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Last week I shared my first review of the PLM Roadmap / PDT Fall 2020 conference, organized by CIMdata and Eurostep. Having digested now most of the content in detail, I can state this was the best conference of 2020. In my first post, the topics I shared were mainly the consultant’s view of digital thread and digital twin concepts.

This time, I want to focus on the content presented by the various Aerospace & Defense working groups who shared their findings, lessons-learned (so far) on topics like the Multi-view BOM, Supply Chain Collaboration, MBSE Data interoperability.

These sessions were nicely wrapped with presentations from Alberto Ferrari (Raytheon), discussing the digital thread between PLM and Simulation Lifecycle Management and Jeff Plant (Boeing) sharing their Model-Based Engineering strategy.

I believe these insights are crucial, although there might be people in the field that will question if this research is essential. Is not there an easier way to achieve to have the same results?

Nicely formulated by Ilan Madjar as a comment to my first post:

Ilan makes a good point about simplifying the ideas to the masses to make it work. The majority of companies probably do not have the bandwidth to invest and understand the future benefits of a digital thread or digital twins.

This does not mean that these topics should not be studied. If your business is in a small, simple eco-system and wants to work in a connected mode, you can choose a vendor and a few custom interfaces.

However, suppose you work in a global industry with an extensive network of partners, suppliers, and customers.

In that case, you cannot rely on ad-hoc interfaces or a single vendor. You need to invest in standards; you need to study common best practices to drive methodology, standards, and vendors to align.

This process of standardization is so crucial if you want to have a sustainable, connected enterprise. In the end, the push from these companies will lead to standards, allowing the smaller companies to ad-here or connect to.

The future is about Connected through Standards, as discussed in part 1 and further in this post. Let’s go!

Global Collaboration – Defining a baseline for data exchange processes and standards

Katheryn Bell (Pratt & Whitney Canada) presented the progress of the A&D Global Collaboration workgroup. As you can see from the project timeline, they have reached the phase to look towards the future.

Katheryn mentioned the need to standardize terminology as the first point of attention. I am fully aligned with that point; without a standardized terminology framework, people will have a misunderstanding in communication.

This happens even more in the smaller businesses that just pick sometimes (buzz) terms without a full understanding.

Several years ago, I talked with a PLM-implementer telling me that their implementation focus was on systems engineering. After some more explanations, it appeared they were making an attempt for configuration management in reality. Here the confusion was massive. Still, a standard, common terminology is crucial in our domain, even if it seems academic.

The group has been analyzing interoperability standards, standards for long-time archival and retrieval (LOTAR), but also has been studying the ISO 44001 standard related to Collaborative business relationship management systems

In the Q&A session, Katheryn explained that the biggest problem to solve with collaboration was the risk of working with the wrong version of data between disciplines and suppliers.

Of course, such errors can lead to huge costs if they are discovered late (or too late). As some of the big OEMs work with thousands of suppliers, you can imagine it is not an issue easily discovered in a more ad-hoc environment.

The move to a standardized Technical Data Package based on a Model-Based Definition is one of these initiatives in this domain to reduce these types of errors.

You can find the proceedings from the Global Collaboration working group here.

 

Connect, Trace, and Manage Lifecycle of Models, Simulation and Linked Data: Is That Easy?

I loved Alberto Ferrari‘s (Raytheon) presentation how he described the value of a model-based digital thread, positioning it in a targeted enterprise.

Click on the image and discover how business objectives, processes and models go together supported by a federated infrastructure.

Alberto’s presentation was a kind of mind map from how I imagine the future, and it is a pity if you have not had the chance to see his session.

Alberto also focused on the importance of various simulation capabilities combined with simulation lifecycle management. For Alberto, they are essential to implement digital twins. Besides focusing on standards, Alberto pleas for a semantic integration, open service architecture with the importance of DevSecOps.

Enough food for thought; as Alberto mentioned, he presented the corporate vision, not the current state.

More A&D Action Groups

There were two more interesting specialized sessions where teams from the A&D action groups provided a status update.

Brandon Sapp (Boeing) and Ian Parent (Pratt & Whitney) shared the activities and progress on Minimum Model-Based Definition (MBD) for Type Design Certification.

As Brandon mentioned, MBD is already a widely used capability; however, MBD is still maturing and evolving.  I believe that is also one of the reasons why MBD is not yet accepted in mainstream PLM. Smaller organizations will wait; however, can your company afford to wait?

More information about their progress can be found here.

Mark Williams (Boeing) reported from the A&D Model-Based Systems Engineering action group their first findings related to MBSE Data Interoperability, focusing on an Architecture Model Exchange Solution.  A topic interesting to follow as the promise of MBSE is that it is about connected information shared in models. As Mark explained, data exchange standards for requirements and behavior models are mature, readily available in the tools, and easily adopted. Exchanging architecture models has proven to be very difficult. I will not dive into more details, respecting the audience of this blog.

For those interested in their progress, more information can be found here

Model-Based Engineering @ Boeing

In this conference, the participation of Boeing was significant through the various action groups. As the cherry on the cake, there was Jeff Plant‘s session, giving an overview of what is happening at Boeing. Jeff is Boeing’s director of engineering practices, processes, and tools.

In his introduction, Jeff mentioned that Boeing has more than 160.000 employees in over 65 countries. They are working with more than 12.000 suppliers globally. These suppliers can be manufacturing, service or technology partnerships. Therefore you can imagine, and as discussed by others during the conference, streamlined collaboration and traceability are crucial.

The now-famous MBE Diamond symbol illustrates the model-based information flows in the virtual world and the physical world based on the systems engineering approach. Like Katheryn Bell did in her session related to Global Collaboration, Jeff started explaining the importance of a common language and taxonomy needed if you want to standardize processes.

Zoom in on the Boeing MBE Taxonomy, you will discover the clarity it brings for the company.

I was not aware of the ISO 23247 standard concerning the Digital Twin framework for manufacturing, aiming to apply industry standards to the model-based definition of products and process planning. A standard certainly to follow as it brings standardization on top of existing standards.

As Jeff noted: A practical standard for implementation in a company of any size. In my opinion, mandatory for a sustainable, connected infrastructure.

Jeff presented the slide below, showing their standardization internally around federated platforms.

This slide resembles a lot the future platform vision I have been sharing since 2017 when discussing PLM’s future at PLM conferences, when explaining the differences between Coordinated and Connected – see also my presentation here on Slideshare.

You can zoom in on the picture to see the similarities. For me, the differences were interesting to observe. In Jeff’s diagram, the product lifecycle at the top indicates the platform of (central) interest during each lifecycle stage, suggesting a linear process again.

In reality, the flow of information through feedback loops will be there too.

The second exciting detail is that these federated architectures should be based on strong interoperability standards. Jeff is urging other companies, academics and vendors to invest and come to industry standards for Model-Based System Engineering practices.  The time is now to act on this domain.

It reminded me again of Marc Halpern’s message mentioned in my previous post (part 1) that we should be worried about vendor alliances offering an integrated end-to-end data flow based on their solutions. This would lead to an immense vendor-lock in if these interfaces are not based on strong industry standards.

Therefore, don’t watch from the sideline; it is the voice (and effort) of the companies that can drive standards.

Finally, during the Q&A part, Jeff made an interesting point explaining Boeing is making a serious investment, as you can see from their participation in all the action groups. They have made the long-term business case.

The team is confident that the business case for such an investment is firm and stable, however in such long-term investment without direct results, these projects might come under pressure when the business is under pressure.

The virtual fireside chat

The conference ended with a virtual fireside chat from which I picked up an interesting point that Marc Halpern was bringing in. Marc mentioned a survey Gartner has done with companies in fast-moving industries related to the benefits of PLM. Companies reported improvements in accuracy and product development. They did not see so much a reduced time to market or cost reduction. After analysis, Gartner believes the real issue is related to collaboration processes and supply chain practices. Here lead times did not change, nor the number of changes.

Marc believes that this topic will be really showing benefits in the future with cloud and connected suppliers. This reminded me of an article published by McKinsey called The case for digital reinvention. In this article, the authors indicated that only 2 % of the companies interview were investing in a digital supply chain. At the same time, the expected benefits in this area would have the most significant ROI.

The good news, there is consistency, and we know where to focus for early results.

Conclusion

It was a great conference as here we could see digital transformation in action (groups). Where vendor solutions often provide a sneaky preview of the future, we saw people working on creating the right foundations based on standards. My appreciation goes to all the active members in the CIMdata A&D action groups as they provide the groundwork for all of us – sooner or later.

Last week I was happy to attend the PLM Roadmap / PDT Fall 2020 conference as usual organized by CIMdata and Eurostep. I wrote about the recent PI DX conference, which touched a lot on the surface of PLM and Digital Transformation. This conference is really a conference for those who want to understand the building blocks needed for current and future PLM.

In this conference, usually with approximately 150 users on-site, now with over 250 connected users for 3 (half) days. Many of us, following every session of the conference. As an active participant in the physical events, it was a little disappointing not to be in the same place with the other participants this time. The informal network meetings in this conference have always been special thanks to a relatively small but stable group of experts.  Due to the slightly reduced schedule, there was this time, less attention for some of the typical PDT-topics most of the time coming from Sweden and related to sustainability.

The conference’s theme was Digital Thread—the PLM Professionals’ Path to Delivering Innovation, Efficiency, and Quality and might sound like a marketing statement.  However, the content presented was much more detailed than just marketing info. The fact that you watched the presentation on your screen made it an intense conference with many valuable details.

Have a look at the agenda, and I will walk you through some of the highlights for me. As there was so much content to discuss, I will share this time part 1. Next week, in part 2, you will see the coherence of all the presentations.

As if there was a Coherent Thread.

Digital Twin, It Requires a Digital Thread

Peter Bilello, President & CEO, CIMdata, ‘s keynote with the title Digital Twin, It Requires a Digital Thread was immediately an illustration of discussing reality.  When I stated at the Digital Twin conference in the Netherlands that “Digital Twins do not run on Documents“, it had the same meaning as when Peter stated,” A Digital Twin without a Digital Thread is an orphan”.

Digital Thread

And Peter’s statement, “All companies do PLM, most of the time however disconnected”, is another way to stimulate companies working in a connected manner.

As usual Peter’s session was a good overview of the various aspect related to the Digital Thread and Digital Twin.

Digital Twin

The concept of a virtual twin is not new. The focus is as mentioned before now more on the term “Connected” Peter provided the CIMdata definition for Digital Thread and Digital Twin. Click on the images to the left to read the full definition.

Peter’s overview also referred to the Boeing Diamond, illustrating the mapping of the physical and virtual world, connected through a Digital Thread the various Digital Twins that can exist. The Boeing Diamond was one of the favorites during the conference.

When you look at Peter’s conclusions, there is an alignment with what I wrote in the post: A Digital Twin for Everyone and the fact that we need to strive for a connected enterprise. Only then we can benefit from a Digital Twin concept.

 

The Multi-view BOM Solution Evaluation
– Process, Results, and Industry Impacts

The reports coming from the various A&D PLM action groups are always engaging sessions to watch. Here, nine companies, even competitors, discuss and explore PLM themes between themselves supported by CIMdata.

These companies were the first that implemented PLM; it is interesting to watch how they move forward like supertankers. They cannot jump from one year to another year on a new fashionable hype. Their PLM-infrastructure needs to be consistent and future-proof due to their data’s longevity and the high standards for regulatory compliance and safety.

However, these companies are also pioneers for the future. They have been practicing Model-Based approaches for over ten years already and are still learning. In next week’s post, you will read later that these frontrunners are pushing for standards to make a Model-Based future affordable and achievable.

In that context, the action group Multi-View BOM shared their evaluation results for a study related to the multi-view BOM. A year ago, I wrote about this topic when Fred Feru from Airbus presented the intermediate results at the CIMdata Roadmap/PDT 2019 conference.

Dan Ganser (Gulfstream) and Javier Reines (Airbus) presented the findings. The conclusion was that the four vendors evaluated, i.e., Aras, Dassault Systems, PTC and Siemens, all passed the essential requirements and use cases. You can find the report and the findings here: Multi-view Bill of Materials

One interesting remark.

When the use cases were evaluated, the vendors could score on a level from 0 to 5, see picture. Interesting to see that apparently, it was possible to exceed the requirement, something that seems like a contradiction.

In particular, in this industry, where formal requirements management is a must – either you meet a requirement or not.

Dan Ganser explained that the current use cases were defined based on the minimum expectations, therefore there was the option to exceed the requirement. I still would be curious to see what does it mean to exceed the requirement. Is it usability, time, or something innovative we might have missed?

 

5G for Digital Twins & Shadows

I learned a lot from the presentation from Niels Koenig, working at the Fraunhofer Institute for Production Technology. Niels explained how important 5G is for realizing the Industry 4.0 targets. At the 5G Industry Campus, several projects are running to test and demonstrate the value of 5G in relation to manufacturing.

If you want to get an impression of the 5G Industry Campus – click on the Youube movie.

One of the examples Niels discussed was closed-loop manufacturing. Thanks to the extremely low latency (< 1ms), a connected NC machine can send real-time measurements to be compared with the expected values. For example, in the case of resonance, the cutting might not be smooth. Thanks to the closed-loop, the operator will be able to interfere or adjust the operation. See the image below.

Digital Thread: Be Careful What you Wish For, It Just Might Come True

I was looking forward to Marc Halpern‘s presentation. Marc often brings a less technical viewpoint but a more business-related viewpoint to the discussion. Over the past ten years, there have been many disruptive events, most recently the COVID-pandemic.

Companies are asking themselves how they can remain resilient. Marc shared some of his thoughts on how Digital Twins and Digital Threads can support resilience.

In that context, Gartner saw a trend that their customers are now eagerly looking for solutions related to Digital Twin, Digital Thread, Model-Based Approaches, combined with the aim to move to the cloud. Related to Digital Thread and Digital Twin, most of Gartner’s clients are looking for traceability and transparency along the product lifecycle. Most Digital Twin initiatives focus on a twin of operational assets, particularly inside the manufacturing facility. Nicely linking to Niels Konig’s session related to 5G.

Marc stated that there seems to be a consensus that a Digital Thread is compelling enough for manufacturers to invest. In the end, they will have to. However, there are also significant risks involved. Marc illustrated the two extremes; in reality, companies will end up somewhere in the middle, illustrated later by Jeff Plant from Boeing. The image on the left is a sneaky preview for next week.

When discussing the Digital Thread, Marc again referred to it more as a Digital Net, a kind of connected infrastructure for various different threads based on the various areas of interest.

I show here a slide from Marc’s presentation at the PDT conference in 2018. It is more an artist’s impression of the same concept discussed during this conference again, the Boeing Diamond.

Related to the risk of implementing a Digital Thread and Digital Twin, Marc showed another artistic interpretation; The two extremes of two potential end states of Digital Thread investment. Marc shared the critical risks for both options.

For the Vendor Black Hole, his main points were that if you choose a combined solution, diminished negotiating power, higher implementation costs, and potentially innovative ideas might not be implemented as they are not so relevant for the vendor. They have the power!

As an example of combined solutions Marc mentioned, the recently announced SAP-Siemens partnership, the Rockwell Automation-PTC partnership, the Schneider Electric-Aveva-partnership, and the ABB-Dassault Systemes partnership.

Once you are in the black hole, you cannot escape. Therefore, Marc recommended making sure you do not depend on a few vendors for your Digital Twin infrastructure.

The picture on the left illustrates the critical risks of the Enterprise Architecture “Mess”. It is a topic that I am following for a long time. Suppose you have a collection of services related to the product lifecycle, like Workflow-services, 3D Modeling-services, BOM-services, Manufacturing-services.

Together they could provide a PLM-infrastructure.

The idea behind this is that thanks to openness and connectivity, every company can build its own unique enterprise architecture. No discussion about standard best practices. You build your company’s best practices (for the future, the current ?)

It is mainly promoted as a kind of bottom-up PLM. If you are missing capabilities, just build them yourselves, using REST-services, APIs, using Low-Code platforms. It seems attractive for the smaller enterprises, however most of the time, only a short time. I fully concur with Marc’s identified risks here.

As I often illustrated in presentations related to a digital future, you will need a mix of both. Based on your point of focus, you could imagine five major platforms being connected together to cover all aspects of a business. Depending on your company’s business model and products, one of them might be the dominant one. With my PLM-focus, this would be the Product Innovation Platform, where the business is created.

Marc ended with five priorities to enable a long-term Digital Thread success.

  • First of all – set the ground rules for data governance. A topic often mentioned but is your company actively engaging on that already?
  • Next, learn from Model-Based Systems Engineering as a foundation for a Model-Based Enterprise.  A topic often discussed during the previous CIMdata Roadmap / PDT-conference.
  • The change from storing and hiding information in siloes towards an infrastructure and mindset of search and access of data, in particular, the access to Bill of Materials

The last point induced two more points.

  • The need for an open architecture and standards. We would learn more on this topic on day 3 of the conference.
  • Make sure your digital transformation sticks within the organization by investing and executing on organizational change management.

Conclusion

The words “Digital Thread” and “Digital Twin” are mentioned 18 times in this post and during the conference even more. However, at this conference, they were not hollow marketing terms. They are part of a dictionary for the future, as we will see in next week’s post when discussing some of the remaining presentations.

Closing this time with a point we all agreed upon: “A Digital Twin without a Digital Thread is an orphan”. Next week more!

After the series about “Learning from the past,” it is time to start looking towards the future.  I learned from several discussions that I am probably working 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, 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

  1. Simple – data only

One of the most straightforward applications of a digital twin is, for example, my Garmin Connect environment. When cycling, my device registers performance parameters (speed, cadence, power, heartbeat, location). 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

  1. 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, predict maintenance, and upgrade the product when needed.

This is the domain of Asset Lifecycle Management, a practice that exists for decades. Based on financial and performance models, the optimal balance between maintaining and overhaul has to be found. Repairs are disruptive and can be extremely costly. A manufacturing site that cannot produce can costs 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.

  1. Advanced – data and connected 3D model

The ditial 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 takes 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 the physical airplane – the behavior model behind the simulator should match as good as possible 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 the 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 for 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, to my experience, the main challenge is still that the product source data is not yet built in a model-based, configurable manner. Therefore, requiring 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 analysis 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 COVIC-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.

Last week I shared the first impression from my favorite conference, the PLM Roadmap / PDT conference organized by CIMdata and Eurostep. You can read some of the highlights here: The weekend after PLM Roadmap / PDT 2019 Day 1.

Click on the logo to see what was the full agenda. In this post, I will focus on some of the highlights of day 2.

Chernobyl, The megaproject with the New Arch

Christophe Portenseigne from the Bouygues Construction Group shared with us his personal story about this megaproject, called Novarka. 33 years ago, reactor #4 exploded and has been confined with an object shelter within six months in 1986. This was done with heroic speed, and it was anticipated that the shelter would only last for 20 – 30 years.  You can read about this project here.

The Novarka project was about creating a shelter for Confinement of the radioactive dust and protection of the existing against external actions (wind, water, snow…) for the next 100 years!

And even necessary, the inside the arch would be a plant where people could work safely on the process of decommissioning the existing contaminated structures. You can read about the full project here at the Novarka website.

What impressed me the most the personal stories of Christophe taking us through some of the massive challenges that need to be solved with innovative thinking. High complexity, a vast number of requirements, many parties, stakeholders involved closed in June 2019. As Christophe mentioned, this was a project to be proud of as it creates a kind of optimism that no matter how big the challenges are, with human ingenuity and effort, we can solve them.

A Model Factory for the Efficient Development of High Performing Vehicles

Eric Landel, expert leader for Numerical Modeling and Simulation at Renault, gave us an interesting insight into an aspect of digitalization that has become very valuable, the connection between design and simulation to develop products, in this case, the Renault CLIO V, as much as possible in the virtual world. You need excellent simulation models to match future reality (and tests). The target of simulation was to get the highest safety test results in the Europe NCAP rating – 5 stars.

The Renault modeling factory implemented a digital loop (below) to ensure that at the end of the design/simulation, a robust design would exist.  Eric mentioned that for the Clio, they did not build a prototype anymore. The first physical tests were done on cars coming from the plant. Despite the investment in simulation software, a considerable saving in crash part over cost before TGA (Tooling Go Ahead).

Combined with the savings, the process has been much faster than before. From 10 weeks for a simulation loop towards 4 weeks. The next target is to reduce this time to 1 week. A real example of digitization and a connected model-based approach.

From virtual prototype to hybrid twin

ESI – their sponsor session Evolving from Virtual Prototype Testing to Hybrid Twin: Challenges & Benefits was an excellent complementary session to the presentation from Renault

PLM, MBSE and Supply chain – challenges and opportunities

Nigel Shaw’s presentation was one of my favorite presentations, as Nigel addressed the same topics that I have been discussing in the past years. His focus was on collaboration between the OEM and supplier with the various aspects of requirements management, configuration management, simulation and the different speeds of PLM (focus on mechanical) and ALM (focus on software)

How can such activities work in a digitally-connected environment instead of a document-based approach?  Nigel looked into the various aspects of existing standards in their domains and their future. There is a direction to MBE (Model-Based Everything) but still topics to consider. See below:

I agree with Nigel – the future is model-based – when will be the issue for the market leaders.

The ISO AP239 ed3 Project and the Through Life Cycle Interoperability Challenge

Yves Baudier from AFNET,  a reference association in France regarding industry digitation, digital threads, and digital processes for Extended Enterprise/Supply chain. All about a digital future and Yves presentation was about the interoperability challenge, mentioning three of my favorite points to consider:

  • Data becoming more and more a strategic asset – as digitalization of Industry and Services, new services enabled by data analytics
  • All engineering domains (from concept design to system end of life) need to develop a data-centric approach (not only model-centric)– An opportunity for PLM to cover the full life-cycle
  • Effectivity and efficiency of data interoperability through the life-cycle is now an essential industry requirement – e.g., “virtual product” and “digital twin” concepts

All the points are crucial for the domain of PLM.

In that context, Yves discussed the evolution of the ISO 10303-239 standard, also known as PLCS. The target with ISO AP239 ed3 is to become the standard for Aerospace and Defense for the full product lifecycle and through this convergence being able to push IT/PLM Vendors to comply – crucial for a digital enterprise

Time for the construction / civil industry

Christophe Castaing, director of digital engineering at Egis, shared with us their solution framework to manage large infrastructure projects by focusing on both the Asset Information (BIM-based) and the collaborative processes between the stakeholders, all based on standards. It was a broad and in-depth presentation – too much to share in a blog post. To conclude (see also Christophe’s slide below) in the construction industry more and more, there is the desire to have a digital twin of a given asset (building/construction), creating the need for standard information models.

Pierre Benning, IT director from Bouygues Public Works gave us an update on the MINnD project. MINnD standing for Modeling INteroperable INformation for sustainable INfrastructures in xD, a French research project dedicated to the deployment of BIM and digital engineering in the infrastructure sector. Where BIM has been starting from the construction industry, there is a need for a similar, digital modeling approach for civil infrastructure. In 2014 Christophe Castaing already reported the activities of the MINnD project – see The weekend after PDT 2014. Now Pierre was updating us on what are the activities for MINnD Season 2 – see below:

As you can see, again, the interest in digital twins for operations and maintenance. Perhaps here, the civil infrastructure industry will be faster than traditional industries because of its enormous value. BIM and GIS reconciliation is a precise topic as many civil infrastructures have a GIS aspect – Road/Train infrastructure for example. The third bullet is evident to me. With digitization and the integration of contractors and suppliers, BIM and PLM will be more-and-more conceptual alike. The big difference still at this moment: BIM has one standard framework where PLM-standards are still not in a consolidation stage.

Digital Transformation for PLM is not an evolution

If you have been following my blog in the past two years, you may have noticed that I am exploring ways to solve the transition from traditional, coordinated PLM processes towards future, connected PLM. In this session, I shared with the audience that digital transformation is disruptive for PLM and requires thinking in two modes.

Thinking in two modes is not what people like, however, organizations can run in two modes. Also, I shared some examples from digital transformation stories that illustrate there was no transformation, either failure or smoke, and mirrors. You can download my presentation via SlideShare here.

Fireplace discussion: Bringing all the Trends Together, What’s next

We closed the day and the conference with a fireplace chat moderated by Dr. Ken Versprille from CIMdata, where we discussed, among other things, the increasing complexity of products and products as a service. We have seen during the sessions from BAE Systems Maritime and Bouygues Construction Group that we can do complex projects, however, when there are competition and time to deliver pressure, we do not manage the project so much, we try to contain the potential risk. It was an interactive fireplace giving us enough thoughts for next year.

Conclusion

Nothing to add to Håkan Kårdén’s closing tweet – I hope to see you next year.

This is the moment of the year, where at least in my region, most people take some time off to disconnect from their day-to-day business.  For me, it is never a full disconnect as PLM became my passion, and you should never switch off your passion.

On August 1st, 1999, I started my company TacIT, the same year the acronym PLM was born. I wanted to focus on knowledge management, therefore the name TacIT.  Being dragged into the SmarTeam world with a unique position interfacing between R&D, implementers and customers I found the unique sweet spot, helping me to see all aspects from PLM – the vendor position, the implementer’s view, the customer’s end-user, and management view.

It has been, and still, is 20 years of learning and have been sharing most in the past ten years through my blog. What I have learned is that the more you know, the more you understand that situations are not black and white. See one of my favorite blog pictures below.

So there is enough to overthink during the holidays. Some of my upcoming points:

From coordinated to connected

Instead of using the over-hyped term: Digital Transformation, I believe companies should learn to work in a connected mode, which has become the standard in our daily life. Connected means that information needs to be stored in databases somewhere, combined with openness and standards to make data accessible. For more transactional environments, like CRM, MES, and ERP, the connected mode is not new.

In the domain of product development and selling, we have still a long learning path to go as the majority of organizations is relying on documents, be it Excels, Drawings (PDF) and reports. The fact that they are stored in electronic file formats does not mean that they are accessible. There is still manpower needed to create these artifacts or to extract the required information from them.

The challenge for modern PLM is to establish new best practices around a model-based approach for systems engineering (MBSE), for engineering to manufacturing (MBD/MBE) and operations (Digital Twins). All these best practices should be generic and connected ultimately.  I wrote about these topics in the past, have a look at:

PLM Vendors are showing pieces of the puzzle, but it is up to the implementers to establish the puzzle, without knowing in detail what the end result will be. This is the same journey of Columbus. He had a boat and a target towards the unknown. He discovered a country with a small population, nowadays a country full of immigrants who call themselves natives.

However, the result was an impressive transformation.

Reading about transformation

Last year I read several books to get more insight into what motivates us, and how can we motivate people to change. In one way, it is disappointing to learn that we civilized human beings most of the time to not make rational decisions but act based on our per-historic brain.

 

Thinking, Fast and Slow from Daniel Kahneman was one of the first books in that direction as a must-read to understand our personal thinking and decision processes.

 

 

 

I read Idiot Brain: What Your Head Is Really Up To from Dean Burnett, where he explains this how our brain appears to be sabotaging our life, and what on earth it is really up to. Interesting to read but could be a little more comprehensive

 

I got more excited from Dan Ariely”s book: Predictably Irrational: The Hidden Forces That Shape Our Decisions as it was structured around topics where we handle completely irrational but predictable. And this predictability is used by people (sales/politicians/ management) to drive your actions. Useful to realize when you recognize the situation

 

These three books also illustrate the flaws of our modern time – we communicate fast (preferable through tweets) – we decide fast based on our gut feelings – so you realize towards what kind of world we are heading.  Going through a transformation should be considered as a slow, learning process. Like reading a book – it takes time to digest.

Once you are aiming at a business transformation for your company or supporting a company in its transformation, the following books were insightful:

Leading Digital: Turning Technology into Business Transformation by George Westerman, Didier Bonnet and Andrew McAfee is maybe not the most inspiring book, however as it stays close to what we experience in our day-to-day-life it is for sure a book to read to get a foundational understanding of business transformation.

 

The book I liked the most recent was Leading Transformation: How to Take Charge of Your Company’s Future by Nathan Furr, Kyle Nel, Thomas Zoega Ramsoy as it gives examples of transformation addressing parts of the irrational brain to get a transformation story. I believe in storytelling instead of business cases for transformation. I wrote about it in my blog post: PLM Measurable or a myth referring to Yuval Harari’s book Homo Sapiens

Note: I am starting my holidays now with a small basket of e-books. If you have any recommendations for books that I must read – please write them in the comments of this blog

Discussing transformation

After the summer holidays, I plan to have fruitful discussions around topics close to PLM. Working on a post and starting a conversation related to PLM, PIM, and Master Data Management. The borders between these domains are perhaps getting vaguer in a digital enterprise.

Further, I am looking forward to a discussion around the value of PLM assisting companies in developing sustainable products. A sustainable and probably circular economy is required to keep this earth a place to live for everybody. The whole discussion around climate change, however, is worrying as we should be Thinking – not fast and slow – but balanced.

A circular economy has been several times a topic during the joint CIMdata PLM Roadmap and PDT conferences, which bring me to the final point.

On 13th and 14th November this year I will participate again in the upcoming PLM Roadmap and PDT conference. This time in La Defense, Paris, France. I will share my experiences from working with companies trying to understand and implement pieces of a digital transformation related to PLM.

There will be inspiring presentations from other speakers, all working on some of the aspects of moving to facets of a connected enterprise. It is not a marketing event, it is done by professionals, serving professionals. Therefore I hope if you are passioned about the new aspects of PLM, no matter how you name label them, come and join, discuss and most of all, learn.

Conclusion

 

Modern life is about continuous learning  – make it a habit. Even a holiday is again a way to learn to disconnect.

How disconnected I was you will see after the holidays.

 

 

 

According to LinkedIn, there are over a 7500 PLM consultants in my network.  It is quite an elite group of people as I have over 100.000 CEOs in my network according to LinkedIn. Being a CEO is a commodity.

PLM consultants share a common definition, the words Product Lifecycle Management. However, what we all mean by PLM is one of the topics that has evolved over the past 19 years in a significant way.

PLM or cPDM (collaborative PDM)?

In the early days, PLM was considered as an engineering tool for collaboration, either between global subsidiaries or suppliers. The main focus of PLM was to bring engineering information to manufacturing in a controlled way. PLM and cPDM, often seen as solving the same business needs as the implementation of a PLM system most of the time got stuck at the cPDM level.

Main players at that time were Dassault Systemes, UGS (later Siemens PLM) and PTC – their solutions were MCAD-driven with limited scope – bringing engineering information towards manufacturing in a coordinated way.

PLM was not really an approach that created visibility at the management level of a company. How do you value and measure collaboration? Because connectivity was expensive in the early days of PLM, combined with the idea that PLM systems needed to be customized, PLM was framed as costly and hard to deliver value.

Systems Engineering and New Product Introduction

Then, 2005 and beyond, thanks to better connectivity and newcomers in the PLM market, the solution landscape from PLM became broader.  CAD integrations were not a necessary part of the PLM scope according to these newcomers as they focused on governance (New Product Introduction), Bill of Materials or at the front-end of the product design cycle, connecting systems engineering by adding requirements management to their PLM suite.

New players in this domain where SAP, Aras, followed by Autodesk – their focus was more metadata-driven, connection and creating an end-to-end data flow for the product. Autodesk started the PLM and cloud path.

These new capabilities brought a broader scope for PLM indeed. However, they also strengthened the idea that PLM is there for engineers. For the management too complicated, unless they understood the value of coordinated collaboration. Large enterprises saw the benefits of having common processes for PLM as an essential reason to invest in PLM. The graph below showed the potential of PLM, where the shaded area indicates the potential revenue benefits.

Still, this graph does not create “hard numbers,” and it requires visionaries to get a PLM implementation explained and justified across the board.  PLM is framed as expensive even if the budgets spent on PLM are twenty percent or less compared to ERP implementations. As PLM is not about transactional data, the effects of PLM are hard to benchmark. Success has many fathers, and in case of difficulties, the newcomer is to blame.

PLM = IoT?

With the future possibilities, connectivity to the machine-level (IoT or IIoT), a new paradigm related to PLM was created by PTC.  PLM equals IoT – read more here.

Through IoT, it became possible to connect to products/assets in the field, and the simplified message from PTC was that now thanks to IoT (read ThingWorx) PLM was now really possible, releasing traditional PLM out of its engineering boundaries. The connected sensors created the possibility to build and implement more advanced and flexible manufacturing processes, often called Smart Manufacturing or Industrie 4.0.

None of the traditional PLM vendors is talking about PLM solely anymore. Digital transformation is a topic discussed at the board level, where GE played a visionary role with their strong message for change, driven by their CEO Jeff Immelt at that time – have a look at one of his energizing talks here.

However is PLM part of this discussion?

Digital Transformation opened a new world for everyone. Existing product lifecycle concepts could be changed, products are becoming systems, interacting with the environment realized through software features. Systems can be updated/upgraded relatively fast, in particular when you are able to watch and analyze the performance of your assets in almost real-time.

All consultants (me included) like to talk about digital transformation as it creates a positive mood towards the future, imagining everything that is possible. And with the elite of PLM consultants we are discovering the new roles of PLM – see picture below:

Is PLM equal to IoT or Digital Transformation?

I firmly believe the whole Digital Transformation and IoT hypes are unfortunately obfuscating the maximum needs for a digital enterprise. The IoT focus only exposes the last part of the lifecycle, disconnected from the concept and engineering cycles – yes on PowerPoint slides there might be a link. Re-framing PLM as Digital Transformation makes is even vaguer as we discussed during the CIMdata / PDT Europe conference last October. My main argument: Companies fail to have a link with their digital operations and dreams because current engineering processes and data, hardware (mechanical and electronics) combined with software are still operating in an analog, document-driven mode.

PLM = MBSE?

However what we also discussed during this conference was the fact that actually there is a need for an end-to-end model-based systems engineering infrastructure to support the full product lifecycle. Don Farr’s (Boeing) new way to depict the classical systems engineering “V” also hinted into that direction. See the image below – a connected environment between the virtual modeled word and the physical world at any time of the product lifecycle

So could MBSE be the new naming for PLM?

The problem is as Peter Bilello also mentioned during the CIMdata/PDT conference is that the word “ENGINEERING” is in Model-Based Systems Engineering. Therefore keeping the work what the PLM “elite” is doing again in the engineering box.

So perhaps Model-Based Enterprise as the new name?

Unfortunate MBE has already two current definitions – look here and here. Already too much confusion, and there a lot of people who like confusion. See Model-Based – The confusion. So any abbreviation with Model-Based terminology in it will not get attention at the board level. Even if it is crucial the words, Model-Based create less excitement as compared to Digital Twin, although the Digital Twin depends on a model-based approach.

Conclusion

Creating and maintaining unique products and experiences for their customers is the primary target of almost every company. However, no easy acronym that frames these aspects to value at the board level. Perhaps PID – the Product Innovation Diamond approach will be noticed? Your say ….

 

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

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

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

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

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

What is blocking Model-Based Definition?

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

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

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

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

There are a few challenges here:

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

Educate, or our brain will block the future!

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

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

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

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

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

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

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

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

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

Conclusion

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

 

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

 

 

 

Earth GIF - Find & Share on GIPHY

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

Three themes triggered me to write this half-year:

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

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

The changing roles of (PLM) consultancy

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

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

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

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

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

The related post to this topic are:

 

The disruptive effect of digital transformation on legacy PLM

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

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

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

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

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

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

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

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

The Model-driven approaches

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

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

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

 

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

My related posts to model-based this year were:

Conclusion

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

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

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

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

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

Digital Twin to define a new Product/System

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

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

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

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

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

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

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

Digital Twin to match a product/system in the field

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

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

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

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

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

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

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

The second part where PLM gets involved is twofold:

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

Processing data from an individual twin

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

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


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

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

The five potential platforms of a digital enterprise

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

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

To conclude:

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

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

 

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

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

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

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

What is a digital twin?

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

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

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

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

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

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

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

Digital Twin – performance focus

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

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

Image SAP blogs

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

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

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

 

Conclusion

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

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

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