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My previous post introducing the concept of connected platforms created some positive feedback and some interesting questions. For example, the question from Maxime Gravel:
Thank you, Jos, for the great blog. Where do you see Change Management tool fit in this new Platform ecosystem?
is one of the questions I try to understand too. You can see my short comment in the comments here. However, while discussing with other experts in the CM-domain, we should paint the path forward. Because if we cannot solve this type of question, the value of connected platforms will be disputable.
It is essential to realize that a digital transformation in the PLM domain is challenging. No company or vendor has the perfect blueprint available to provide an end-to-end answer for a connected enterprise. In addition, I assume it will take 10 – 20 years till we will be familiar with the concepts.
It takes a generation to move from drawings to 3D CAD. It will take another generation to move from a document-driven, linear process to data-driven, real-time collaboration in an iterative manner. Perhaps we can move faster, as the Automotive, Aerospace & Defense, and Industrial Equipment industries are not the most innovative industries at this time. Other industries or startups might lead us faster into the future.
Although I prefer discussing methodology, I believe before moving into that area, I need to clarify some more technical points before moving forward. My apologies for writing it in such a simple manner. This information should be accessible for the majority of readers.
What means data-driven?
I often mention a data-driven environment, but what do I mean precisely by that. For me, a data-driven environment means that all information is stored in a dataset that contains a single aspect of information in a standardized manner, so it becomes accessible by outside tools.
A document is not a dataset, as often it includes a collection of datasets. Most of the time, the information it is exposed to is not standardized in such a manner a tool can read and interpret the exact content. We will see that a dataset needs an identifier, a classification, and a status.
An identifier to be able to create a connection between other datasets – traceability or, in modern words, a digital thread.
A classification as the classification identifier will determine the type of information the dataset contains and potential a set of mandatory attributes
A status to understand if the dataset is stable or still in work.
Examples of a data-driven approach – the item
The most common dataset in the PLM world is probably the item (or part) in a Bill of Material. The identifier is the item number (ID + revision if revisions are used). Next, the classification will tell you the type of part it is.
Part classification can be a topic on its own, and every industry has its taxonomy.
Finally, the status is used to identify if the dataset is shareable in the context of other information (released, in work, obsolete), allowing tools to expose only relevant information.
In a data-driven manner, a part can occur in several Bill of Materials – an example of a single definition consumed in other places.
When the part information changes, the accountable person has to analyze the relations to the part, which is easy in a data-driven environment. It is normal to find this functionality in a PDM or ERP system.
When the part would change in a document-driven environment, the effort is much higher.
First, all documents need to be identified where this part occurs. Then the impact of change needs to be managed in document versions, which will lead to other related changes if you want to keep the information correct.
Examples of a data-driven approach – the requirement
Another example illustrating the benefits of a data-driven approach is implementing requirements management, where requirements become individual datasets. Often a product specification can contain hundreds of requirements, addressing the needs of different stakeholders.
In addition, several combinations of requirements need to be handled by other disciplines, mechanical, electrical, software, quality and legal, for example.
As requirements need to be analyzed and ranked, a specification document would never be frozen. Trade-off analysis might lead to dropping or changing a single requirement. It is almost impossible to manage this all in a document, although many companies use Excel. The disadvantages of Excel are known, in particular in a dynamic environment.
The advantage of managing requirements as datasets is that they can be grouped. So, for example, they can be pushed to a supplier (as a specification).
Or requirements could be linked to test criteria and test cases, without the need to manage documents and make sure you work with them last updated document.
As you will see, also requirements need to have an Identifier (to manage digital relations), a classification (to allow grouping) and a status (in work / released /dropped)
Data-driven and Models – the 3D CAD model
3D PDF Model
When I launched my series related to the model-based approach in 2018, the first comments I got came from people who believed that model-based equals the usage of 3D CAD models – see Model-based – the confusion. 3D Models are indeed an essential part of a model-based infrastructure, as the 3D model provides an unambiguous definition of the physical product. Just look at how most vendors depict the aspects of a virtual product using 3D (wireframe) models.
Although we use a 3D representation at each product lifecycle stage, most companies do not have a digital continuity for the 3D representation. Design models are often too heavy for visualization and field services support. The connection between engineering and manufacturing is usually based on drawings instead of annotated models.
I wrote about modern PLM and Model-Based Definition, supported by Jennifer Herron from Action Engineering – read the post PLM and Model-Based Definition here.
If your company wants to master a data-driven approach, this is one of the most accessible learning areas. You will discover that connecting engineering and manufacturing requires new technology, new ways of working and much more coordination between stakeholders.
Implementing Model-Based Definition is not an easy process. However, it is probably one of the best steps to get your digital transformation moving. The benefits of connected information between engineering and manufacturing have been discussed in the blog post PLM and Model-Based Definition
Essential to realize all these exciting capabilities linked to Industry 4.0 require a data-driven, model-based connection between engineering and manufacturing.
If this is not the case, the projected game-changers will not occur as they become too costly.
Data-driven and mathematical models
To manage complexity, we have learned that we have to describe the behavior in models to make logical decisions. This can be done in an abstract model, purely based on mathematical equations and relations. For example, suppose you look at climate models, weather models or COVID infections models.
In that case, we see they all lead to discussions from so-called experts that believe a model should be 100 % correct and any exception shows the model is wrong.
It is not that the model is wrong; the expectations are false.
For less complex systems and products, we also use models in the engineering domain. For example, logical models and behavior models are all descriptive models that allow people to analyze the behavior of a product.
For example, how software code impacts the product’s behavior. Usually, we speak about systems when software is involved, as the software will interact with the outside world.
There can be many models related to a product, and if you want to get an impression, look at this page from the SEBoK wiki: Types of Models. The current challenge is to keep the relations between these models by sharing parameters.
The sharable parameters then again should be datasets in a data-driven environment. Using standardized diagrams, like SysML or UML, enables the used objects in the diagram to become datasets.
I will not dive further into the modeling details as I want to remain at a high level.
Essential to realize digital models should connect to a data-driven infrastructure by sharing relevant datasets.
What does data-driven imply?
I want to conclude this time with some statements to elaborate on further in upcoming posts and discussions
- Data-driven does not imply there needs to be a single environment, a single database that contains all information. Like I mentioned in my previous post, it will be about managing connected datasets in a federated manner. It is not anymore about owned the data; it is about access to reliable data.
- Data-driven does not mean we do not need any documents anymore. Read electronic files for documents. Likely, document sets will still be the interface to non-connected entities, suppliers, and regulatory bodies. These document sets can be considered a configuration baseline.
- Data-driven means that we need to manage data in a much more granular manner. We have to look different at data ownership. It becomes more data accountability per role as the data can be used and consumed throughout the product lifecycle.
- Data-driven means that you need to have an enterprise architecture, data governance and a master data management (MDM) approach. So far, the traditional PLM vendors have not been active in the MDM domain as they believe their proprietary data model is leading. Read also this interesting McKinsey article: How enterprise architects need to evolve to survive in a digital world
- A model-based approach with connected datasets seems to be the way forward. Managing data in documents will become inefficient as they cannot contribute to any digital accelerator, like applying algorithms. Artificial Intelligence relies on direct access to qualified data.
- I don’t believe in Low-Code platforms that provide ad-hoc solutions on demand. The ultimate result after several years might be again a new type of spaghetti. On the other hand, standardized interfaces and protocols will probably deliver higher, long-term benefits. Remember: Low code: A promising trend or a Pandora’s Box?
- Configuration Management requires a new approach. The current methodology is very much based on hardware products with labor-intensive change management. However, the world of software products has different configuration management and change procedure. Therefore, we need to merge them in a single framework. Unfortunately, this cannot be the BOM framework due to the dynamics in software changes. An interesting starting point for discussion can be found here: Configuration management of industrial products in PDM/PLM
Conclusion
Again, a long post, slowly moving into the future with many questions and points to discuss. Each of the seven points above could be a topic for another blog post, a further discussion and debate.
After my summer holiday break in August, I will follow up. I hope you will join me in this journey by commenting and contributing with your experiences and knowledge.
So far, I have been discussing PLM experiences and best practices that have changed due to introducing electronic drawings and affordable 3D CAD systems for the mainstream. From vellum to PDM to item-centric PLM to manage product designs and manufacturing specifications.
Although the technology has improved, the overall processes haven’t changed so much. As a result, disciplines could continue to work in their own comfort zone, most of the time hidden and disconnected from the outside world.
Now, thanks to digitalization, we can connect and format information in real-time. Now we can provide every stakeholder in the company’s business to have almost real-time visibility on what is happening (if allowed). We have seen the benefits of platformization, where the benefits come from real-time connectivity within an ecosystem.
Apple, Amazon, Uber, Airbnb are the non-manufacturing related examples. Companies are trying to replicate these models for other businesses, connecting the concept owner (OEM ?), with design and manufacturing (services), with suppliers and customers. All connected through information, managed in data elements instead of documents – I call it connected PLM
Vendors have already shared their PowerPoints, movies, and demos from how the future would be in the ideal world using their software. The reality, however, is that implementing such solutions requires new business models, a new type of organization and probably new skills.
The last point is vital, as in schools and organizations, we tend to teach what we know from the past as this gives some (fake) feeling of security.
The reality is that most of us will have to go through a learning path, where skills from the past might become obsolete; however, knowledge of the past might be fundamental.
In the upcoming posts, I will share with you what I see, what I deduct from that and what I think would be the next step to learn.
I firmly believe connected PLM requires the usage of various models. Not only the 3D CAD model, as there are so many other models needed to describe and analyze the behavior of a product.
I hope that some of my readers can help us all further on the path of connected PLM (with a model-based approach). This series of posts will be based on the max size per post (avg 1500 words) and the ideas and contributes coming from you and me.
What is platformization?
In our day-to-day life, we are more and more used to direct interaction between resellers and services providers on one side and consumers on the other side. We have a question, and within 24 hours, there is an answer. We want to purchase something, and potentially the next day the goods are delivered. These are examples of a society where all stakeholders are connected in a data-driven manner.
We don’t have to create documents or specialized forms. An app or a digital interface allows us to connect. To enable this type of connectivity, there is a need for an underlying platform that connects all stakeholders. Amazon and Salesforce are examples for commercial activities, Facebook for social activities and, in theory, LinkedIn for professional job activities.
The platform is responsible for direct communication between all stakeholders.
The same applies to businesses. Depending on the products or services they deliver, they could benefit from one or more platforms. The image below shows five potential platforms that I identified in my customer engagements. Of course, they have a PLM focus (in the middle), and the grouping can be made differently.
Five potential business platforms
The 5 potential platforms
The ERP platform
is mainly dedicated to the company’s execution processes – Human Resources, Purchasing, Finance, Production scheduling, and potentially many more services. As platforms try to connect as much as possible all stakeholders. The ERP platform might contain CRM capabilities, which might be sufficient for several companies. However, when the CRM activities become more advanced, it would be better to connect the ERP platform to a CRM platform. The same logic is valid for a Product Innovation Platform and an ERP platform. Examples of ERP platforms are SAP and Oracle (and they will claim they are more than ERP)
Note: Historically, most companies started with an ERP system, which is not the same as an ERP platform. A platform is scalable; you can add more apps without having to install a new system. In a platform, all stored data is connected and has a shared data model.
The CRM platform
a platform that is mainly focusing on customer-related activities, and as you can see from the diagram, there is an overlap with capabilities from the other platforms. So again, depending on your core business and products, you might use these capabilities or connect to other platforms. Examples of CRM platforms are Salesforce and Pega, providing a platform to further extend capabilities related to core CRM.
The MES platform
In the past, we had PDM and ERP and what happened in detail on the shop floor was a black box for these systems. MES platforms have become more and more important as companies need to trace and guide individual production orders in a data-driven manner. Manufacturing Execution Systems (and platforms) have their own data model. However, they require input from other platforms and will provide specific information to other platforms.
For example, if we want to know the serial number of a product and the exact production details of this product (used parts, quality status), we would use an MES platform. Examples of MES platforms (none PLM/ERP related vendors) are Parsec and Critical Manufacturing
The IoT platform
these platforms are new and are used to monitor and manage connected products. For example, if you want to trace the individual behavior of a product of a process, you need an IoT platform. The IoT platform provides the product user with performance insights and alerts.
However, it also provides the product manufacturer with the same insights for all their products. This allows the manufacturer to offer predictive maintenance or optimization services based on the experience of a large number of similar products. Examples of IoT platforms (none PLM/ERP-related vendors) are Hitachi and Microsoft.
The Product Innovation Platform (PIP)
All the above platforms would not have a reason to exist if there was not an environment where products were invented, developed, and managed. The Product Innovation Platform PIP – as described by CIMdata -is the place where Intellectual Property (IP) is created, where companies decide on their portfolio and more.
The PIP contains the traditional PLM domain. It is also a logical place to manage product quality and technical portfolio decisions, like what kind of product platforms and modules a company will develop. Like all previous platforms, the PIP cannot exist without other platforms and requires connectivity with the other platforms is applicable.
Look below at the CIMdata definition of a Product Innovation Platform.
You will see that most of the historical PLM vendors aiming to be a PIP (with their different flavors): Aras, Dassault Systèmes, PTC and Siemens.
Of course, several vendors sell more than one platform or even create the impression that everything is connected as a single platform. Usually, this is not the case, as each platform has its specific data model and combining them in a single platform would hurt the overall performance.
Therefore, the interaction between these platforms will be based on standardized interfaces or ad-hoc connections.
Standard interfaces or ad-hoc connections?
Suppose your role and information needs can be satisfied within a single platform. In that case, most likely, the platform will provide you with the right environment to see and manipulate the information.
However, it might be different if your role requires access to information from other platforms. For example, it could be as simple as an engineer analyzing a product change who needs to know the actual stock of materials to decide how and when to implement a change.
This would be a PIP/ERP platform collaboration scenario.
Or even more complex, it might be a product manager wanting to know how individual products behave in the field to decide on enhancements and new features. This could be a PIP, CRM, IoT and MES collaboration scenario if traceability of serial numbers is needed.
The company might decide to build a custom app or dashboard for this role to support such a role. Combining in real-time data from the relevant platforms, using standard interfaces (preferred) or using API’s, web services, REST services, microservices (for specialists) and currently in fashion Low-Code development platforms, which allow users to combine data services from different platforms without being an expert in coding.
Without going too much in technology, the topics in this paragraph require an enterprise architecture and vision. It is opportunistic to think that your existing environment will evolve smoothly into a digital highway for the future by “fixing” demands per user. Your infrastructure is much more likely to end up congested as spaghetti.
In that context, I read last week an interesting post Low code: A promising trend or Pandora’s box. Have a look and decide for yourself
I am less focused on technology, more on methodology. Therefore, I want to come back to the theme of my series: The road to model-based and connected PLM. For sure, in the ideal world, the platforms I mentioned, or other platforms that run across these five platforms, are cloud-based and open to connect to other data sources. So, this is the infrastructure discussion.
In my upcoming blog post, I will explain why platforms require a model-based approach and, therefore, cause a challenge, particularly in the PLM domain.
It took us more than fifty years to get rid of vellum drawings. It took us more than twenty years to introduce 3D CAD for design and engineering. Still primarily relying on drawings. It will take us for sure one generation to switch from document-based engineering to model-based engineering.
Conclusion
In this post, I tried to paint a picture of the ideal future based on connected platforms. Such an environment is needed if we want to be highly efficient in designing, delivering, and maintaining future complex products based on hardware and software. Concepts like Digital Twin and Industry 4.0 require a model-based foundation.
In addition, we will need Digital Twins to reach our future sustainability goals efficiently. So, there is work to do.
Your opinion, Your contribution?
Another episode of “The PLM Doctor is IN“. This time a question from Ilan Madjar, partner and co-founder of XLM Solutions. Ilan is my co-moderator at the PLM Global Green Alliance for sustainability topics.
All these activities resulted in the following question(s) related to the Digital Twin. Now sit back and enjoy.
PLM and the Digital Twin
Is it a new concept? How to implement and certify the result?
Relevant topics discussed in this video
- An article about the introduction of the Digital Twin concept by Michael Grieves: The history and creation of the digital twin concept discussing Michael Grieves
- A presentation from Don Farr explaining the digital value chain with the “famous” Boeing diamond model – PDF HERE
- My Slideshare presentation discussing the difference between Coordinated and Connected PLM
- My Slideshare presentation from the Digital Twin conference Eindhoven Nov 2020: Digital Twins do not run on documents
Conclusion
I hope you enjoyed the answer and look forward to your questions and comments. Let me know if you want to be an actor in one of the episodes.
The main rule: A (single) open question that is puzzling you related to PLM.
After the first article discussing “The Future of PLM,” now again a post in the category of PLM and complementary practices/domains a topic that is already for a long time on the radar: Model-Based Definition, I am glad to catch up with Jennifer Herron, founder of Action Engineering, who is one of the thought leaders related to Model-Based Definition (MBD) and Model-Based Enterprise (MBE).
In 2016 I spoke with Jennifer after reading her book: “Re-Use Your CAD – The Model-Based CAD Handbook”. At that time, the discussion was initiated through two articles on Engineering.com. Action Engineering introduced OSCAR seven years later as the next step towards learning and understanding the benefits of Model-Based Definition.
Therefore, it is a perfect moment to catch up with Jennifer. Let’s start.
Model-Based Definition
Jennifer, first of all, can you bring some clarity in terminology. When I discussed the various model-based approaches, the first response I got was that model-based is all about 3D Models and that a lot of the TLA’s are just marketing terminology.
Can you clarify which parts of the model-based enterprise you focus on and with the proper TLA’s?
Model-Based means many things to many different viewpoints and systems of interest. All these perspectives lead us down many rabbit holes, and we are often left confused when first exposed to the big concepts of model-based.
At Action Engineering, we focus on Model-Based Definition (MBD), which uses and re-uses 3D data (CAD models) in design, fabrication, and inspection.
There are other model-based approaches, and the use of the word “model” is always a challenge to define within the proper context.
For MBD, a model is 3D CAD data that comes in both native and neutral formats
Another model-based approach is Model-Based Systems Engineering (MBSE). The term “model” in this context is a formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later lifecycle phases.
<Jos> I will come back on Model-Based Systems Engineering in future posts
Sometimes MBSE is about designing widgets, and often it is about representing the entire system and the business operations. For MBD, we often focus our education on the ASME Y14.47 definition that MBD is an annotated model and associated data elements that define the product without a drawing.
Model-Based Definition for Everybody?
I believe it took many years till 3D CAD design became a commodity; however, I still see the disconnected 2D drawing used to specify a product or part for manufacturing or suppliers. What are the benefits of model-based definition?
Are there companies that will not benefit from the model-based definition?
There’s no question that the manufacturing industry is addicted to their drawings. There are many reasons why, and yet mostly the problem is lack of awareness of how 3D CAD data can make design, fabrication, and inspection work easier.
For most, the person doing an inspection in the shipping and receiving department doesn’t have exposure to 3D data, and the only thing they have is a tabulated ERP database and maybe a drawing to read. If you plop down a 3D viewable that they can spin and zoom, they may not know how that relates to their job or what you want them to do differently.
Today’s approach of engineering championing MBD alone doesn’t work. To evolve information from the 2D drawing onto the 3D CAD model without engaging the stakeholders (machinists, assembly technicians, and inspectors) never yields a return on investment.
Organizations that succeed in transitioning to MBD are considering and incorporating all departments that touch the drawing today.
Incorporating all departments requires a vision from the management. Can you give some examples of companies that have transitioned to MBD, and what were the benefits they noticed?
I’ll give you an example of a small company with no First Article Inspection (FAI) regulatory requirements and a huge company with very rigorous FAI requirements.
Note: click on the images below to enjoy the details.
The small company instituted a system of CAD modeling discipline that allowed them to push 3D viewable information directly to the factory floor. The assembly technicians instantly understood engineering’s requirements faster and better.
The positive MBD messages for these use cases are 3D navigation, CAD Re-Use, and better control of their revisions on the factory floor.
The large company has added inspection requirements directly onto their engineering and created a Bill of Characteristics (BOC) for the suppliers and internal manufacturers. They are removing engineering ambiguity, resulting in direct digital information exchange between engineering, manufacturing, and quality siloes.
These practices have reduced error and reduced time to market.
The positive MBD messages for these use cases are unambiguous requirements capture by Engineering, Quality Traceability, and Model-Based PMI (Product and Manufacturing Information).
Model-Based Definition and PLM?
How do you see the relation between Model-Based Definition and PLM? Is a PLM system a complication or aid to implement a Model-Based Definition? And do you see a difference between the old and new PLM Vendors?
Model-Based Definition data is complex and rich in connected information, and we want it to be. With that amount of connected data, a data management system (beyond upload/download of documents) must keep all that data straight.
Depending on the size and function of an organization, a PLM may not be needed. However, a way to manage changes and collaboration amongst those using 3D data is necessary. Sometimes that results in a less sophisticated Product Data Management (PDM) system. Large organizations often require PLM.
There is significant resistance to doing MBD and PLM implementations simultaneously because PLM is always over budget and behind schedule. However, doing just MBD or just PLM without the other doesn’t work either. I think you should be brave and do both at once.
I think we can debate why PLM is always over budget and behind schedule. I hear the same about ERP implementations. Perhaps it has to deal with the fact that enterprise applications have to satisfy many users?
I believe that working with model versions and file versions can get mixed in larger organizations, so there is a need for PDM or PLM. Have you seen successful implementations of both interacting together?
Yes, the only successful MBD implementations are those that already have a matured PDM/PLM (scaled best to the individual business).
Model-Based Definition and Digital Transformation
In the previous question, we already touched on the challenge of old and modern PLM. How do you see the introduction of Model-Based Definition addressing the dreams of Industry 4.0, the Digital Twin and other digital concepts?
I just gave a presentation at the ASME Digital Twin Summit discussing the importance of MBD for the Digital Twin. MBD is a foundational element that allows engineering to compare their design requirements to the quality inspection results of digital twin data.
The feedback loop between Engineering and Quality is fraught with labor-intensive efforts in most businesses today.
Leveraging the combination of MBD and Digital Twin allows automation possibilities to speed up and increase the accuracy of the engineering to inspection feedback loop. That capability helps organizations realize the vision of Industry 4.0.
And then there is OSCAR.
I noticed you announced OSCAR. First, I thought OSCAR was a virtual aid for model-based definition, and I liked the launching page HERE. Can you tell us more about what makes OSCAR unique?
One thing that is hard with MBD implementation is there is so much to know. Our MBDers at Action Engineering have been involved with MBD for many years and with many companies. We are embedded in real-life transitions from using drawings to using models.
Suppose you start down the model-based path for digital manufacturing. In that case, there are significant investments in time to learn how to get to the right set of capabilities and the right implementation plan guided by a strategic focus. OSCAR reduces that ramp-up time with educational resources and provides vetted and repeatable methods for an MBD implementation.
OSCAR combines decades of Action Engineering expertise and lessons learned into a multi-media textbook of sorts. To kickstart an individual or an organization’s MBD journey, it includes asynchronous learning, downloadable resources, and CAD examples available in Creo, NX, and SOLIDWORKS formats.
CAD users can access how-to training and downloadable resources such as the latest edition of Re-Use Your CAD (RUYC). OSCAR enables process improvement champions to make their case to start the MBD journey. We add content regularly and post what’s new. Free trials are available to check out the online platform.
Learn more about what OSCAR is here:
Want to learn more?
In this post, I believe we only touched the tip of the iceberg. There is so much to learn and understand. What would you recommend to a reader of this blog who got interested?
RUYC (Re-Use Your CAD) is an excellent place to start, but if you need more audio-visual, and want to see real-life examples of MBD in action, get a Training subscription of OSCAR to get rooted in the vocabulary and benefits of MBD with a Model-Based Enterprise. Watch the videos multiple times! That’s what they are for. We love to work with European companies and would love to support you with a kickstart coaching package to get started.
What I learned
First of all, I learned that Jennifer is a very pragmatic person. Her company (Action Engineering) and her experience are a perfect pivot point for those who want to learn and understand more about Model-Based Definition. In particular, in the US, given her strong involvement in the American Society of Mechanical Engineers (ASME).
I am still curious if European or Asian counterparts exist to introduce and explain the benefits and usage of Model-Based Definition to their customers. Feel free to comment.
Next, and an important observation too, is the fact that Jennifer also describes the tension between Model-Based Definition and PLM. Current PLM systems might be too rigid to support end-to-end scenarios, taking benefit of the Model-Based definition.
I have to agree here. PLM Vendors mainly support their own MBD (model-based definition), where the ultimate purpose is to share all product-related information using various models as the main information carriers efficiently.
We have to study and solve a topic in the PLM domain, as I described in my technical highlights from the PLM Road Map & PDT Spring 2021 conference.
There is work to do!
Conclusion
Model-Based Definition is, for me, one of the must-do steps of a company to understand the model-based future. A model-based future sometimes incorporates Model-Based Systems Engineering, a real Digital Thread and one or more Digital Twins (depending on your company’s products).
It is a must-do activity because companies must transform themselves to depend on digital processes and digital continuity of data to remain competitive. Document-driven processes relying on the interpretation of a person are not sustainable.
After the first article discussing “The Future of PLM,” now again a post in the category of PLM and complementary practices/domains a topic that is already for a long time on the radar: Model-Based Definition, I am glad to catch up with Jennifer Herron, founder of Action Engineering, who is one of the thought leaders related to Model-Based Definition (MBD) and Model-Based Enterprise (MBE).
In 2016 I spoke with Jennifer after reading her book: “Re-Use Your CAD – The Model-Based CAD Handbook”. At that time, the discussion was initiated through two articles on Engineering.com. Action Engineering introduced OSCAR seven years later as the next step towards learning and understanding the benefits of Model-Based Definition.
Therefore, it is a perfect moment to catch up with Jennifer. Let’s start.
Model-Based Definition
Jennifer, first of all, can you bring some clarity in terminology. When I discussed the various model-based approaches, the first response I got was that model-based is all about 3D Models and that a lot of the TLA’s are just marketing terminology.
Can you clarify which parts of the model-based enterprise you focus on and with the proper TLA’s?
Model-Based means many things to many different viewpoints and systems of interest. All these perspectives lead us down many rabbit holes, and we are often left confused when first exposed to the big concepts of model-based.
At Action Engineering, we focus on Model-Based Definition (MBD), which uses and re-uses 3D data (CAD models) in design, fabrication, and inspection.
There are other model-based approaches, and the use of the word “model” is always a challenge to define within the proper context.
For MBD, a model is 3D CAD data that comes in both native and neutral formats
Another model-based approach is Model-Based Systems Engineering (MBSE). The term “model” in this context is a formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later lifecycle phases.
<Jos> I will come back on Model-Based Systems Engineering in future posts
Sometimes MBSE is about designing widgets, and often it is about representing the entire system and the business operations. For MBD, we often focus our education on the ASME Y14.47 definition that MBD is an annotated model and associated data elements that define the product without a drawing.
Model-Based Definition for Everybody?
I believe it took many years till 3D CAD design became a commodity; however, I still see the disconnected 2D drawing used to specify a product or part for manufacturing or suppliers. What are the benefits of model-based definition?
Are there companies that will not benefit from the model-based definition?
There’s no question that the manufacturing industry is addicted to their drawings. There are many reasons why, and yet mostly the problem is lack of awareness of how 3D CAD data can make design, fabrication, and inspection work easier.
For most, the person doing an inspection in the shipping and receiving department doesn’t have exposure to 3D data, and the only thing they have is a tabulated ERP database and maybe a drawing to read. If you plop down a 3D viewable that they can spin and zoom, they may not know how that relates to their job or what you want them to do differently.
Today’s approach of engineering championing MBD alone doesn’t work. To evolve information from the 2D drawing onto the 3D CAD model without engaging the stakeholders (machinists, assembly technicians, and inspectors) never yields a return on investment.
Organizations that succeed in transitioning to MBD are considering and incorporating all departments that touch the drawing today.
Incorporating all departments requires a vision from the management. Can you give some examples of companies that have transitioned to MBD, and what were the benefits they noticed?
I’ll give you an example of a small company with no First Article Inspection (FAI) regulatory requirements and a huge company with very rigorous FAI requirements.
Note: click on the images below to enjoy the details.
The small company instituted a system of CAD modeling discipline that allowed them to push 3D viewable information directly to the factory floor. The assembly technicians instantly understood engineering’s requirements faster and better.
The positive MBD messages for these use cases are 3D navigation, CAD Re-Use, and better control of their revisions on the factory floor.
The large company has added inspection requirements directly onto their engineering and created a Bill of Characteristics (BOC) for the suppliers and internal manufacturers. They are removing engineering ambiguity, resulting in direct digital information exchange between engineering, manufacturing, and quality siloes.
These practices have reduced error and reduced time to market.
The positive MBD messages for these use cases are unambiguous requirements capture by Engineering, Quality Traceability, and Model-Based PMI (Product and Manufacturing Information).
Model-Based Definition and PLM?
How do you see the relation between Model-Based Definition and PLM? Is a PLM system a complication or aid to implement a Model-Based Definition? And do you see a difference between the old and new PLM Vendors?
Model-Based Definition data is complex and rich in connected information, and we want it to be. With that amount of connected data, a data management system (beyond upload/download of documents) must keep all that data straight.
Depending on the size and function of an organization, a PLM may not be needed. However, a way to manage changes and collaboration amongst those using 3D data is necessary. Sometimes that results in a less sophisticated Product Data Management (PDM) system. Large organizations often require PLM.
There is significant resistance to doing MBD and PLM implementations simultaneously because PLM is always over budget and behind schedule. However, doing just MBD or just PLM without the other doesn’t work either. I think you should be brave and do both at once.
I think we can debate why PLM is always over budget and behind schedule. I hear the same about ERP implementations. Perhaps it has to deal with the fact that enterprise applications have to satisfy many users?
I believe that working with model versions and file versions can get mixed in larger organizations, so there is a need for PDM or PLM. Have you seen successful implementations of both interacting together?
Yes, the only successful MBD implementations are those that already have a matured PDM/PLM (scaled best to the individual business).
Model-Based Definition and Digital Transformation
In the previous question, we already touched on the challenge of old and modern PLM. How do you see the introduction of Model-Based Definition addressing the dreams of Industry 4.0, the Digital Twin and other digital concepts?
I just gave a presentation at the ASME Digital Twin Summit discussing the importance of MBD for the Digital Twin. MBD is a foundational element that allows engineering to compare their design requirements to the quality inspection results of digital twin data.
The feedback loop between Engineering and Quality is fraught with labor-intensive efforts in most businesses today.
Leveraging the combination of MBD and Digital Twin allows automation possibilities to speed up and increase the accuracy of the engineering to inspection feedback loop. That capability helps organizations realize the vision of Industry 4.0.
And then there is OSCAR.
I noticed you announced OSCAR. First, I thought OSCAR was a virtual aid for model-based definition, and I liked the launching page HERE. Can you tell us more about what makes OSCAR unique?
One thing that is hard with MBD implementation is there is so much to know. Our MBDers at Action Engineering have been involved with MBD for many years and with many companies. We are embedded in real-life transitions from using drawings to using models.
Suppose you start down the model-based path for digital manufacturing. In that case, there are significant investments in time to learn how to get to the right set of capabilities and the right implementation plan guided by a strategic focus. OSCAR reduces that ramp-up time with educational resources and provides vetted and repeatable methods for an MBD implementation.
OSCAR combines decades of Action Engineering expertise and lessons learned into a multi-media textbook of sorts. To kickstart an individual or an organization’s MBD journey, it includes asynchronous learning, downloadable resources, and CAD examples available in Creo, NX, and SOLIDWORKS formats.
CAD users can access how-to training and downloadable resources such as the latest edition of Re-Use Your CAD (RUYC). OSCAR enables process improvement champions to make their case to start the MBD journey. We add content regularly and post what’s new. Free trials are available to check out the online platform.
Learn more about what OSCAR is here:
Want to learn more?
In this post, I believe we only touched the tip of the iceberg. There is so much to learn and understand. What would you recommend to a reader of this blog who got interested?
RUYC (Re-Use Your CAD) is an excellent place to start, but if you need more audio-visual, and want to see real-life examples of MBD in action, get a Training subscription of OSCAR to get rooted in the vocabulary and benefits of MBD with a Model-Based Enterprise. Watch the videos multiple times! That’s what they are for. We love to work with European companies and would love to support you with a kickstart coaching package to get started.
What I learned
First of all, I learned that Jennifer is a very pragmatic person. Her company (Action Engineering) and her experience are a perfect pivot point for those who want to learn and understand more about Model-Based Definition. In particular, in the US, given her strong involvement in the American Society of Mechanical Engineers (ASME).
I am still curious if European or Asian counterparts exist to introduce and explain the benefits and usage of Model-Based Definition to their customers. Feel free to comment.
Next, and an important observation too, is the fact that Jennifer also describes the tension between Model-Based Definition and PLM. Current PLM systems might be too rigid to support end-to-end scenarios, taking benefit of the Model-Based definition.
I have to agree here. PLM Vendors mainly support their own MBD (model-based definition), where the ultimate purpose is to share all product-related information using various models as the main information carriers efficiently.
We have to study and solve a topic in the PLM domain, as I described in my technical highlights from the PLM Road Map & PDT Spring 2021 conference.
There is work to do!
Conclusion
Model-Based Definition is, for me, one of the must-do steps of a company to understand the model-based future. A model-based future sometimes incorporates Model-Based Systems Engineering, a real Digital Thread and one or more Digital Twins (depending on your company’s products).
It is a must-do activity because companies must transform themselves to depend on digital processes and digital continuity of data to remain competitive. Document-driven processes relying on the interpretation of a person are not sustainable.
Last week I wrote about the recent PLM Road Map & PDT Spring 2021 conference day 1, focusing mainly on technology. There were also interesting sessions related to exploring future methodologies for a digital enterprise. Now on Day 2, we started with two sessions related to people and methodology, indispensable when discussing PLM topics.
Designing and Keeping Great Teams
This keynote speech from Noshir Contractor, Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, intrigued me as the subtitle states: Lessons from Preparing for Mars. What Can PLM Professionals Learn from This?
You might ask yourself, is a PLM implementation as difficult and as complex as a mission to Mars? I hoped, so I followed with great interest Noshir’s presentation.
Noshir started by mentioning that many disruptive technologies have emerged in recent years, like Teams, Slack, Yammer and many more.
The interesting question he asked in the context of PLM is:
As the domain of PLM is all about trying to optimize effective collaboration, this is a fair question
Structural Signatures
Noshir shared with us that it is not the most crucial point to look at people’s individual skills but more about who they know.
Measure who they work with is more important than who they are.
Based on this statement, Noshir showed some network patterns of different types of networks.
Click on the image to see the enlarged picture.
It is clear from these patterns how organizations communicate internally and/or externally. It would be an interesting exercise to perform in a company and to see if the analysis matches the perceived reality.
Noshir’s research was used by NASA to analyze and predict the right teams for a mission to Mars.
Noshir went further by proposing what PLM can learn from teams that are going into space. And here, I was not sure about the parallel. Is a PLM project comparable to a mission to Mars? I hope not! I have always advocated that a PLM implementation is a journey. Still, I never imagined that it could be a journey into the remote unknown.
Noshir explained that they had built tools based on their scientific model to describe and predict how teams could evolve over time. He believes that society can also benefit from these learnings. Many inventions from the past were driven by innovations coming from space programs.
I believe Noshir’s approach related to team analysis is much more critical for organizations with a mission. How do you build multidisciplinary teams?
The proposed methodology is probably best for a holocracy based organization. Holocrazy is an interesting concept for companies to get their employees committed, however, it also demands a type of involvement that not every person can deliver. For me, coming back to PLM, as a strategy to enable collaboration, the effectiveness of collaboration depends very much on the organizational culture and created structure.
DISRUPTION – EXTINCTION or still EVOLUTION?
We talk a lot about disruption because disruption is a painful process that you do not like to happen to yourself or your company. In the context of this conference’s theme, I discussed the awareness that disruptive technologies will be changing the PLM Value equation.
A disruption like the switch from mini-computers to PCs (killed DEC) or from Symbian to iOS (killed Nokia) is therefore not likely to happen that fast. Still, there is a need to take benefit from these new disruptive technologies.
My presentation was focusing on describing the path of evolution and focus areas for the PLM community. Doing nothing means extinction; experimenting and learning towards the future will provide an evolutionary way.
Starting from acknowledging that there is an incompatibility between data produced most of the time now and the data needed in the future, I explained my theme: From Coordinated to Connected. As a PLM community, we should spend more time together in focus groups, conferences on describing and verifying methodology and best practices.
Nigel Shaw (EuroStep) and Mark Williams (Boeing) hinted in this direction during this conference (see day 1). Erik Herzog (SAAB Aeronautics) brought this topic to last year’s conference (see day 3). Outside this conference, I have comparable touchpoints with Martijn Dullaert when discussing Configuration Management in the future in relation to PLM.
In addition, this decade will probably be the most disruptive decade we have known in humanity due to external forces that push companies to change. Sustainability regulations from governments (the Paris agreement), the implementation of circular economy concepts combined with the positive and high Total Share Holder return will push companies to adapt themselves more radical than before.
What is clear is that disruptive technologies and concepts, like Industry 4.0, Digital Thread and Digital Twin, can serve a purpose when implemented efficiently, ensuring the business becomes sustainable.
Due to the lack of end-to-end experience, we need focus groups and conferences to share progress and lessons learned. And we do not need to hear the isolated vendor success stories here as a reference, as often they are siloed again and leading to proprietary environments.
You can see my full presentation on SlideShare: DISRUPTION – EXTINCTION or still EVOLUTION?
Building a profitable Digital T(win) business
Beatrice Gasser, Technical, Innovation, and Sustainable Development Director from the Egis group, gave an exciting presentation related to the vision and implementation of digital twins in the construction industry.
The Egis group both serves as a consultancy firm as well as an asset management organization. You can see a wide variety of activities on their website or have a look at their perspectives
Historically the construction industry has been lagging behind having low productivity due to fragmentation, risk aversion and recently, more and more due to the lack of digital talent. In addition, some of the construction companies make their money from claims inside of having a smooth and profitable business model.
Without innovation in the construction industry, companies working the traditional way would lose market share and investor-focused attention, as we can see from the BCG diagram I discussed in my session.
The digital twin of construction is an ideal concept for the future. It can be built in the design phase to align all stakeholders, validate and integrate solutions and simulate the building operational scenarios at almost zero materials cost. Egis estimates that by using a digital twin during construction, the engineering and construction costs of a building can be reduced between 15 and 25 %
More importantly, the digital twin can also be used to first simulate operations and optimize energy consumption. The connected digital twin of an existing building can serve as a new common data environment for future building stakeholders. This could be the asset owner, service companies, and even the regulatory authorities needing to validate the building’s safety and environmental impact.
Beatrice ended with five principles essential to establish a digital twin, i.e
I think the construction industry has a vast potential to disrupt itself. Faster than the traditional manufacturing industries due to their current needs to work in a best-connected manner.
Next, there is almost no legacy data to deal with for these companies. Every new construction or building is a unique project on its own. The key differentiators will be experience and efficient ways of working.
It is about the belief, the guts and the skilled people that can make it work – all for a more efficient and sustainable future.
Leveraging PLM and Cloud Technology for Market Success
Stan Przybylinski, Vice President of CIMdata, reported their global survey related to the cloud, completed in early 2021. Also, Stan typified Industry 4.0 as a connected vision and cloud and digital thread as enablers to implementing this vision.
The companies interviewed showed a lot of goodwill to make progress – click on the image to see the details. CIMdata is also working with PLM Vendors to learn and describe better the areas of beneft. I remain curious about who comes with a realization and business case that is future-proof. This will define our new PLM Value Equation.
Conclusion
These were two exciting days with enough mentioning of disruptive technologies. Our challenge in the PLM domain will be to give them a purpose. A purpose is likely driven by external factors related to the need for a sustainable future. Efficiency and effectiveness must come from learning to work in connected environments (digital twin, digital thread, industry 4.0, Model-Based (Systems) Engineering.
Note: You might have seen the image below already – a nice link between sustainability and the mission to Mars
Last summer, I wrote a series of blog posts grouped by the theme “Learning from the past to understand the future”. These posts took you through the early days of drawings and numbering practices towards what we currently consider the best practice: PLM BOM-centric backbone for product lifecycle information.
You can find an overview and links to these posts on the Learning from the past page.
If you have read these posts, or if you have gone through this journey, you will realize that all steps were more or less done evolutionary. There were no disruptions. Affordable 3D CAD systems, new internet paradigms (interactive internet), global connectivity and mobile devices all introduced new capabilities for the mainstream. As described in these posts, the new capabilities sometimes create friction with old practices. Probably the most popular topics are the whole Form-Fit-Function interpretation and the discussion related to meaningful part numbers.
What is changing?
In the last five to ten years, a lot of new technology has come into our lives. The majority of these technologies are related to dealing with data. Digital transformation in the PLM domain means moving from a file-based/document-centric approach to a data-driven approach.
A Bill of Material on the drawing has become an Excel-like table in a PLM system. However, an Excel file is still used to represent a Bill of Material in companies that have not implemented PLM.
Another example is the specification document which has become a collection of individual requirements in a system. Each requirement is a data object with its own status and content. The specification becomes a report combining all valid requirement objects.
Related to CAD, the 2D drawing is no longer the deliverable as a document; the 3D CAD model with its annotated views becomes the information carrier for engineering and manufacturing.
Most importantly, traditional PLM methodologies have been based on a mechanical design and release process. Meanwhile, modern products are systems where the majority of capabilities are defined by software. Software has an entirely different configuration and lifecycle approach which conflict with a mechanical approach, which is too rigid for software.
The last two aspects, from 2D drawings to 3D Models and Mechanical products towards Systems (hardware and software), require new data management methods. In this environment, we need to learn to manage simulation models, behavior models, physics models and 3D models as connected as possible.
I wrote about these changes three years ago: Model-Based – an introduction, which led to a lot of misunderstanding (too advanced – too hypothetical).
I plan to revisit these topics in the upcoming months again to see what has changed over the past three years.
What will I discuss in the upcoming weeks?
My first focus is on participating and contributing to the upcoming PLM Roadmap & PDT spring 2021 conference. Here speakers will discuss the need for reshaping the PLM Value Equation due to new emerging technologies. A topic that contributes perfectly to the future of PLM series.
My contribution will focus on the fact that technology alone cannot disrupt the PLM domain. We also have to deal with legacy data and legacy ways of working.
Next, I will discuss with Jennifer Herron from Action Engineering the progress made in Model-Based Definition, which fits best practices for today – a better connection between engineering and manufacturing. We will also discuss why Model-Based Definition is a significant building block required for realizing the concepts of a digital enterprise, Industry 4.0 and digital twins.
Another post will focus on the difference between the digital thread and the digital thread. Yes, it looks like I am writing twice the same words. However, you will see based on its interpretation, one definition is hanging on the past, the other is targeting the future. Again here, the differentiation is crucial if the need for a maintainable Digital Twin is required.
Model-Based Systems Engineering (MBSE) in all its aspects needs to be discussed too. MBSE is crucial for defining complex products. Model-Based Systems Engineering is seen as a discipline to design products. Understanding data management related to MBSE will be the foundation for understanding data management in a Model-Based Enterprise. For example, how to deal with configuration management in the future?
Writing Learning from the past was an easy job as explaining with hindsight is so much easier if you have lived it through. I am curious and excited about the outcome of “The Future of PLM”. Writing about the future means you have digested the information coming to you, knowing that nobody has a clear blueprint for the future of PLM.
There are people and organizations are working on this topic more academically, for example read this post from Lionel Grealou related to the Place of PLM in the Digital Future. The challenge is that an academic future might be disrupted by unpredictable events, like COVID, or disruptive technologies combined with an opportunity to succeed. Therefore I believe, it will be a learning journey for all of us where we need to learn to give technology a business purpose. Business first – then technology.
No Conclusion
Normally I close my post with a conclusion. At this moment. there is no conclusion as the journey has just started. I look forward to debating and learning with practitioners in the field. Work together on methodology and concepts that work in a digital enterprise. Join me on this journey. I will start sharing my thoughts in the upcoming months
Another episode of “The PLM Doctor is IN“. This time a question from Rob Ferrone. Rob is one of the founders of QuickRelease, a passionate, no-nonsense PDM/PLM consultancy company focusing on process improvement.
Now sit back and enjoy.
PLM and Digital Plumbing
What’s inside the digital plumber’s toolbox?
Relevant topic discussed in this video
Inside this video you see a slide from Marc Halpern (Gartner), depicting the digital thread during the last PLM Roadmap – PDT conference – fall 2020. This conference is THE place for more serious content and I am happy to announce my participation and anxiety for the next upcoming PLM Roadmap – PDT conference on May 19-20.
The theme: DISRUPTION—the PLM Professionals’ Exploration of Emerging Technologies that Will Reshape the PLM Value Equation.
Looking forward to seeing you there.
Conclusion
I hope you enjoyed the answer and look forward to your questions and comments. Let me know if you want to be an actor in one of the episodes.
The main rule: A single open question that is puzzling you related to PLM.
This time in the series of complementary practices to PLM, I am happy to discuss product modularity. In my previous post related to Virtual Events, I mentioned I had finished reading the book “The Modular Way”, written by Björn Eriksson & Daniel Strandhammar, founders of the consulting company Brick Strategy.
The first time I got aware of Brick Strategy was precisely a year ago during the Technia Innovation Forum, the first virtual event I attended since COVID-19. Daniel’s presentation at that event was one of the four highlights that I shared about the conference. See My four picks from PLMIF.
As I wrote in my last post:
Modularity is a popular topic in many board meetings. How often have you heard: “We want to move from Engineering To Order (ETO) to more Configure To Order (CTO)”? Or another related incentive: “We need to be cleverer with our product offering and reduced the number of different parts”.
Next, the company buys a product that supports modularity, and management believes the work has been done. Of course, not. Modularity requires a thoughtful strategy.
I am now happy to have a dialogue with Daniel to learn and understand Brick Strategy’s view on PLM and Modularization. Are these topics connected? Can one live without the other? Stay tuned till the end if you still have questions for a pleasant surprise.
The Modular Way
Daniel, first of all, can you give us some background and intentions of the book “The Modular Way”?
Let me start by putting the book in perspective. In today’s globalized business, competition among industrial companies has become increasingly challenging with rapidly evolving technology, quickly changing customer behavior, and accelerated product lifecycles. Many companies struggle with low profitability.
To survive, companies need to master product customizations, launch great products quickly, and be cost-efficient – all at the same time. Modularization is a good solution for industrial companies with ambitions to improve their competitiveness significantly.
The aim of modularization is to create a module system. It is a collection of pre-defined modules with standardized interfaces. From this, you can build products to cater to individual customer needs while keeping costs low. The main difference from traditional product development is that you develop a set of building blocks or modules rather than specific products.
The Modular Way explains the concept of modularization and the ”how-to.” It is a comprehensive and practical guidebook, providing you with inspiration, a framework, and essential details to succeed with your journey. The book is based on our experience and insights from some of the world’s leading companies.
Björn and I have long thought about writing a book to share our combined modularization experience and learnings. Until recently, we have been fully busy supporting our client companies, but the halted activities during the peak of the COVID-19 pandemic gave us the perfect opportunity.
PLM and Modularity
Did you have PLM in mind when writing the book?
Yes, definitely. We believe that modularization and a modular way of working make product lifecycle management more efficient. Then we talk foremost about the processes, roles, product structure, decision making etc. Companies often need minor adjustments to their IT systems to support and sustain the new way of working.
Companies benefit the most from modularization when the contents, or foremost the products, are well structured for configuration in streamlined processes.
Many times, this means “thinking ahead” and preparing your products for more configuration and less engineering in the sales process, i.e., go from ETO to CTO.
Modularity for Everybody?
It seems like the modularity concept is prevalent in the Scandinavian countries, with famous examples of Scania, LEGO, IKEA, and Electrolux mentioned in your book. These examples come from different industries. Does it mean that all companies could pursue modularity, or are there some constraints?
We believe that companies designing and manufacturing products fulfilling different customer needs within a defined scope could benefit from modularization. Off-the-shelf content, commonality and reuse increase efficiency. However, the focus, approach and benefits are different among different types of companies.
We have, for example, seen low-volume companies expecting the same benefits as high-volume consumer companies. This is unfortunately not the case.
Companies can improve their ability and reduce the efforts to configure products to individual needs, i.e., customization. And when it comes to cost and efficiency improvements, high-volume companies can reduce product and operational costs.
Image:
Low-volume companies can shorten lead time and increase efficiency in R&D and product maintenance. Project solution companies can shorten the delivery time through reduced engineering efforts.
As an example, Electrolux managed to reduce part costs by 20 percent. Half of the reduction came from volume effects and the rest from design for manufacturing and assembly.
All in all, Electrolux has estimated its operating cost savings at approximately SEK 4bn per year with full effect, or around 3.5 percentage points of total costs, compared to doing nothing from 2010–2017. Note: SEK 4 bn is approximate Euro 400 Mio
Where to start?
Thanks to your answer, I understand my company will benefit from modularity. To whom should I talk in my company to get started? And if you would recommend an executive sponsor in my company, who would recommend leading this initiative.
Defining a modular system, and implementing a modular way of working, is a business-strategic undertaking. It is complex and has enterprise-wide implications that will affect most parts of the organization. Therefore, your management team needs to be aligned, engaged, and prioritize the initiative.
The implementation requires a cross-functional team to ensure that you do it from a market and value chain perspective. Modularization is not something that your engineering or IT organization can solve on its own.
We recommend that the CTO or CEO owns the initiative as it requires horizontal coordination and agreement.
Modularity and Digital Transformation
The experiences you are sharing started before digital transformation became a buzzword and practice in many companies. In particular, in the PLM domain, companies are still implementing past practices. Is modularization applicable for the current (coordinated) and for the (connected) future? And if yes, is there a difference?
Modularization means that your products have a uniform design based on common concepts and standardized interfaces. To the market, the end products are unique, and your processes are consistent. Thus, modularization plays a role independently of where you are on the digital transformation journey.
Digital transformation will continue for quite some time. Costs can be driven down even further through digitalization, enabling companies to address the connection of all value chain elements to streamline processes and accelerate speed to market. Digitalization will enhance the customer experience by connecting all relevant parts of the value chain and provide seamless interactions.
Industry 4.0 is an essential part of digitalization, and many companies are planning further investments. However, before considering investing in robotics and digital equipment for the production system, your products need to be well prepared.
image
The more complex products you have, the less efficient and costlier the production is, even with advanced production lines. Applying modularization means that your products have a uniform design based on common concepts and standardized interfaces. To the market, the end products are unique, and your production process is consistent. Thus, modularization increases the value of Industry 4.0.
Want to learn more?
First of all, I recommend people who are new to modularity to read the book as a starting point as it is written for a broad audience. Now I want to learn more. What can you recommend?
As you say, we also encourage you to read the book, reflect on it, and adapt the knowledge to your unique situation. We know that it could be challenging to take the next steps, so you are welcome to contact us for advice.
Please visit our website www.brickstrategy.com for more.
For readers of the book, we plan to organize a virtual meeting in May 2021 -the date and time to be confirmed with the audience. Duration approx. 1 hour.
Björn Eriksson and Daniel Strandhammar will answer questions from participants in the meeting. Also, we are curious about your comments/feedback.
To allow time for a proper discussion, we will invite a maximum of 4 guests. Therefore be fast to apply for this virtual meeting by sending an email to tacit@planet.nl or info@brickstrategy.com with your contact details
before May 7th.
I will moderate and record the meeting. We will publish the recording in a short post, allowing everyone to benefit from the discussion. Stay tuned if you are interested, and be fast to apply if you have a question to ask.
What I learned
- Modularization is a strategy that applies to almost every business and increases the competitiveness of a company.
- Modularization is not a technical decision to be executed by R&D and Engineering. It requires an effort from all stakeholders in the company. Therefore, it should be led by a CEO or CTO.
- For future products, modularization is even more important to fulfill one of the promises of Industry 4.0: batch-size 1 (manufacturing a unique product for a single customer with the cost and effort as if it were done in a serial production mode)
- Although we talk a lot about modularization in PLM implementations, it is a people and processes first activity. Then the PLM infrastructure has to support modularization. Do not buy a PLM system to start modularization. Think first!
Conclusion
Modularization is a popular topic at board meetings as it is easy to explain the business benefits. People in engineering and marketing often miss the time and skills to translate modularization into a framework that aligns all stakeholders. After reading the book “The Modular Way,” you will not have solved this issue. There are many, more academic books related to modularization. With this book, you will be better aware of where to start and how to focus.
There is another interesting virtual event in May: the CIMdata PLM Road Map & PDT Spring 2021conference. The theme:
DISRUPTION—the PLM Professionals’ Exploration of Emerging Technologies that Will Reshape the PLM Value Equation.
I look forward to seeing you at this conference and discuss and learn together the changes we have to make – DISRUPTION or EXTINCTION or EVOLUTION. More on this topic soon.
After the series about “Learning from the past,” it is time to start looking toward the future. I learned from several discussions that I probably work most of the time with advanced companies. I believe this would motivate companies that lag behind even to look into the future even more.
If you look into the future for your company, you need new or better business outcomes. That should be the driver for your company. A company does not need PLM or a Digital Twin. A company might want to reduce its time to market and improve collaboration between all stakeholders. These objectives can be realized by different ways of working and an IT infrastructure to allow these processes to become digital and connected.
That is the “game”. Coming back to the future of PLM. We do not need a discussion about definitions; I leave this to the academics and vendors. We will see the same applies to the concept of a Digital Twin.
My statement: The digital twin is not new. Everybody can have their own digital twin as long as you interpret the definition differently. Does this sound like the PLM definition?
The definition
I like to follow the Gartner definition:
A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person, or other abstraction. Data from multiple digital twins can be aggregated for a composite view across a number of real-world entities, such as a power plant or a city, and their related processes.
As you see, not a narrow definition. Now we will look at the different types of interpretations.
Single-purpose siloed Digital Twins
- Simple – data only
One of the most straightforward applications of a digital twin is, for example, my Garmin Connect environment. My device registers performance parameters (speed, cadence, power, heartbeat, location) when cycling. Then, after every trip, I can analyze my performance. I can see changes in my overall performance; compare my performance with others in my category (weight, age, sex).
Based on that, I can decide if I want to improve my performance. My personal business goal is to maintain and improve my overall performance, knowing I cannot stop aging by upgrading my body.
On November 4th, 2020, I am participating in the (almost virtual) Digital Twin conference organized by Bits&Chips in the Netherlands. In the context of human performance, I look forward to Natal van Riel’s presentation: Towards the metabolic digital twin – for sure, this direction is not simple. Natal is a full professor at the Technical University in Eindhoven, the “smart city” in the Netherlands.
- Medium – data and operating models
Many connected devices in the world use the same principle. An airplane engine, an industrial robot, a wind turbine, a medical device, and a train carriage; all track the performance based on this connection between physical and virtual, based on some sort of digital connectivity.
The business case here is also monitoring performance, predicting maintenance, and upgrading the product when needed.
This is the domain of Asset Lifecycle Management, a practice that has existed for decades. Based on financial and performance models, the optimal balance between maintaining and overhauling has to be found. Repairs are disruptive and can be extremely costly. A manufacturing site that cannot produce can cost millions per day. Connecting data between the physical and the virtual model allows us to have real-time insights and be proactive. It becomes a digital twin.
- Advanced – data and connected 3D model
The digital twin we see the most in marketing videos is a virtual twin, using a 3D representation for understanding and navigation. The 3D representation provides a Virtual Reality (VR) environment with connected data. When pointing at the virtual components, information might appear, or some animation might take place.
Building such a virtual representation is a significant effort; therefore, there needs to be a serious business case.
The simplest business case is to use the virtual twin for training purposes. A flight simulator provides a virtual environment and behavior as-if you are flying in a physical airplane – the behavior model behind the simulator should match as well as possibly the real behavior. However, as it is a model, it will never be 100 % reality and requires updates when new findings or product changes appear.
A virtual model of a platform or plant can be used for training on Standard Operating Procedures (SOPs). In the physical world, there is no place or time to conduct such training. Here the complexity might be lower. There is a 3D Model; however, serious updates can only be expected after a major maintenance or overhaul activity.
These practices are not new either and are used in places where physical training cannot be done.
More challenging is the Augmented Reality (AR) use case. Here the virtual model, most of the time, a lightweight 3D Model, connects to real-time data coming from other sources. For example, AR can be used when an engineer has to service a machine. The AR environment might project actual data from the machine, indicate service points and service procedures.
The positive side of the business case is clear for such an opportunity, ensuring service engineers always work with the right information in a real-time context. The main obstacle to implementing AR, in reality, is the access to data, the presentation of the data and keeping the data in the AR environment matching the reality.
And although there are 3D Models in use, they are, to my knowledge, always created in siloes, not yet connected to their design sources. Have a look at the Digital Twin conference from Bits&Chips, as mentioned before.
Several of the cases mentioned above will be discussed here. The conference’s target is to share real cases concluded by Q & A sessions, crucial for a virtual event.
Connected Virtual Twins along the product lifecycle
So far, we have been discussing the virtual twin concept, where we connect a product/system/person in the physical world to a virtual model. Now let us zoom in on the virtual twins relevant for the early parts of the product lifecycle, the manufacturing twin, and the development twin. This image from Siemens illustrates the concept:
On slides they imagine a complete integrated framework, which is the future vision. Let us first zoom in on the individual connected twins.
The digital production twin
This is the area of virtual manufacturing and creating a virtual model of the manufacturing plant. Virtual manufacturing planning is not a new topic. DELMIA (Dassault Systèmes) and Tecnomatix (Siemens) are already for a long time offering virtual manufacturing planning solutions.
At that time, the business case was based on the fact that the definition of a manufacturing plant and process done virtually allows you to optimize the plant before investing in physical assets.
Saving money as there is no costly prototype phase to optimize production. In a virtual world, you can perform many trade-off studies without extra costs. That was the past (and, for many companies, still the current situation).
With the need to be more flexible in manufacturing to address individual customer orders without increasing the overhead of delivering these customer-specific solutions, there is a need for a configurable plant that can produce these individual products (batch size 1).
This is where the virtual plant model comes into the picture again. Instead of having a virtual model to define the ultimate physical plant, now the virtual model remains an active model to propose and configure the production process for each of these individual products in the physical plant.
This is partly what Industry 4.0 is about. Using a model-based approach to configure the plant and its assets in a connected manner. The digital production twin drives the execution of the physical plant. The factory has to change from a static factory to a dynamic “smart” factory.
In the domain of Industry 4.0, companies are reporting progress. However, in my experience, the main challenge is still that the product source data is not yet built in a model-based, configurable manner. Therefore, requires manual rework. This is the area of Model-Based Definition, and I have been writing about this aspect several times. Latest post: Model-Based: Connecting Engineering and Manufacturing
The business case for this type of digital twin, of course, is to be able to customer-specific products with extremely competitive speed and reduced cost compared to standard. It could be your company’s survival strategy. As it is hard to predict the future, as we see from COVID-19, it is still crucial to anticipate the future instead of waiting.
The digital development twin
Before a product gets manufactured, there is a product development process. In the past, this was pure mechanical with some electronic components. Nowadays, many companies are actually manufacturing systems as the software controlling the product plays a significant role. In this context, the model-based systems engineering approach is the upcoming approach to defining and testing a system virtually before committing to the physical world.
Model-Based Systems Engineering can define a single complex product and perform all kinds of analyses on the system even before there is a physical system in place. I will explain more about model-based systems engineering in future posts. In this context, I want to stress that having a model-based system engineering environment combined with modularity (do not confuse it with model-based) is a solid foundation for dealing with unique custom products. Solutions can be configured and validated against their requirements already during the engineering phase.
The business case for the digital development twin is easy to make. Shorter time to market, improved and validated quality, and reduced engineering hours and costs compared to traditional ways of working. To achieve these results, for sure, you need to change your ways of working and the tools you are using. So it won’t be that easy!
For those interested in Industry 4.0 and the Model-Based System Engineering approach, join me at the upcoming PLM Road Map 2020 and PDT 2020 conference on 17-18-19 November. As you can see from the agenda, a lot of attention to the Digital Twin and Model-Based approaches.
Three digital half-days with hopefully a lot to learn and stay with our feet on the ground. In particular, I am looking forward to Marc Halpern’s keynote speech: Digital Thread: Be Careful What you Wish For, It Just Might Come True
Conclusion
It has been very noisy on the internet related to product features and technologies, probably due to COVID-19 and therefore disrupted interactions between all of us – vendors, implementers and companies trying to adjust their future. The Digital Twin concept is an excellent framing for a concept that everyone can relate to. Choose your business case and then look for the best matching twin.
Jos Voskuil's Weblog 
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