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In my previous post, the PLM blame game, I briefly mentioned that there are two delivery models for PLM. One approach based on a PLM system, that contains predefined business logic and functionality, promoting to use the system as much as possible out-of-the-box (OOTB) somehow driving toward a certain rigidness or the other approach where the PLM capabilities need to be developed on top of a customizable infrastructure, providing more flexibility. I believe there has been a debate about this topic over more than 15 years without a decisive conclusion. Therefore I will take you through the pros and cons of both approaches illustrated by examples from the field.
PLM started as a toolkit
The initial cPDM/PLM systems were toolkits for several reasons. In the early days, scalable connectivity was not available or way too expensive for a standard collaboration approach. Engineering information, mostly design files, needed to be shared globally in an efficient manner, and the PLM backbone was often a centralized repository for CAD-data. Bill of Materials handling in PLM was often at a basic level, as either the ERP-system (mostly Aerospace/Defense) or home-grown developed BOM-systems(Automotive) were in place for manufacturing.
Depending on the business needs of the company, the target was too connect as much as possible engineering data sources to the PLM backbone – PLM originated from engineering and is still considered by many people as an engineering solution. For connectivity interfaces and integrations needed to be developed in a time that application integration frameworks were primitive and complicated. This made PLM implementations complex and expensive, so only the large automotive and aerospace/defense companies could afford to invest in such systems. And a lot of tuition fees spent to achieve results. Many of these environments are still operational as they became too risky to touch, as I described in my post: The PLM Migration Dilemma.
The birth of OOTB
Around the year 2000, there was the first development of OOTB PLM. There was Agile (later acquired by Oracle) focusing on the high-tech and medical industry. Instead of document management, they focused on the scenario from bringing the BOM from engineering to manufacturing based on a relatively fixed scenario – therefore fast to implement and fast to validate. The last point, in particular, is crucial in regulated medical environments.
At that time, I was working with SmarTeam on the development of templates for various industries, with a similar mindset. A predefined template would lead to faster implementations and therefore reducing the implementation costs. The challenge with SmarTeam, however, was that is was very easy to customize, based on Microsoft technology and wizards for data modeling and UI design.
This was not a benefit for OOTB-delivery as SmarTeam was implemented through Value Added Resellers, and their major revenue came from providing services to their customers. So it was easy to reprogram the concepts of the templates and use them as your unique selling points towards a customer. A similar situation is now happening with Aras – the primary implementation skills are at the implementing companies, and their revenue does not come from software (maintenance).
The result is that each implementer considers another implementer as a competitor and they are not willing to give up their IP to the software company.
SmarTeam resellers were not eager to deliver their IP back to SmarTeam to get it embedded in the product as it would reduce their unique selling points. I assume the same happens currently in the Aras channel – it might be called Open Source however probably it is only high-level infrastructure.
Around 2006 many of the main PLM-vendors had their various mid-market offerings, and I contributed at that time to the SmarTeam Engineering Express – a preconfigured solution that was rapid to implement if you wanted.
Although the SmarTeam Engineering Express was an excellent sales tool, the resellers that started to implement the software began to customize the environment as fast as possible in their own preferred manner. For two reasons: the customer most of the time had different current practices and secondly the money come from services. So why say No to a customer if you can say Yes?
OOTB and modules
Initially, for the leading PLM Vendors, their mid-market templates were not just aiming at the mid-market. All companies wanted to have a standardized PLM-system with as little as possible customizations. This meant for the PLM vendors that they had to package their functionality into modules, sometimes addressing industry-specific capabilities, sometimes areas of interfaces (CAD and ERP integrations) as a module or generic governance capabilities like portfolio management, project management, and change management.
The principles behind the modules were that they need to deliver data model capabilities combined with business logic/behavior. Otherwise, the value of the module would be not relevant. And this causes a challenge. The more business logic a module delivers, the more the company that implements the module needs to adapt to more generic practices. This requires business change management, people need to be motivated to work differently. And who is eager to make people work differently? Almost nobody, as it is an intensive coaching job that cannot be done by the vendors (they sell software), often cannot be done by the implementers (they do not have the broad set of skills needed) or by the companies (they do not have the free resources for that). Precisely the principles behind the PLM Blame Game.
OOTB modularity advantages
The first advantage of modularity in the PLM software is that you only buy the software pieces that you really need. However, most companies do not see PLM as a journey, so they agree on a budget to start, and then every module that was not identified before becomes a cost issue. Main reason because the implementation teams focus on delivering capabilities at that stage, not at providing value-based metrics.
The second potential advantage of PLM modularity is the fact that these modules supposed to be complementary to the other modules as they should have been developed in the context of each other. In reality, this is not always the case. Yes, the modules fit nicely on a single PowerPoint slide, however, when it comes to reality, there are separate systems with a minimum of integration with the core. However, the advantage is that the PLM software provider now becomes responsible for upgradability or extendibility of the provided functionality, which is a serious point to consider.
The third advantage from the OOTB modular approach is that it forces the PLM vendor to invest in your industry and future needed capabilities, for example, digital twins, AR/VR, and model-based ways of working. Some skeptic people might say PLM vendors create problems to solve that do not exist yet, optimists might say they invest in imagining the future, which can only happen by trial-and-error. In a digital enterprise, it is: think big, start small, fail fast, and scale quickly.
OOTB modularity disadvantages
Most of the OOTB modularity disadvantages will be advantages in the toolkit approach, therefore discussed in the next paragraph. One downside from the OOTB modular approach is the disconnect between the people developing the modules and the implementers in the field. Often modules are developed based on some leading customer experiences (the big ones), where the majority of usage in the field is targeting smaller companies where people have multiple roles, the typical SMB approach. SMB implementations are often not visible at the PLM Vendor R&D level as they are hidden through the Value Added Reseller network and/or usually too small to become apparent.
Toolkit advantages
The most significant advantage of a PLM toolkit approach is that the implementation can be a journey. Starting with a clear business need, for example in modern PLM, create a digital thread and then once this is achieved dive deeper in areas of the lifecycle that require improvement. And increased functionality is only linked to the number of users, not to extra costs for a new module.
However, if the development of additional functionality becomes massive, you have the risk that low license costs are nullified by development costs.
The second advantage of a PLM toolkit approach is that the implementer and users will have a better relationship in delivering capabilities and therefore, a higher chance of acceptance. The implementer builds what the customer is asking for.
However, as Henry Ford said, if I would ask my customers what they wanted, they would ask for faster horses.
Toolkit considerations
There are several points where a PLM toolkit can be an advantage but also a disadvantage, very much depending on various characteristics of your company and your implementation team. Let’s review some of them:
Innovative: a toolkit does not provide an innovative way of working immediately. The toolkit can have an infrastructure to deliver innovative capabilities, even as small demonstrations, the implementation, and methodology to implement this innovative way of working needs to come from either your company’s resources or your implementer’s skills.
Uniqueness: with a toolkit approach, you can build a unique PLM infrastructure that makes you more competitive than the other. Don’t share your IP and best practices to be more competitive. This approach can be valid if you truly have a competing plan here. Otherwise, the risk might be you are creating a legacy for your company that will slow you down later in time.
Performance: this is a crucial topic if you want to scale your solution to the enterprise level. I spent a lot of time in the past analyzing and supporting SmarTeam implementers and template developers on their journey to optimize their solutions. Choosing the right algorithms, the right data modeling choices are crucial.
Sometimes I came into a situation where the customer blamed SmarTeam because customizations were possible – you can read about this example in an old LinkedIn post: the importance of a PLM data model
Experience: When you plan to implement PLM “big” with a toolkit approach, experience becomes crucial as initial design decisions and scope are significant for future extensions and maintainability. Beautiful implementations can become a burden after five years as design decisions were not documented or analyzed. Having experience or an experienced partner/coach can help you in these situations. In general, it is sporadic for a company to have internally experienced PLM implementers as it is not their core business to implement PLM. Experienced PLM implementers vary from size and skills – make the right choice.
Conclusion
After writing this post, I still cannot write a final verdict from my side what is the best approach. Personally, I like the PLM toolkit approach as I have been working in the PLM domain for twenty years seeing and experiencing good and best practices. The OOTB-box approach represents many of these best practices and therefore are a safe path to follow. The undecisive points are who are the people involved and what is your business model. It needs to be an end-to-end coherent approach, no matter which option you choose.
This is the moment of the year to switch-off from the details. No more talking and writing about digital transformation or model-based approaches. It is time to sit back and relax. Two years ago I shared the PLM Songbook, now it is time to see one or more movies. Here are my favorite top five PLM movies:
Bruce Almighty
Bruce Nolan, an engineer in Buffalo, N.Y., is discontented with almost everything in the company despite his popularity and the love of his draftswoman Grace. At the end of the worst day of his life, Bruce angrily ridicules and rages against PLM and PLM responds. PLM appears in human form and, endowing Bruce with divine powers op collaboration, challenges Bruce to take on the big job to see if he can do it any better.
A movie that makes you modest and you realize there is more than your small ecosystem.
The good, the bad and the ugly
Blondie (The Good PLM consultant) is a professional who is out trying to earn a few dollars. Angel Eyes (The Bad PLM Vendor) is a PLM salesman who always commits to a task and sees it through, as long as he is paid to do so. And Tuco (The Ugly PLM Implementer) is a wanted outlaw trying to take care of his own hide. Tuco and Blondie share a partnership together making money off Tuco’s bounty, but when Blondie unties the partnership, Tuco tries to hunt down Blondie. When Blondie and Tuco come across a PLM implementation loaded with dead bodies, they soon learn from the only survivor (Bill Carson – the PLM admin) that he and a few other men have buried a stash of value on a file server. Unfortunately, Carson dies, and Tuco only finds out the name of the file server, while Blondie finds out the name on the hard disk. Now the two must keep each other alive in order to find the value. Angel Eyes (who had been looking for Bill Carson) discovers that Tuco and Blondie met with Carson and knows they know the location of the value. All he needs is for the two to ..
A movie that makes you realize that it is a challenging journey to find the value out of PLM. It is not only about execution – but it is also about all the politics of people involved – and there are good, bad and ugly people on a PLM journey.
The Grump
The Grump is a draftsman in Finland from the past. A man who knows that everything used to be so much better in the old days. Pretty much everything that’s been done after 1953 has always managed to ruin The Grump’s day. Our story unfolds The Grump opens a 3D Model on his computer, hurting his brain. He has to spend a weekend in Helsinki to attend a model-based therapy. Then the drama unfolds …….
A movie that makes you realize that progress and innovation do not come from grumps. In every environment when you want to do a change of the status quo, grumps will appear. With the exciting Finish atmosphere, a perfect film for Christmas.
Deliverance
The Cahulawassee River Valley company in Northern Georgia is one of the last analog companies in the state, which will soon change with the imminent implementation of a PLM system in the company, breaking down silos everywhere. As such, four Atlanta city slickers, alpha male Lewis Medlock, generally even-keeled Ed Gentry, slightly condescending Bobby Trippe, and wide-eyed Drew Ballinger decide to implement PLM in one trip, with only Lewis and Ed having experience in CAD. They know going in that the area is ethnoculturally homogeneous and isolated, but don’t understand the full extent of such until they arrive and see what they believe is the result of generations of inbreeding. Their relatively peaceful trip takes a turn for the worse when half way through they encounter a couple of hillbilly moonshiners. That encounter not only makes the four battle their way out of the PLM project intact and alive but threatens the relationships of the four as they do.
This movie, from 1972, makes you realize that in the early days of PLM starting a big-bang implementation journey into an area that is not ready for it, can be deadly, for your career and friendship. Not suitable for small children!
Diamonds Are Forever or Tron (legacy)
James Bond’s mission is to find out who has been drawing diamonds, which are appearing on blogs. He adopts another identity in the form of Don Farr. He joins up with CIMdata and acts as if he is developing diamonds, but everyone is hungry for these diamonds. He also has to avoid Mr. Brouwer and Mr. Kidd, the dangerous couple who do not leave anyone in their way when it comes to model-based. And Ernst Stavro Blofeld isn’t out of the question. He may have changed his looks, but is he linked with the V-shape? And if he is, can Bond finally defeat his ultimate enemy?
Sam Flynn, the tech-savvy 27-year-old son of Kevin Flynn, looks into his father’s disappearance and finds himself pulled into the same world of virtual twins and augmented reality where his father has been living for 20 years. Along with Kevin’s loyal confidant Quorra, father and son embark on a life-and-death journey across a visually-stunning cyber universe that has become far more advanced and exceedingly dangerous. Meanwhile, the malevolent program IoT, who dominates the digital world, plans to invade the real world and will stop at nothing to prevent their escape
I could not decide about number five. The future is bright with Boeing’s new representation of Systems Engineering, see my post on CIMdata’s PLM Europe roadmap event where Don Farr presented his diamond(s). However, the future is also becoming a mix of real with virtual and here Tron (legacy) will help my readers to understand the beauty of a mixed virtual and real world. You can decide – or send me your favorite PLM movies.
Note: All movie reviews are based on IMBd.com story lines, and I thank the authors of these story lines for their contribution and hope they agree with the PLM-related twist. Click on the image to find the full details and original review.
Conclusion
2018 has been an exciting year with a lot of buzzwords combined with the reality that the current PLM approach is incompatible with the future. How we can address this issue more in 2019 – first at PI PLMx 2019 in London (be there – last chance to meet people in the UK when they are still Europeans and share/discuss plans for the upcoming year)
Wishing you all the best during the break and a happy and prosperous 2019
A week ago I attended the joined CIMdata Roadmap and PDT Europe conference in Stuttgart as you can recall from last week’s post: The weekend after CIMdata Roadmap / PDT Europe 2018. As there was so much information to share, I had to split the report into two posts. This time the focus on the PDT Europe. In general, the PDT conferences have always been focusing on sharing experiences and developments related to standards. A topic you will not see at PLM Vendor conferences. Therefore, your chance to learn and take part if you believe in standards.
This year’s theme: Collaboration in the Engineering and Manufacturing Supply Chain – the Extended Digital Thread and Smart Manufacturing. Industry 4.0 plays a significant role here.
Model-based X: What is it and what is the status?
I have seen Peter Bilello presenting this topic now several times, and every time there is a little more progress. The fact that there is still an acronym war illustrated that the various aspects of a model-based approach are not yet defined. Some critics will be stating that’s because we do not need model-based and it is only a vendor marketing trick again. Two comments here:
- If you want to implement an end-to-end model-based approach including your customers and supply chain, you cannot avoid standard. More will become clear when you read the rest of this post. Vendors will not promote standards as it reduces their capabilities to deliver unique So standards must come from the market, not from the marketing.
- In 2007 Carl Bass, at that time CEO at Autodesk made his statement: “There are only three customers in the world that have a PLM problem; Dassault, PTC, and There are no other companies that say I have a PLM problem”. Have a look here. PLM is understood by now and even by Autodesk. The statement illustrates that in the beginning the PLM target was not clear and people thought PLM was a system instead of a strategic approach. Model-based ways of working have to go through the same learning path, hopefully, faster.
Peter’s presentation was a good walk-through pointing out what exists, where we focus and that there is still working to be done. Not by vendors but by companies. Therefore I wholeheartedly agree with Peter’s closing remarks – no time to sit back and watch if you want to benefit from model-based approaches.
Smart Manufacturing
Kenny Swope, known from his presentations related to Boeing, now spoke to us as the Chair of the ISO/TC 184/SC 4 workgroup related to Industrial Data. To say it in decoded mode: Kenny is heading Sub-committee 4 with a focus on Industrial Data. SC4 is part of a more prominent theme: Automation Systems and integration identified by TC 184 all as part of the ISO framework. The scope:
Standardization of the content, meaning, structure, representation and quality management of the information required to define an engineered product and its characteristics at any required level of detail at any part of its lifecycle from conception through disposal, together with the interfaces required to deliver and collect the information necessary to support any business or technical process or service related to that engineered product during its lifecycle.
Perhaps boring to read if you think about all the demos you have seen at trade shows related to Smart Manufacturing. If you want these demos to become true in a vendor-independent environment, you will need to agree on a common framework of definitions to ensure future continuity beyond the demo. And here lies the business excitement, the real competitive advantages companies can have implementing Smart Manufacturing in a Scaleable, future-oriented way.
One of the often heard statements is that standards are too slow or incomplete. Incomplete is not a problem when there is a need, the standard will follow. Compare it with language, we will always invent new words for new concepts.
Being slow might be the case in the past. Kenny showed the relative fast convergence from country-specific Smart Manufacturing standards into a joined ISO/IEC framework – all within three years. ISO and IEC have been teaming-up already to build Smart Manufacturing Reference models.
This is already a considerable effort, as the local reference models need to be studied and mapped to a common architecture. The target is to have a first Technical Specification for a joint standard final 2020 – quite fast!
Meinolf Gröpper from the German VDMA presented what they are doing to support Smart Manufacturing / Industrie 4.0. The VDMA is a well-known engineering federation with 3200 member companies, 85 % of them are Small and Medium Enterprises – the power of the German economy.
The VDMA provides networking capabilities, readiness assessments for members to be the enabler for companies to transform. As Meinolf stated Industrie 4.0 is not about technology, it is about cross-border services and international cooperation. A strategy that every company has to develop and if possible implement at its own pace. Standards will accelerate the implementation of Industrie 4.0
The Smart Manufacturing session was concluded by Gunilla Sivard, Professor at KTH in Stockholm and Hampus Wranér, Consultant at Eurostep. They presented the work done on the DIgln project, targeting an infrastructure for Smart Manufacturing.
The presentation showed the implementation of the testbed using twittering bus communication and the ISO 10303-239 PLCS information standard as the persistent layer. The results were promising to further build capabilities on top of the infrastructure below:
The conclusion from the Smart Manufacturing session was that emerging and available standards can accelerate the deployment.
Enabling digital continuity in the Factory of the Future
Alcibiades Gonzalez-Noval from Airbus shared challenges and the strategy for Airbus’s factory of the future based on digital continuity from the virtual world towards the physical world, connecting with PLM, ERP, and MOM. Concepts many companies are currently working on with various maturity stages.
I agree with his lessons learned. We cannot think in silos anymore in a digital future – everything is connected. And please forget the PoC, to gain time start piloting and fail or succeed fast. Companies have lost years because of just doing PoCs and not going into action. The last point, networks segregation for sure is an issue, relevant for plant operations. I experienced this also in the past when promoting PLM concepts for (nuclear) owners/operators of plants. Network security is for sure an issue to resolve.
Cross-Discipline Lifecycle Collaboration Forum
Setting up the digital thread across engineering and the value chain.
Peter Gerber, Chairman of CDLC Forum and Data Exchange & Integration Leader at Schaefller and Pierre Bodin at Senior Manager Mews Partners, presented their findings related to the challenge of managing complex products (mechanical, electrical, software using system engineering methodology) to work properly at affordable cost in a real-time mode, multidisciplinary and coordination across the whole value chain. Something you might expect could be done when reviewing all PLM Vendor’s marketing materials, something you might expect hard to do when remembering Martin Eigner’s statement that 95 % of the companies have not solved mechatronics collaboration yet. (See: The weekend after CIMdata PLM Roadmap and PDT Europe)
A demonstrator was defined, and various vendors participated in building a demonstrator based on their Out-Of-The-Box capabilities. The result showed that for all participants there were still gaps to resolve for full collaboration. A new version of the demonstrator is now planned for the middle of next year – curious to learn the results at that time. Multi-disciplinary collaboration is a (conceptual) pillar for future digital business – it needs to be possible.
A Digital Thread based on the PLCS standard.
Nigel Shaw, Eurostep’s managing director in the UK, took us through his evolution of PLCS (Product Life Cycle Support) and extension of the ISO 10303 STEP standard. (STEP Standard for Exchange of Product data). Nigel mentioned how over all these years, millions (and a lot of brain power) have been invested in PLCS to where it is now.
PLCS has been extremely useful as an interface standard for contracting, provide product data in a neutral way. As an example, last year the Swedish Defense organization (FMV) and France’s DGA made PLCS DEXs as part of the contractual conditions. It would be too costly to have all product data for all defense systems in proprietary vendor formats and this over the product lifecycle.
Those following the standards in the process industry will rely on ISO 15926 / CFIHOS as this standard’s dictionary, and data model is more geared to process data- and in particular the exchange of data from the various contractors with the owner/operator.
Coming back to PLCS and the Digital Twin – it is all about digital continuity of information. Otherwise, if we have to recreate information in every lifecycle stage of a product (design/manufacturing / operations), it will be too costly and not digital connected. This illustrates the growing needs for standards. I had nothing to add to Nigel’s conclusions:
It is interesting to note that product management has moved a long way over the last 10-20 years however as we include more and more into PLM, there are all the time new concepts to be solved. The cases we discuss today in our PLM communities were most of the time visions 10 years ago. Nowadays we want to include Model-Based Systems Engineering, 3D Modeling and simulation, electronics and software and even aftermarket, product support in true PLM. This was not the case 20 years ago. The people involved in the development of PLCS were for sure visionaries as product data connectivity along the whole lifecycle is needed and enabled by the standard.
Investing in Industry 4.0?
Hard Realities of the Grand Vision.
Marc Halpern from Gartner is one of the regular speakers at the PDT conference. Unfortunate he could not be with us that day, however, through a labor-intensive connection (mobile phone close to the speaker and Nigel Shaw trying to stay in sync with the presented slides) we could hear Marc speak about what we wanted to achieve too – a digital continuity.
Marc restated the massive potential of Industrie 4.0 when it comes to scalability, agility, flexibility, and efficiency.
Although technologies are evolving rapidly, it is the existing legacy that inhibits fast adoption. A topic that was also central in my presentation. It is not just a change in technology, there is much more connected.
Marc recommends a changing role for IT, where they should focus more on business priorities and business leadership strategies. This as opposed to the classical role of the IT organization where IT needed to support the business, now they will be part of leading the business too.
To orchestrate such an IT evolution, Marc recommends a “systems of systems” planning and execution across IT and Business. One of my recent blog posts: Moving to a model-based enterprise: The business (information) model can be seen in that context.
How to deal with the incompatible future?
I was happy to conclude the sessions with the topic that concerns me the most at this time. Companies in their current business are already struggling to get aligned and coordinated between disciplines and external stakeholders, the gap to be connected is vast as it requires a master data management approach, an enterprise data model and model-based ways of working. Read my posts from the past ½ year starting here, and you get the picture.
Note: This image is based on Marc Halpern’s (Gartner) Technology/Maturity diagram from PDT 2015
I concluded with explaining companies need to learn to work in two modes. One mode will be the traditional way of working which I call the coordinated approach and a growing focus on operating in a connected mode. You can see my full presentation here on SlideShare: How to deal with the incompatible future.
Conclusion
The conference was closed with a panel discussion where we shared our concerns related to the challenges companies face to change their traditional ways of working meanwhile entering a digital era. The positive points are there – baby steps – PLM is becoming understood, the significance of standards is becoming more clear. The need: a long-term vision.
This concludes my review of an excellent conference – I learned again a lot and I hope to see you next year too. Thanks again to CIMdata and Eurostep for organizing this event
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)
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
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):
- The Innovator’s Dilemma by Clayton Christensen
- On Intelligence by Jeff Hawkins
- Thinking, Fast and Slow by Daniel Kahneman
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
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?
Model-based continued: Model-Based Definition
After a short celebration, 10 years blogging and 200 posts, now it is time to continue my series related to the future of model-based. So far my introduction and focus on the bigger picture of the term Model-Based has led to various reactions. In particular, related to Model-Based Definition, the topic I am going to discuss in this post. Probably this is the topic where opinions vary the most as it is more close to the classical engineering and manufacturing processes.
What is Model-Based Definition?
There are various definitions of the term Model-Based Definition. Often the term Model-Based Enterprise is used in the same context. Where some people might stop thinking because the terminology is not 100 % aligned, I propose to focus on content. Let’s investigate what it is.
In the classical product lifecycle, a product is first designed for its purpose based on specifications. The product can be simple, purely mechanical or more complex, requiring mechanical design, electronic components, and software to work together. For the first case, I will focus on Model-Based definition, for the second case I recommend to start reading about Model-Based Systems Engineering approaches where the mechanical design is part of a more complex system.
Model-Based Definition for Mechanical Designs – the role of 2D
Historically designs were done on the drawing board in 2D. After the introduction of 2D CAD and later affordable 3D CAD systems at the end of the previous century, companies made a shift from designing in 2D towards 3D. The advantages were clear. A much better understanding of products. Reading a 2D drawing requires special skills and sometimes they were not unambiguous. Therefore, 3D CAD models lead to increased efficiency and quality combined with the potential to reuse and standardize parts or sub-assemblies in a design.
These benefits were not always observed as complementary to the design (the engineering point of view), there was still the need to describe and define how a product needs to be manufactured. The manufacturing definition remained in a set of 2D drawings, and the 2D Drawings were the legal authority describing the product.
An interesting side note observation:
You will still see in industrial machinery companies, a pure EBOM does not exist, as designs were made to target the manufacturing drawings, not the 3D Model, engineering focused, intent. In this type of companies, the discussion EBOM/MBOM is challenging to explain.
Once the 3D Model becomes the authority, the split between design and manufacturing information will create extra work if you keep on creating 2D drawings for manufacturing. It requires non-value added extra work, i.e., reinterpreting 3D data in 2D formats (extra engineering hours) and there is the risk for new errors (interpretations/versioning issues). This non-value added engineering time can add up to over 30 percent of the time spent by engineering. You can find these numbers through the links below this post. I will not be the MBD teacher in this post, I will focus on the business impact.
Model-Based Definition based on 3D
3D PDF Model
The logical step is to use the 3D Model and add manufacturing information attached to the model, through different views. This can be Geometric Dimensioning and Tolerancing information (GF&T), Quality measurement information, Assembly instructions and more, all applied to different views of the model.
Of course here you become dependent on the chosen environments that support the combination of a 3D CAD model combined with annotation views that can be selected in the context of the model. There are existing standards how to annotate a model, find your most practical standard to your industry / Eco-system. Next, most CAD vendors and PLM vendors have their proprietary 3D formats and when you stay within their solution range working with a model-based definition will bring direct benefits, however …..
Model-Based Definition data standards
Every company needs to be able to combine and share information internally with other teams or with partners and suppliers, so a single vendor solution is a utopia. Even if your company has standardized themselves to one system, the next acquisition might be disturbing this dream. Anticipating for openness is crucial and when you start working according to a model-based definition, make sure that at least you have import or export capabilities from within your environment towards model-based definition standards.
The two major standards for model-based definition are 3DPDF and AP242/JT based. Don’t expect these standards to be complete. They will give you a good foundation for your model-based journey and make sure you are part of this journey. (Listen to the CIMdata webinar also listed below)
The Model-Based journey
It took almost 20 years for 3D CAD to become the mainstream for mechanical design. Engineers are now trained in 3D and think in 3D. Now it is time to start the journey to abandon 2D and connect engineering, manufacturing and service more efficient. Similar gains can be expected. Follow the links below this article, here already a quote from an old post by Isha Gupta Ray (Capgemini) related to MBD:
MBE Drivers: The need for consumption of 3D product data by non-engineering departments and the elimination of 2D drawing related rework and costs are driving companies to adopt 3D MBE methods rapidly. DoD predicts that the move away from 2D Drawings and into open and free-to-view 3D MBE documents will reduce the cost of its internal engineering activities by up to 30%, reduce the scrap and rework it currently deals with from its supply channel by nearly 20% and improves supplier response times by up to 50%.
Conclusion
Model-Based Definition is not as challenging as becoming a Model-Driven enterprise, that I described in my introduction post to this theme. It is a first step to challenge or energize your company to become a digital enterprise, as sharing between engineering and manufacturing needs to be orchestrated, even with your external parties. It is easy to do nothing and to wait till your company is pushed or pushed out, which would cause extra stress (or relieve forever). For me Model-Based Definition is a first (baby) step towards a digital enterprise, warming-up your company to change a look at your data in a different way. Next when you combine parameters and simulation to your models, you will make the next step towards a model-driven digital enterprise.
Below a selection of links related to the theme of Model-Based Definition. If you feel I missed some crucial links, please provide them through the comments section of this post, and I will add them to the post if relevant.
Tech-Clarity: The How-to Guide for Adopting Model Based Definition (MBD)
Action Engineering: Articles, Blog plus training
Engineering.com: How Model-Based Definition Can Fix Your CAD Models
Lifecycle Insights: Quantifying the value of Model-Based definitions
CIMdata: Webinar on Model-Based Definition and Standards
Capgemini: Model-Based Enterprise with 3D PDF
if you want to learn more in-depth the advanced usage and potential of MBD, try to understand:
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
At this moment there are two approaches to implement PLM. The most common practice is item-centric and model-centric will be potentially the best practice for the future. Perhaps your company still using a method from the previous century called drawing-centric. In that case, you should read this post with even more attention as there are opportunities to improve.
The characteristics of item-centric
In an item-centric approach, the leading information carrier is an item also known as a part. The term part is sometimes confusing in an organization as it is associated with a 3D CAD part. In SAP terminology the item is called Material, which is sometimes confusing for engineering as they consider Material the raw material. Item-centric is an approach where items are managed and handled through the whole lifecycle. In theory, an item can be a conceptual item (for early estimates), a design item (describing the engineering intent), a manufacturing item (defining how an item is consumed) and potentially a service item.
The picture below illustrates the various stages of an item-centric approach. Don’t focus on the structure, it’s an impression.
It is clear these three structures are different and can contain different item types. To read more about the details for an EBOM/MBOM approach read these post on my blog:
Back to item-centric. This approach means that the item is the leading authority of the product /part. The id and revision describe the unique object in the database, and the status of the item tells you in the current lifecycle stage for the item. In some cases, where your company makes configurable products also the relation between two items can define effectivity characteristics, like data effectivity, serial number effectivity and more. From an item structure, you can find its related information in context. The item points to the correct CAD model, the assembly or related manufacturing drawings, the specifications. In case of an engineering item, it might point towards approved manufacturers or approved manufacturing items.
Releasing an item or a BOM means the related information in context needs to validated and frozen too. In case your company works with drawings for manufacturing, these drawings need to be created, correct and released, which sometimes can be an issue due to some last-minute changes that can happen. The above figure just gives an impression of the potential data related to an item. It is important to mention that reports, which are also considered documents, do not need an approval as they are more a snapshot of the characteristics at that moment of generation.
The advantages of an item-centric approach are:
- End-to-end traceability of information
- Can be implemented in an evolutionary approach after PDM-ERP without organizational changes
- It enables companies to support sharing of information
- Sharing of information forces companies to think about data governance
(not sure if a company wants to invest on that topic)
The main disadvantages of an item-centric approach are:
- Related information on the item is not in context and therefore requires its own management and governance to ensure consistency
- Related information is contained in documents, where availability and access is not always guaranteed
Still, the item-centric approach brings big benefits to a company that was working in a classical drawing-driven PDM-ERP approach. An additional remark needs to be made that not every company will benefit from an item-centric approach as typically Engineering-to-Order companies might find this method creating too much overhead.
The characteristics of Model-Centric
A model-centric approach is considered the future approach for modern enterprises as it brings efficiency, speed, multidisciplinary collaboration and support for incremental innovation in an agile way. When talking about a model-centric approach, I do not mean a 3D CAD model-centric approach. Yes, in case the product is mature, there will be a 3D Model serving as a base for the physical realization of the product.
However, in the beginning, the model can be still a functional or logical model. In particular, for complex products, model-based systems engineering might be the base for defining the solution. Actually, when we talk about products that interact with the outside world through software, we tend to call them systems. This explains that model-based systems engineering is getting more and more a recommended approach to make sure the product works as expected, fulfills all the needs for the product and creates a foundation for incremental innovation without starting from scratch.
Where the model-based architecture provides a framework for all stakeholders, the 3D CAD model will be the base for a digital thread towards manufacturing. Linking parameters from the logical and functional model towards the physical model a connection is created without the need to create documents or input-files for other disciplines. Adding 3D Annotations to the 3D CAD model and manufacturing process steps related to the model provides a direct connection to the manufacturing process.
The primary challenge of this future approach is to have all these data elements (requirements, functions, components, 3D design instances, manufacturing processes & resources to be connected in a federated environment (the product innovation platform). Connecting, versioning and baselining are crucial for a model-centric approach. This is what initiatives like Industry 4.0 are now exploring through demonstrators, prototypes to get a coherent collection of managed data.
Once we are able to control this collection of managed data concepts of digital twin or even virtual twin can be exploited linking data to a single instance in the field.
Also, the model can serve as the foundation for introduction incremental innovation, bringing in new features. As the model-based architecture provides direct visibility for change impact (there are no documents to study), it will be extremely lean and cost-efficient to innovate on an existing product.
Advantages of model-centric
- End-to-end traceability of all data related to a product
- Extremely efficient in data-handling – no overhead on data-conversions
- Providing high-quality understanding of the product with reduced effort compared to drawing-centric or item-centric approaches
- It is scalable to include external stakeholders directly (suppliers/customers) leading to potential different, more beneficial business models
- Foundation for Artificial Intelligence at any lifecycle step.
Disadvantages of model-centric
- It requires a fundamentally different way of working compared to past. Legacy departments, legacy people, and legacy data do not fit directly into the model-centric approach. A business transformation is required, not evolution.
- It is all about sharing data, which requires an architecture that is built to share information across Not through a service bus but as a (federated) platform of information.
A platform requires a strong data governance, both from the dictionary as well as authorizations which discipline is leading/following. - There is no qualified industrial solution from any vendor yet at this time. There is advanced technology, there are demos, but to my knowledge, there is no 100% model-centric enterprise yet. We are all learning. Trying to distinguish reality from the hype.
Conclusions
The item-centric approach is the current best practice for most PLM implementations. However, it has the disadvantage that it is not designed for a data-driven approach, the foundation of a digital enterprise. The model-centric approach is new. Some facets already exist. However, for the total solution companies, vendors, consultants, and implementers are all learning step-by-step how it all connects. The future of model-centric is promising and crucial for survival.
Jos Voskuil's Weblog 
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