

I can see “mode-based” as a new and very interesting wave of marketing in 3D CAD and PLM.  However, it is not pure marketing and it has some rational. The rational part of model-based approach is to have information model combined from 3D design and all connected data element. Such model can be used as a foundation for design, engineering, manufacturing, support, maintenance. Pretty much everything we do. It is hard to create such model and it is hard to combine a functional solution from existing packages and products. You should think how to combine multiple CAD systems, PLM platforms and many other things together. It requires standards. It requires from people to change. And it requires changing of status quo. New approaches in data management can change siloed world of 3D CAD and PLM. It is hard, but nothing to do with slides that will bring shiny words “model-base”. Without changing of technology and people, it will remain as a history of marketing

Again it shows the narrow mindset on the future of a model-based enterprise. When it comes to standards I recommend you to register and watch CIMdata’s educational webinar called: Model-Based Enterprise and Standards – you need to register. John MacKrell CIMdata’s chairman gives an excellent overview and status of model-based enterprise initiative.  After having studied and digested all the links in this post, I challenge you to make your mind up. The picture below comes from John’s presentation, an illustration where we are with model-based definition currently

 

Conclusion

The challenge of modern businesses is that too often we conclude too fast on complex issues or we frame new developments because they do not fit our purpose. You know it from politics. Be aware it is also valid in the world of PLM. Innovation and a path to a modern digital enterprise do not come easy – you need to invest and learn all the aspects. To be continued (and I do not have all the answers either)

The recent years I have been mentioning several times addressing the term model-based in the context of a modern, digital enterprise. Posts like: Digital PLM requires a model-based enterprise (Sept 2016) or Item-Centric or Model-Centric (Sept 2017) describe some of the aspects of a model-based approach. And if you follow the PLM vendors in their marketing messages, everyone seems to be looking for a model-based environment.

This is however in big contrast with reality in the field. In February this year I moderated a focus group related to PLM and the Model-Based approach and the main conclusion from the audience was that everyone was looking at it, and only a few started practicing. Therefore, I promised to provide some step-by-step education related to model-based as like PLM we need to get a grip on what it means and how it impacts your company. As I am not an academic person, it will be a little bit like model-based for dummies, however as model-based in all aspects is not yet a wide-spread common practice, we are all learning.

What is a Model?

The word Model has various meanings and this is often the first confusion when people speak about Model-Based. The two main interpretations in the context of PLM are:

  • A Model represents a 3D CAD Model – a virtual definition of a physical product
  • A Model represents a scientific / mathematical model

And although these are the two main interpretations there are more aspects to look at model-based in the context of a digital enterprise. Let’s explore the 3D CAD Model first

The role of the 3D CAD Model in a digital enterprise

Just designing a product in 3D and then generating 2D drawings for manufacturing is not really game-changing and bringing big benefits. 3D Models provide a better understanding of the product, mechanical simulations allow the engineer to discover clashes and/or conflicts and this approach will contribute to a better understanding of the form & fit of a product. Old generations of designers know how to read a 2D drawing and in their mind understand the 3D Model.

Modern generations of designers are no longer trained to start from 2D, so their way of thinking is related 3D modeling. Unfortunate businesses, in particular when acting in Eco-systems with suppliers, still rely on the 2D definition as the legal document.  The 3D Model has brought some quality improvements and these benefits already justify most of the companies to design in 3D, still it is not the revolution a model-based enterprise can bring.

A model-based enterprise has to rely on data, so the 3D Model should rely on parameters that allow other applications to read them. These parameters can contribute to simulation analysis and product optimization or they can contribute to manufacturing. In both cases the parameters provide data continuity between the various disciplines, eliminating the need to create new representations in different formats. I will come back in a future post to the requirements for the 3D CAD model in the context of the model-based enterprise, where I will zoom in on Model-Based Definition and the concepts of Industry 4.0.

The role of mathematical models in a digital enterprise

The mathematical model of a product allows companies to analyze and optimize the behavior of a product. When companies design a product they often start from a conceptual model and by running simulations they can optimize the product and define low-level requirements within a range that optimizes the product performance. The relation between design and simulation in a virtual model is crucial to be as efficient as possible. In the current ways of working, often design and simulation are not integrated and therefore the amount of simulations is relative low, as time-to-market is the key driver to introduce a new product.

In a digital enterprise, design and simulations are linked through parameters, allowing companies to iterate and select the optimal solution for the market quickly. This part is closely related to model-based systems engineering (MBSE) , where the focus is on defining complex systems. In the context of MBSE I will also zoom in on the relation between hardware and software, which at the end will deliver the desired functionality for the customer. Again in this part we will zoom in on the importance of having a parameter model, to ensure digital continuity.

Digital Twin

There is still a debate if the Digital Twin is part of PLM or should be connected to PLM. A digital twin can be based on a set of parameters that represent the product performance in the field. There is no need to have a 3D representation, despite the fact that many marketing videos always show a virtual image to visualize the twin.

Depending on the business desire, there can be various digital twins for the same products in the field, all depending on the parameters that you want to monitor. Again it is about passing parameters, in this case from the field back to R&D and these parameters should be passed in a digital manner. In a future post I will zoom in on the targets and benefits of the digital twin.

Conclusion

There are various aspects to consider related to “model-based”.  The common thread for each of the aspects is related to PARAMETERS.  The more you can work with parameters to connect the various usages of a product/system, the closer you are related to the digital enterprise. The real advantages of a digital enterprise are speed (information available in real-time), end-to-end visibility (as data is not locked in files / closed systems).

PARAMETERS the objects to create digital continuity

 

 

 

 

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