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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.



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%.


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 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:

CIMdata: Minimum MDB and BOM definition with STEP AP 242

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.


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.



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.

Do you want to learn where we are now related to a model-centric approach?
Come to PDT2017 in Gothenburg on 18-19th October and find out more from the experts and your peers.

PLM holiday thoughts

July and August are the months that privileged people go on holiday. Depending on where you live and work it can be a long weekend or a long month. I plan to give my PLM twisted brain a break for two weeks. I am not sure if it will happen as Greek beaches always have inspired for philosophers. What do you think about “PLM on the beach”?

There are two topics that keep me intrigued at this moment, and I hope to experience more about them the rest of the year.

Moving to Model-Based processes

I believe we all get immune for the term “Digital Transformation” (11.400.000 hits on Google today). I have talked about digital transformation in the context many times too. Change is happening. The classic ways of working were based on documents, a container of information, captured on paper (very classical) or captured in a file (still current).

As every stakeholder in a company (marketing, engineering, manufacturing, supplier, services, customers, and management) required a different set of information, many pieces of information all referring to the same product, have been parsed and modified into other documents.  It is costly and expensive to get a complete view of what is happening in the business. Meanwhile, all these information transformations (with Excel as the king) are creating an overhead for information management, both on IT-level and even more for non-value added resources who are manipulating information for the next silo/discipline.

What we have learned from innovative companies is that a data-driven approach, where more granular information is stored uniquely as data objects instead of document containers bring huge benefits. Information objects can be shared where relevant along the product lifecycle and without the overhead of people creating and converting documents, the stakeholders become empowered as they can retrieve all information objects they desire (if allowed). We call this the digital thread.

The way to provide a digital thread for manufacturing companies is to change the way they organize the product development and delivery processes. A model-based approach is required. I wrote about in a post: Digital PLM requires a Model-Based Enterprise a year ago. The term “Model-Based” also has many variations (67.800.00 hits on Google today). Some might consider the 3D MCAD Model at the center of information both for engineering and manufacturing.A good overview in the video below

Others might think about a behavior/simulation model of the product for simulating and delivering a digital twin often referred in the context of model-based design (MBD).

And ultimately a model-based approach integrated with systems engineering into Model-Based Systems Engineering (MBSE) allowing all stakeholders to collaborate in a data-driven manner around complex products based.

You can learn a lot about that during the upcoming PDT Europe conference on 18-19th October in Gothenburg. Concepts and experiences will be shared, and my contribution to the conference will be all about the challenges and lessons learned from the transformation process companies are embarking on becoming model-based.


A second topic that becomes more and more relevant for companies is how to combine the domains of product development and application software empowering these products. The challenge here is that we have no mature concepts yet for both domains. It reminds me of the early PDM implementations where companies implemented their PDM system for MCAD software and documents. All the electrical stuff was done disconnected in separate systems and somewhere in the product lifecycle information from MCAD and ECAD was merged in the bill of materials and documents. Mainly manually with a decent overhead for people consolidating the data.  Modern PLM systems have found best practices to manage a combination of mechanical and electronic components through an EBOM even connecting embedded software as an item in the BOM.

Now more and more the behavior and experience of products are driven by software. Sensors and connectivity of data are driving new capabilities and business models to the market. Customers are getting better connected, however also the companies delivering these solutions can act much faster now based on trends or issues experienced from the field.

The challenge, however, is that the data coming from the systems and the software defining the behavior of the products most of the time is managed in a separate environment, the ALM environment. In the ALM environment delivery of new solutions can be extremely fast and agile, creating a disconnect between the traditional product delivery processes and the software delivery processes.

Companies are learning now how to manage the dependencies between these two domains, as consistency of requirements and features of the products is required. Due to the fast pace of software changes, it is almost impossible to connect everything to the PLM product definition. PLM Vendors are working on concepts to connect PLM and ALM through different approaches. Other companies might believe that their software process is crucial and that the mechanical product becomes a commodity. Could you build a product innovation platform starting from the software platform which some of the old industry giants believe?

PLM combined with ALM concepts are the ones to follow, and I am looking forward to meeting the first company that has implemented a consistent flow between the world of hardware and software. So far there are many slide solutions, the reality and legacy at this moment are still inhibitors for the next step.


There is still a lot to discover and execute in the domain of PLM. Moving to a data-driven enterprise with all stakeholders connected is the challenging journey. Can we build robust concepts taking accuracy, security, and speed into account? I believe so, in particular when dreaming at the beach.


Bye for now


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