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I am writing this post because one of my PLM peers recently asked me this question: “Is the BOM losing its position? He was in discussion with another colleague who told him:
“If you own the BOM, you own the Product Lifecycle”.
This statement made me think of ä recent post from Jan Bosch recent post: Product Development fallacy #8: the bill of materials has the highest priority.
Software becomes increasingly an essential part of the final product, and combined with Jan’s expertise in software development, he wrote this article. I recommend reading the full post (4 min read) and next browse through the comments.
If you cannot afford these 10 minutes, here is my favorite quote from the article:
An excessive focus on the bill of materials leads to significant challenges for companies that are undergoing a digital transformation and adopting continuous value delivery. The lack of headroom, high coupling and versioning hell may easily cause an explosion of R&D expenditure over time.
Where did the BOM focus come from? A historical overview related to the rise (and fall) of the BOM.
In the beginning, there was the drawing.
Before the era of computers, there was “THE drawing”, describing assemblies, subassemblies or parts. And on the drawing, you can find the parts list if relevant. This parts list was the first Bill of Material, describing the parts/materials shown on the drawing.
Next came MRP/ERP
With the introduction of the MRP system (Material Requirement Planning), it was the first step that by using computers, people could collect the material requirements for one system as data and process. Entering new materials/parts described on drawings was still a manual process, as well as referring to existing parts on the drawing. Reuse of parts was a manual process based on individual knowledge.
In the nineties, MRP evolved into ERP (Enterprise Resource Planning), which included the MRP part and added resource and manufacturing planning and financial reporting.
The ERP system became the most significant IT system, the execution system of the company. As it was the first enterprise system implemented, it was the first moment we learned about implementation challenges – people change and budget overruns. However, as the ERP system brought visibility to the company’s execution, it became a “must-have” system for management.
The introduction of mainstream 2D CAD did not affect the company’s culture so much. Drawings became electronic drawings, and the methodology of the parts list on the drawing remained.
Sometimes the interaction with the MRP/ERP system was enhanced by an interface – sending the drawing BOM to ERP. The advantage of the interface: no manual transfer of data reducing typos and BOM errors. The disadvantages at that time: relatively expensive (connectivity between systems was a challenge) and mostly one direction.
And then there was PDM.
In parallel with the introduction of ERP systems, mainstream 3D CAD systems became affordable, particularly SolidWorks, Solid Edge and Inventor. These 3D CAD systems allow sharing of parts and assemblies in different products, and the PDM database was the first aid to support part reuse, versioning and standardization.
By extracting the parts from the assemblies and subassemblies, it was possible to generate a BOM structure in the PDM system to be transferred or typed into the ERP system. We did not talk about EBOM or MBOM then, as there was only one BOM in the ERP system, and the PDM system was a tool to feed the ERP system.
Many companies still have based their processes on this approach. ERP (read SAP nowadays) is the central execution system, and PDM is an external system. You might remember the story and image from my previous post about people, processes and tools. The bad practice example: Asking the ERP system to provide a part number when starting to design a part.
And then products started to change.
In the early 2000s, I worked with SmarTeam to define the E&E (Electronics and Electrical) template. One of the new concepts was to synchronize all design data coming from different disciplines to a single BOM structure.
It was the time we started to talk about the EBOM. A type of BOM, as the structure to consolidate all the design data, was based on parts.
The EBOM, most of the time, reflects the design intent in logical groups and sending the relevant parts in the correct order to the ERP system was a favorite expensive customization for service providers. How to transfer an engineering BOM view to an ERP system that only understands the manufacturing view?
Note: not all ERP systems have the data model to differentiate between engineering parts and manufacturing parts
The image below illustrates the challenge and the customer’s perception. 
The automated link between the design side (EBOM) and manufacturing side (MBOM) was a mission impossible – too many exceptions for the (spaghetti) code.
And then came the MBOM.
The identified issues connecting PDM and ERP led to the concept of implementing the MBOM in the PLM system. The MBOM in PLM is one of the characteristics of a PLM implementation compared to a PDM implementation. In a traditional PLM system, there is an interaction and connection between the EBOM and MBOM. EBOM parts should end up as MBOM parts. This interaction can be supported by automation, however, as it is in the same system, still leaving manual changes possible.
The MBOM structure in PLM could then be the information structure to transfer to the ERP system; however, there is more, as Jörg W. Fischer wrote in his provoking post-Die MBOM muss weg (The MBOM must go). He rightly points out (in German) that the MBOM is not a structure on its own but a combination of different views based on Assembly Drawings, Process Planning and Material Requirements.
His conclusion:
Calling these structures, MBOM is trying to squeeze all three structures into one. That usually doesn’t work and then leads to much more emotional discussions in the project. It also costs a lot of money. It is, therefore, better not to use the term MBOM at all.
And indeed, just having an MBOM in your PLM system might help you to prepare some of the manufacturing steps, the needed resources and parts. The MBOM result still has to be localized at the local plant where the manufacturing takes place. And here, the systems used are the ERP system and the MES system.
The main advantage of having the MBOM in the PLM system is the direct relation between specification and manufacturing intent, allowing manufacturing engineering to work collaboratively with engineering in the same environment.
- The first benefit is fewer iterations and a shorter time to production, thanks to early interaction and manufacturing involvement in the engineering process.
- The second benefit is: product knowledge is centralized in a single system. Consolidating your Product Knowledge in ERP does not make sense due to global localization and the missing capabilities to manage the iterative engineering processes on non-existing parts.
And then came the SBOM, the xBOM
Traditional PLM vendors and implementations kept using xBOM structures as placeholders for related specification data (mechanical designs, electrical, software deliverables, serialized products). Most of the time, related files.
And with this approach, talking about digital thread, PLM systems also touch on the concepts of Configuration Management.
I will not go into the details here but look at the two images by clicking on them and see a similar mindset.
It is about the traceability of information in structures and systems. These structures work well in a relatively static and linear product development and delivery environment, as illustrated below:
Engineering change and release processes are based on managing the changes in different structures from the left to the right.
And then came software!
Modern connected products are no longer mechanical products. The product’s functionality no longer depends on the mechanical properties but mainly on embedded electronics and software used. For example, look at the mechanical design of a telecom transmission tower – its behavior merely comes from non-mechanical components, and they can change over time. Still, the Bill of Material contains a lot of concrete and steel parts.
The ultimate example is comparing a Tesla (software on wheels) with a traditional car. For modern connected products, electronics and software need to be part of the solution. Software and electronics allow the product to be upgraded over time. Managing these products in the same manner as mechanical products is impossible, inefficient and therefore threatening your company’s future business.
I requote Jan Bosch:
An excessive focus on the bill of materials leads to significant challenges for companies that are undergoing a digital transformation and adopting continuous value delivery. The lack of headroom, high coupling and versioning hell may easily cause an explosion of R&D expenditure over time.
The model-based, connected enterprise
I will not solve the puzzle of the future in this post. You can read my observations in my series: The road to model-based and connected PLM. We need a new infrastructure with at least two modes. One that still serves as a System of Record, storing information in a traditional manner, like a Bill of Materials for the static parts, as not everyone and everything can be connected.
In addition, we need various Systems of Engagement that enable close to real-time interaction between products (systems) and relevant stakeholders for the engagement scope(multidisciplinary / consumers).
Digital twins are examples of such environments. Currently, these Systems of Engagement often work disconnected from the System of Record due to the lack of understanding of how to connect. (standard connectors? / OSLC?)
Our mission is to explore, as I wrote in my post Time to split PLM and drop our mechanical mindset.
And while I was finalizing this post, I read a motivating post from Jan Bosch again for all of you working on understanding and pushing the digital transformation in your eco-system.
The title: Be the protagonist of your life: 15 rules A starting point for more to come.
Conclusion
The BOM is no longer the master of the product lifecycle when it comes to managing connected products, where functionality mainly depends on software. BOM structures with related documents are just one of the extracted baselines from a data-driven, connected enterprise. This traditional PLM infrastructure requires other, non-BOM-driven structures to represent the actual status of a virtual or physical product.
The BOM is not dead, but there is more ………
Your thoughts?
This year started for me with a discussion related to federated PLM. A topic that I highlighted as one of the imminent trends of 2022. A topic relevant for PLM consultants and implementers. If you are working in a company struggling with PLM, this topic might be hard to introduce in your company.
Before going into the discussion’s topics and arguments, let’s first describe the historical context.
The traditional PLM frame.
Historically PLM has been framed first as a system for engineering to manage their product data. So you could call it PDM first. After that, PLM systems were introduced and used to provide access to product data, upstream and downstream. The most common usage was the relation with manufacturing, leading to EBOM and MBOM discussions.
The traditional ENOVIA PLM backbone
IT landscape simplification often led to an infrastructure of siloed solutions – PLM, ERP, CRM and later, MES. IT was driving the standardization of systems and defining interfaces between systems. System capabilities were leading, not the flow of information.
As many companies are still in this stage, I would call it PLM 1.0
PLM 1.0 systems serve mainly as a System of Record for the organization, where disciplines consolidate their data in a given context, the Bills of Information. The Bill of Information then is again the place to connect specification documents, i.e., CAD models, drawings and other documents, providing a Digital Thread.
Aras – Bills of Information creating the Digital Thread
The actual engineering work is done with specialized tools, MCAD/ECAD, CAE, Simulation, Planning tools and more. Therefore, each person could work in their discipline-specific environment and synchronize their data to the PLM system in a coordinated manner.
However, this interaction is not easy for some of the end-users. For example, the usability of CAD integrations with the PLM system is constantly debated.
Many of my implementation discussions with customers were in this context. For example, suppose your products are relatively simple, or your company is relatively small. In that case, the opinion is that the System or Record approach is overkill.
That’s why many small and medium enterprises do not see the value of a PLM backbone.
This could be true till recently. However, the threats to this approach are digitization and regulations.
Customers, partners, and regulators all expect more accurate and fast responses on specific issues, preferably instantly. In addition, sustainability regulations might push your company to implement a System of Record.
PLM as a business strategy
For the past fifteen years, we have discussed PLM more as a business strategy implemented with business systems and an infrastructure designed for sharing. Therefore, I choose these words carefully to avoid overhanging the expression: PLM as a business strategy.
The reason for this prudence is that, in reality, I have seen many PLM implementations fail due to the ambiguity of PLM as a system or strategy. Many enterprises have previously selected a preferred PLM Vendor solution as a starting point for their “PLM strategy”.
One of the most neglected best practices.
In reality, this means there was no strategy but a hope that with this impressive set of product demos, the company would find a way to support its business needs. Instead of people, process and then tools to implement the strategy, most of the time, it was starting with the tools trying to implement the processes and transform the people. That is not really the definition of business transformation.
In my opinion, this is happening because, at the management level, decisions are made based on financials.
Developing a PLM-related business strategy requires management understanding and involvement at all levels of the organization.
This is often not the case; the middle management has to solve the connection between the strategy and the execution. By design, however, the middle management will not restructure the organization. By design, they will collect the inputs van the end users.
And it is clear what end users want – no disruption in their comfortable way of working.
Halfway conclusion:
Rebranding PLM as a business strategy has not really changed the way companies work. PLM systems remain a System of Record mainly for governance and traceability.
To understand the situation in your company, look at who is responsible for PLM.
- If IT is responsible, then most likely, PLM is not considered a business strategy but more an infrastructure.
- If engineering is responsible for PLM, then you are still in the early days of PLM, the engineering tools to be consulted by others upstream or downstream.
Only when PLM accountability is at the upper management level, it might be a business strategy (assume the upper management understands the details)
Connected is the game changer
Connecting all stakeholders in an engagement has been a game changer in the world. With the introduction of platforms and the smartphone as a connected device, consumers could suddenly benefit from direct responses to desired service requests (Spotify, iTunes, Uber, Amazon, Airbnb, Booking, Netflix, …).
The business change: connecting real-time all stakeholders to deliver highly rapid results.
What would be the game changer in PLM was the question? The image below describes the 2014 Accenture description of digital PLM and its potential benefits.
Is connected PLM a utopia?
Marc Halpern from Gartner shared in 2015 the slide below that you might have seen many times before. Digital Transformation is really moving from a coordinated to a connected technology, it seems.
The image below gives an impression of an evolution.
I have been following this concept till I was triggered by a 2017 McKinsey publication: “our insights/toward an integrated technology operating model“.
This was the first notion for me that the future should be hybrid, a combination of traditional PLM (system of record) complemented with teams that work digitally connected; McKinsey called them pods that become product-centric (multidisciplinary team focusing on a product) instead of discipline-centric (marketing/engineering/manufacturing/service)
In 2019 I wrote the post: The PLM migration dilemma supporting the “shocking” conclusion “Don’t think about migration when moving to data-driven, connected ways of working. You need both environments.”
One of the main arguments behind this conclusion was that legacy product data and processes were not designed to ensure data accuracy and quality on such a level that it could become connected data. As a result, converting documents into reliable datasets would be a costly, impossible exercise with no real ROI.
The second argument was that the outside world, customers, regulatory bodies and other non-connected stakeholders still need documents as standardized deliverables.
The conclusion led to the image below.
Systems of Record (left) and Systems of Engagement (right)
Splitting PLM?
In 2021 these thoughts became more mature through various publications and players in the PLM domain.
We saw the upcoming of Systems of Engagement – I discussed OpenBOM, Colab and potentially Configit in the post: A new PLM paradigm. These systems can be characterized as connected solutions across the enterprise and value chain, focusing on a platform experience for the stakeholders.
These are all environments addressing the needs of a specific group of users as efficiently and as friendly as possible.
A System of Engagement will not fit naturally in a traditional PLM backbone; the System of Record.
Erik Herzog with SAAB Aerospace and Yousef Houshmand at that time with Daimler published that year papers related to “Federated PLM” or “The end of monolithic PLM.”. They acknowledged a company needs to focus on more than a single PLM solution. The presentation from Erik Herzog at the PLM Roadmap/PDT conference was interesting because Erik talked about the Systems of Engagement and the Systems of Record. He proposed using OSLC as the standard to connect these two types of PLM.
It was a clear example of an attempt to combine the two kinds of PLM.
And here comes my question: Do we need to split PLM?
When I look at PLM implementations in the field, almost all are implemented as a System of Record, an information backbone proved by a single vendor PLM. The various disciplines deliver their content through interfaces to the backbone (Coordinated approach).
However, there is low usability or support for multidisciplinary collaboration; the PLM backbone is not designed for that.
Due to concepts of Model-Based Systems Engineering (MBSE) and Model-Based Definition (MBD), there are now solutions on the market that allow different disciplines to work jointly related to connected datasets that can be manipulated using modeling software (1D, 2D, 3D, 4D,…).
These environments, often a mix of software and hardware tools, are the Systems of Engagement and provide speedy results with high quality in the virtual world. Digital Twins are running on Systems of Engagements, not on Systems of Records.
Systems of Engagement do not need to come from the same vendor, as they serve different purposes. But how to explain this to your management, who wants simplicity. I can imagine the IT organization has a better understanding of this concept as, at the end of 2015, Gartner introduced the concept of the bimodal approach.
Their definition:
Mode 1 is optimized for areas that are more well-understood. It focuses on exploiting what is known. This includes renovating the legacy environment so it is fit for a digital world. Mode 2 is exploratory, potentially experimenting to solve new problems. Mode 2 is optimized for areas of uncertainty. Mode 2 often works on initiatives that begin with a hypothesis that is tested and adapted during a process involving short iterations.
No Conclusion – but a question this time:
At the management level, unfortunately, there is most of the time still the “Single PLM”-mindset due to a lack of understanding of the business. Clearly splitting your PLM seems the way forward. IT could be ready for this, but will the business realize this opportunity?
What are your thoughts?
This week there was an interesting discussion on LinkedIn initiated by Alex Bruskin from Senticore Technologies. I have known Alex for over 20 years, starting from the SmarTeam days and later through encounters in the PLM space. Alex is a real techie on the outside but also a person with a very creative mind to connect technology to business.
You can see his LinkedIn featured posts here to get an impression.
Where is PLM @ Startups?
This time Alex shared an observation from an event organized by the Pittsburgh Robotics Network, where he spoke with several startups.
His point, and I quote Alex:
Then, I spoke to a number of presenters there, explaining Senticore capabilities and listening to their situation around engineering/ manufacturing.
– many startups offered an add-on to other platforms => an autonomous module for UAV/helicopter/Vehicle. Some offered robotic components or entire robots (robot-dog).
– all startups use #solidworks , and none use #catia or #nx
– none of them have a PLM system nor an MES. I am 90% certain none of them have ERP, either. They all are apparently using #excel for all these purposes.
– only a handful of them are considering getting a PLM system in the near future.
Read the full post here and the comments below to get a broader insight into the topic.
The PLM Doctor knows it all.
The point reminded me of an episode I did together with Helena Gutierrez from Share PLM last year. She asked the same question to the PLM Doctor.
Do you think PLM is only for big corporations or can startups also benefit from it?
You can see the conversation here:
Meanwhile, the PLM Doctor is unemployed due to the lack of incoming questions.
When looking at startups, I could see two paths. One is the traditional path based on historical mechanical PLM, and a second (potential) approach which is based on understanding the future complexity of the startup offering.
There are two paths – path #1
The first evolutionary path you might have seen a few times before in my blog post is the one depicted by Marc Halpern from Gartner in 2015. At that time, we started discussing Product Innovation Platforms and the new generation of PLM. You can see Marc’s slide below, which is still valid for most situations.
In the slide above, you see the startup company on the left side.
Often the main purpose of a startup company is to be visible on the market with their concept as fast as possible. Startups are often driven by a small group of multifunctional people developing a solution. In this approach, there is no place for people and reflection on processes as they are considered overhead.
Only when you target your solution in a strongly regulated environment, e.g., medical devices and aerospace, you need to focus on the process too.
Therefore it is logical that most startup companies focus on the tools to develop their solution. A logical path, as what could you do without tools? Next, the choice of the tools will be, most of the time, driven by the team’s experience and available skills in the market.
Again statistics show it is not likely that advanced tools like NX or CATIA will be chosen for the design part. More likely mid-market products like SolidWorks or Autodesk products. And for data management and reporting, the logical tools are the office tools, Excel, Word and Visio.
And don’t forget PowerPoint to sell the solution.
The role of investors is often also here to question investments that are not clearly understood or relevant at that time.
How a startup scales up very much depends on the choices they make for Repeatable business. This is the moment that a company starts to create its legacy. Processes and best practices need to be established and why you often see is that seasoned people join the company. These people have proven their skills in the past, and most likely, they are willing to repeat this.
And here comes the risk – experienced people come with a much better holistic overview of the product lifecycle aspects. They know what critical steps are needed to move the company to an Integrated business. These experiences are crucial; however, they should not become the new single standard.
Implementing the past is not a guarantee for success in a digital and connected future.
Implementing their past experiences would focus too much on creating a System of Record (PLM 1.0), which is crucial for configuration management, change management and compliance. However, it would also create a productivity dip for those developing the product or solution.
This is the same dilemma that very small and medium enterprises face. They function reasonably well in a Repeatable business. How much should they invest in an Integrated or Collaborating business approach?
Following the evolution path described by Marc Halpern always brings you to the point where technology changes from Coordinated to Connected. This is a challenging and immature topic, which I have discussed in my blog posts and during conferences.
See: The Challenges of a connected ecosystem for PLM or this full series of posts: The road to model-based and connected PLM.
There are two paths – path #2
Another path that startups could follow is a more forward-looking path, understanding that you need a coordinated and connected approach in the long term. For the fastest execution, you would like to work in a multidisciplinary mode in real time, exactly the characteristic of a startup.
However, in path #2, the startup should have a longer-term vision. Instead of choosing the obvious tools, they should focus on their company’s most important value streams. They have the opportunity to select integrated domains that are based on a connected, often model-based approach. Some examples of these integrated domains:
- An MBSE environment focusing on real-time interaction related to product architecture and solution components(RFLP)
- A connected product design environment, where in real-time a virtual product can be created, analyzed, and optimized – connected software might be relevant here.
- A connected product realization environment where product engineering and suppliers work together in real time.
All three examples are typical Systems of Engagement. The big difference with individual tools is that they already focus on multidisciplinary collaboration on a data-driven, model-based approach.
In addition, having these systems in place allows the startup company to invest separately in a System of Record(s) environment when scaling up. This could be a traditional PLM system combined with a Configuration Management System or an Asset Management System.
System of Record choices, of course, depends on the industry needs and the usage of the product in the field. We should not consider one system that serves all; it is an infrastructure.
In the image below, you see the concept of this approach described by Erik Herzog from SAAB Aeronautics during the recent PLM Roadmap / PDT Europe conference. You can read more details of this approach in this post: The Week after PLM Roadmap PDT Europe.
Note: SAAB is not a startup; therefore, they must deal with their legacy. They are now working on business sustainable concepts for the future: Heterogeneous and federated PLM.
My opinion: The heterogeneous and federated approach is the ultimate target for any enterprise. I already mentioned the importance of connected environments regarding digital twins and sustainability. Material properties, process environmental impacts and product behavior coming from the field will all work only efficiently if dealt with in a connected and federated manner.
Conclusion
The challenge for startups is that they often start without the knowledge and experience that multidisciplinary collaboration within a value stream is crucial for a connected future. This a topic that I would like to explore further with startups and peers in my ecosystem. What do you think? What are your questions? Join the conversation.
While preparing my presentation for the Dutch Model-Based Definition solutions event, I had some reflections and experiences discussing Model-Based Definition. Particularly in traditional industries. In the Aerospace & Defense, and Automotive industry, Model-Based Definition has become the standard. However, other industries have big challenges in adopting this approach. In this post, I want to share my observations and bring clarifications about the importance.
What is a Model-Based Definition?
The Wiki-definition for Model-Based Definition is not bad:
Model-based definition (MBD), sometimes called digital product definition (DPD), is the practice of using 3D models (such as solid models, 3D PMI and associated metadata) within 3D CAD software to define (provide specifications for) individual components and product assemblies. The types of information included are geometric dimensioning and tolerancing (GD&T), component level materials, assembly level bills of materials, engineering configurations, design intent, etc.
By contrast, other methodologies have historically required the accompanying use of 2D engineering drawings to provide such details.
When I started to write about Model-Based definition in 2016, the concept of a connected enterprise was not discussed. MBD mainly enhanced data sharing between engineering, manufacturing, and suppliers at that time. The 3D PMI is a data package for information exchange between these stakeholders.
The main difference is that the 3D Model is the main information carrier, connected to 2D manufacturing views and other relevant data, all connected in this package.
MBD – the benefits
There is no need to write a blog post related to the benefits of MBD. With some research, you find enough reasons. The most important benefits of MBD are:
- the information is and human-readable and machine-readable. Allowing the implementation of Smart Manufacturing / Industry 4.0 concepts
- the information relies on processes and data and is no longer dependent on human interpretation. This leads to better quality and error-fixing late in the process.
- MBD information is a building block for the digital enterprise. If you cannot master this concept, forget the benefits of MBSE and Virtual Twins. These concepts don’t run on documents.
To help you discover the benefits of MBD described by others – have a look here:
- What is MBD, and what are its benefits?
- MBD Efficiencies for Small Manufacturers
- 5 reasons to use MBD
- 10 reasons why everyone is moving away from traditional 2D drawings
MBD as a stepping stone to the future
When you are able to implement model-based definition practices in your organization and connect with your eco-system, you are learning what it means to work in a connected matter. Where the scope is limited, you already discover that working in a connected manner is not the same as mandating everyone to work with the same systems or tools. Instead, it is about new ways of working (skills & people), combined with exchange standards (which to follow).
Where MBD is part of the bigger model-based enterprise, the same principles apply for connecting upstream information (Model-Based Systems Engineering) and downstream information(IoT-based operation and service models).
Oleg Shilovitsky addresses the same need from a data point of view in his recent blog: PLM Strategy For Post COVID Time. He makes an important point about the Digital Thread:
Digital Thread is one of my favorite topics because it is leading directly to the topic of connected data and services in global manufacturing networks.
I agree with that statement as the digital thread is like MBD, another steppingstone to organize information in a connected manner, even beyond the scope of engineering-manufacturing interaction. However, Digital Thread is an intermediate step toward a full data-driven and model-based enterprise.
To master all these new ways is working, it is crucial for the management of manufacturing companies, both OEM and their suppliers, to initiate learning programs. Not as a Proof of Concept but as a real-life, growing activity.
Why MBD is not yet a common practice?
If you look at the success of MBD in Aerospace & Defense and Automotive, one of the main reasons was the push from the OEMs to align their suppliers. They even dictated CAD systems and versions to enable smooth and efficient collaboration.
In other industries, there we not so many giant OEMs that could dictate their supply chain. Often also, the OEM was not even ready for MBD. Therefore, the excuse was often we cannot push our suppliers to work different, let’s remain working as best as possible (the old way and some automation)
Besides the technical changes, MBD also had a business impact. Where the traditional 2D-Drawing was the contractual and leading information carrier, now the annotated 3D Model has to become the contractual agreement. This is much more complex than browsing through (paper) documents; now, you need an application to open up the content and select the right view(s) or datasets.
In the interaction between engineering and manufacturing, you could hear statements like:
you can use the 3D Model for your NC programming, but be aware the 2D drawing is leading. We cannot guarantee consistency between them.
In particular, this is a business change affecting the relationship between an OEM and its suppliers. And we know business changes do not happen overnight.
Smaller suppliers might even refuse to work on a Model-Based definition, as it is considered an extra overhead they do not benefit from.
In particular, when working with various OEMs that might have their own preferred MBD package content based on their preferred usage. There are standards; however, OEMs often push for their preferred proprietary format.
It is about an orchestrated change.
Implementing MBD in your company, like PLM, is challenging because people need to be aligned and trained on new ways of working. In particular, this creates resistance at the end-user level.
Similar to the introduction of mainstream CAD (AutoCAD in the eighties) and mainstream 3D CAD (Solidworks in the late nineties), it requires new processes, trained people, and matching tools.
I am aware of learning materials coming from the US, not so much about European or Asian thought leaders. Feel free to add other relevant resources for the readers in this post’s comments. Have a look and talk with:
Action Engineering with their OSCAR initiative: Bringing MBD Within Reach. I spoke with Jennifer Herron, founder of Action Engineering, a year ago about MBD and OSCAR in my blog post: PLM and Model-Based Definition.
Another interesting company to follow is Capvidia. Read their blog post to start with is MBD model-based definition in the 21st century.
The future
What you will discover from these two companies is that they focus on the connected flow of information between companies while anticipating that each stakeholder might have their preferred (traditional) PLM environment. It is about data federation.
The future of a connected enterprise is even more complex. So I was excited to see and download Yousef Hooshmand’s paper: ”From a Monolithic PLM Landscape to a Federated Domain and Data Mesh”.
Yousef and some of his colleagues report about their PLM modernization project @Mercedes-Benz AG, aiming at transforming a monolithic PLM landscape into a federated Domain and Data Mesh.
This paper provides a lot of structured thinking related to the concepts I try to explain to my audience in everyday language. See my The road to model-based and connected PLM thoughts.
This paper has much more depth and is a must-read and must-discuss writing for those interested – perhaps an opportunity for new startups and a threat to traditional PLM vendors.
Conclusion
Vellum drawings are almost gone now – we have electronic 2D Drawings. The model-based definition has confirmed the benefits of improving the interaction between engineering, manufacturing & suppliers. Still, many industries are struggling with this approach due to process & people changes needed. If you are not able or willing to implement a model-based definition approach, be worried about the future. The eco-systems will only run efficiently (and survive) when their information exchange is based on data and models. Start learning now.
p.s. just out of curiosity:
If you are model-based advocate support this post with a 
Once and a while, the discussion pops up if, given the changes in technology and business scope, we still should talk about PLM. John Stark and others have been making a point that PLM should become a profession.
In a way, I like the vagueness of the definition and the fact that the PLM profession is not written in stone. There is an ongoing change, and who wants to be certified for the past or framed to the past?
However, most people, particularly at the C-level, consider PLM as something complex, costly, and related to engineering. Partly this had to do with the early introduction of PLM, which was a little more advanced than PDM.
The focus and capabilities made engineering teams happy by giving them more access to their data. But unfortunately, that did not work, as engineers are not looking for more control.
Old (current) PLM
Therefore, I would like to suggest that when we talk about PLM, we frame it as Product Lifecycle Data Management (the definition). A PLM infrastructure or system should be considered the System of Record, ensuring product data is archived to be used for manufacturing, service, and proving compliance with regulations.
In a modern way, the digital thread results from building such an infrastructure with related artifacts. The digital thread is somehow a slow-moving environment, connecting the various as-xxx structures (As-Designed, As-Planned, As-Manufactured, etc.). Looking at the different PLM vendor images, Aras example above, I consider the digital thread a fancy name for traceability.
I discussed the topic of Digital Thread in 2018: Document Management or Digital Thread. One of the observations was that few people talk about the quality of the relations when providing traceability between artifacts.
The quality of traceability is relevant for traditional Configuration Management (CM). Traditional CM has been framed, like PLM, to be engineering-centric.
Both PLM and CM need to become enterprise activities – perhaps unified.
Read my blog post and see the discussion with Martijn Dullaart, Lisa Fenwick and Maxim Gravel when discussing the future of Configuration Management.
New digital PLM
In my posts, I talked about modern PLM. I described it as data-driven, often in relation to a model-based approach. And as a result of the data-driven approach, a digital PLM environment could be connected to processes outside the engineering domain. I wrote a series of posts related to the potential of such a new PLM infrastructure (The road to model-based and connected PLM)
Digital PLM, if implemented correctly, could serve people along the full product lifecycle, from marketing/portfolio management until service and, if relevant, decommissioning). The bigger challenge is even connecting eco-systems to the same infrastructure, in particular suppliers & partners but also customers. This is the new platform paradigm.
Some years ago, people stated IoT is the new PLM (IoT is the new PLM – PTC 2017). Or MBSE is the foundation for a new PLM (Will MBSE be the new PLM instead of IoT? A discussion @ PLM Roadmap conference 2018).
Even Digital Transformation was mentioned at that time. I don’t believe Digital Transformation is pointing to a domain, more to an ongoing process that most companies have t go through. And because it is so commonly used, it becomes too vague for the specifics of our domain. I liked Monica Schnitger‘s LinkedIn post: Digital Transformation? Let’s talk. There is enough to talk about; we have to learn and be more specific.
What is the difference?
The challenge is that we need more in-depth thinking about what a “digital transformed” company would look like. What would impact their business, their IT infrastructure, and their organization and people? As I discussed with Oleg Shilovitsky, a data-driven approach does not necessarily mean simplification.
I just finished recording a podcast with Nina Dar while writing this post. She is even more than me, active in the domain of PLM and strategic leadership toward a digital and sustainable future. You can find the pre-announcement of our podcast here (it was great fun to talk), and I will share the result later here too.
What is clear to me is that a new future data-driven environment becomes like a System of Engagement. You can simulate assumptions and verify and qualify trade-offs in real-time in this environment. And not only product behavior, but you can also simulate and analyze behaviors all along the lifecycle, supporting business decisions.
This is where I position the digital twin. Modern PLM infrastructures are in real-time connected to the business. Still, PLM will have its system of record needs; however, the real value will come from the real-time collaboration.
The traditional PLM consultant should transform into a business consultant, understanding technology. Historically this was the opposite, creating friction in companies.
Starting from the business needs
In my interactions with customers, the focus is no longer on traditional PLM; we discuss business scenarios where the company will benefit from a data-driven approach. You will not obtain significant benefits if you just implement your serial processes again in a digital PLM infrastructure.
Efficiency gains are often single digit, where new ways of working can result in double-digit benefits or new opportunities.
Besides traditional pressure on companies to remain competitive, there is now a new additional driver that I have been discussing in my previous post, the Innovation Dilemma. To survive on our planet, we and therefore also companies, need to switch to sustainable products and business models.
This is a push for innovation; however, it requires a coordinated, end-to-end change within companies.
Be the change
Interesting to read is this article from Jan Bosch that I read this morning: Resistance to Change. Read the article as it makes so much sense, but we need more than sense – we need people to get involved. My favorite quote from the article:
“The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore, all progress depends on the unreasonable man”.
Conclusion
PLM consultants should retrain themselves in System Thinking and start from the business. PLM technology alone is no longer enough to support companies in their (digital/sustainable) transformation. Therefore, I would like to introduce BLM (Business Lifecycle Management) as the new TLA.
However, BLM has been already framed as Black Lives Matter. I agree with that, extending it to ALM (All Lives Matter).
What do you think should we leave the comfortable term PLM behind us for a new frame?
Yes, it is not a typo. Clayton Christensen famous book written in 1995 discussed the Innovator’s Dilemma when new technologies cause great firms to fail. This was the challenge two decades ago. Existing prominent companies could become obsolete quickly as they were bypassed by new technologies.
The examples are well known. To mention a few: DEC (Digital Equipment Corporation), Kodak, and Nokia.
Why the innovation dilemma?
This decade the challenge has become different. All companies are forced to become more sustainable in the next ten years. Either pushed by global regulations or because of their customer demands. The challenge is this time different. Besides the priority of reducing greenhouse gas emissions, there is also the need to transform our society from a linear, continuous growth economy into a circular doughnut economy.
The circular economy makes the creation, the usage and the reuse of our products more complex as the challenge is to reduce the need for raw materials and avoid landfills.
The circular economy concept – the regular product lifecycle in the middle
The doughnut economy makes the values of an economy more complex as it is not only about money and growth, human and environmental factors should also be considered.
Doughnut Economics: Trying to stay within the green boundaries
To manage this complexity, I wrote SYSTEMS THINKING – a must-have skill in the 21st century, focusing on the logical part of the brain. In my follow-up post, Systems Thinking: a second thought, I looked at the human challenge. Our brain is not rational and wants to think fast to solve direct threats. Therefore, we have to overcome our old brains to make progress.
An interesting and thought-provoking was shared by Nina Dar in this discussion, sharing the video below. The 17 Sustainability Development Goals (SDGs) describe what needs to be done. However, we also need the Inner Development Goals (IDGs) and the human side to connect. Watch the movie:
Our society needs to change and innovate; however, we cannot. The Innovation Dilemma.The future is data-driven and digital.
What is clear to me is that companies developing products and services have only one way to move forward: becoming data-driven and digital.
Why data-driven and digital?
Let’s look at something companies might already practice, REACH (Registration, Evaluation, Authorization and Restriction of Chemicals). This European directive, introduced in 2007, had the aim to protect human health and protect the environment by communicating information on chemicals up and down the supply chain. This would ensure that manufacturers, importers, and their customers are aware of information relating to the health and safety of the products supplied.
The regulation is currently still suffering in execution as most of the reporting and evaluation of chemicals is done manually. Suppliers report their chemicals in documents, and companies report the total of chemicals in their summary reports. Then, finally, authorities have to go through these reports.
Where the scale of REACH is limited, the manual effort to have end-to-end reporting is relatively high. In addition, skilled workers are needed to do the job because reporting is done in a document-based manner.
Life Cycle Assessments (LCA)
Where you might think REACH is relatively simple, the real new challenges for companies are the need to perform Life Cycle Assessments for their products. In a Life Cycle Assessment. The Wiki definition of LCA says:
Life cycle assessment or LCA (also known as life cycle analysis) is a methodology for assessing environmental impacts associated with all the stages of the life cycle of a commercial product, process, or service. For instance, in the case of a manufactured product, environmental impacts are assessed from raw material extraction and processing (cradle), through the product’s manufacture, distribution and use, to the recycling or final disposal of the materials composing it (grave)
This will be a shift in the way companies need to define products. Much more thinking and analysis are required in the early design phases. Before committing to a physical solution, engineers and manufacturing engineers need to simulate and calculate the impact of their design decisions in the virtual world.
This is where the digital twin of the design and the digital twin of the manufacturing process becomes relevant. And remember: Digital Twins do not run on documents – you need connected data and various types of models to calculate and estimate the environmental impact.
LCA done in a document-based manner will make your company too slow and expensive.
I described this needed transformation in my series from last year: The road to model-based and connected PLM – nine posts exploring the technology and concept of a model-based, data-driven PLM infrastructure.
Digital Product Passport (DPP)
The European Commission has published an action plan for the circular economy, one of the most important building blocks of the European Green Deal. One of the defined measures is the gradual introduction of a Digital Product Passport (DPP). As the quality of an LCA depends on the quality and trustworthy information about products and materials, the DPP is targeting to ensure circular economy metrics become reliable.
This will be a long journey. If you want to catch a glimpse of the complexity, read this Medium article: The digital product passport and its technical implementation related to the DPP for batteries.
The innovation dilemma
Suppose you agree with my conclusion that companies need to change their current product or service development into a data-driven and model-based manner. In that case, the question will come up: where to start?
Becoming data-driven and model-based, of course, is not the business driver. However, this change is needed to be able to perform Life Cycle Assessments and comply with current and future regulations by remaining competitive.
A document-driven approach is a dead-end.
Now let’s look at the real dilemmas by comparing a startup (clean sheet / no legacy) and an existing enterprise (experience with the past/legacy). Is there a winning approach?
The Startup
Having lived in Israel – the nation where almost everyone is a startup – and working with startups afterward in the past 10 years, I always get inspired by these people’s energy in startup companies. They have a unique value proposition most of the time, and they want to be visible on the market as soon as possible.
This approach is the opposite of systems thinking. It is often a very linear process to deliver this value proposition without exploring the side effects of such an approach.
For example, the new “green” transportation hype. Many cities now have been flooded with “green” scooters and electric bikes to promote transportation as a service. The idea behind this concept is that citizens do not require to own polluting motorbikes or cars anymore, and transportation means will be shared. Therefore, the city will be cleaner and greener.
However, these “green” vehicles are often designed in the traditional linear way. Is there a repair plan or a plan to recycle the batteries? Reuse of materials used.? Most of the time, not. Please, if you have examples contradicting my observations, let me know. I like to hear good news.
When startup companies start to scale, they need experts to help them grow the company. Often these experts are seasoned people, perhaps close to retirement. They will share their experience and what they know best from the past: traditional linear thinking.
As a result, even though startup companies can start with a clean sheet, their focus on delivering the product or service blocks further thinking. Instead, the seasoned experts will drive the company towards ways of working they know from the past.
Out of curiosity: Do you know or work in a startup that has started with a data-driven and model-based vision from scratch? Please add the name of this company in the comments, and let’s learn how they did it.
The Existing company
Working in an established company is like being on board a big tanker. Changing its direction takes a clear eye on the target and navigation skills to come there. Unfortunately, most of the time, these changes take years as it is impossible to switch the PLM infrastructure and the people skills within a short time.
From the bimodal approach in 2015 to the hybrid approach for companies, inspired by this 2017 McKinsey article: Toward an integrated technology operating model, I discovered that this is probably the best approach to ensure a change will happen. In this approach – see image – the organization keeps running on its document-driven PLM infrastructure. This type of infrastructure becomes the system of record. Nothing different from what PLM currently is in most companies.
In parallel, you have to start with small groups of people who independently focus on a new product, a new service. Using the model-based approach, they work completely independently from the big enterprise in a data-driven approach. Their environment can be considered the future system of engagement.
The data-driven approach allows all disciplines to work in a connected, real-time manner. Mastering the new ways of working is usually the task of younger employees that are digital natives. These teams can be completed by experienced workers who behave as coaches. However, they will not work in the new environment; these coaches bring business knowledge to the team.
People cannot work in two modes, but organizations can. As you can see from the McKinsey chart, the digital teams will get bigger and more important for the core business over time. In parallel, when their data usage grows, more and more data integration will occur between the two operation modes. Therefore, the old PLM infrastructure can remain a System of Record and serve as a support backbone for the new systems of engagement.
The Innovation Dilemma conclusion
The upcoming ten years will push organizations to innovate their ways of working to become sustainable and competitive. As discussed before, they must learn to work in a data-driven, connected manner. Both startups and existing enterprises have challenges – they need to overcome the “thinking fast and acting slow” mindset. Do you see the change in your company?
Note: Before publishing this post, I read this interesting and complementary post from Jan Bosch Boost your digitalization: instrumentation.
It is in the air – grab it.
In my previous post, “My PLM Bookshelf,” on LinkedIn, I shared some of the books that influenced my thinking related to PLM. As you can see in the LinkedIn comments, other people added their recommendations for PLM-related books to get inspired or more knowledgeable.
Where reading a book is a personal activity, now I want to share with you how to get educated in a more interactive manner related to PLM. In this post, I talk with Peter Bilello, President & CEO of CIMdata. If you haven’t heard about CIMdata and you are active in PLM, more to learn on their website HERE. Now let us focus on Education.
CIMdata
Peter, knowing CIMdata from its research valid for the whole PLM community, I am curious to learn what is the typical kind of training CIMdata is providing to their customers.
Jos, throughout much of CIMdata’s existence, we have delivered educational content to the global PLM industry. With a core business tenant of knowledge transfer, we began offering a rich set of PLM-related tutorials at our North American and pan-European conferences starting in the earlier 1990s.
Since then, we have expanded our offering to include a comprehensive set of assessment-based certificate programs in a broader PLM sense. For example, systems engineering and digital transformation-related topics. In total, we offer more than 30 half-day classes. All of which can be delivered in-person as a custom configuration for a specific client and through public virtual-live or in-person classes. We have certificated more than 1,000 PLM professionals since the introduction in 2009 of this PLM Leadership offering.
Based on our experience, we recommend that an organization’s professional education strategy and plans address the organization’s specific processes and enabling technologies. This will help ensure that it drives the appropriate and consistent operations of its processes and technologies.
For that purpose, we expanded our consulting offering to include a comprehensive and strategic digital skills transformation framework. This framework provides an organization with a roadmap that can define the skills an organization’s employees need to possess to ensure a successful digital transformation.
In turn, this framework can be used as an efficient tool for the organization’s HR department to define its training and job progression programs that align with its overall transformation.
The success of training
We are both promoting the importance of education to our customers. Can you share with us an example where Education really made a difference? Can we talk about ROI in the context of training?
Jos, I fully agree. Over the years, we have learned that education and training are often minimized (i.e., sub-optimized). This is unfortunate and has usually led to failed or partially successful implementations.
In our view, both education and training are needed, along with strong organizational change management (OCM) and a quality assurance program during and after the implementation.
In our terms, education deals with the “WHY” and training with the “HOW”. Why do we need to change? Why do we need to do things differently? And then “HOW” to use new tools within the new processes.
We have seen far too many failed implementations where sub-optimized decisions were made due to a lack of understanding (i.e., a clear lack of education). We have also witnessed training and education being done too early or too late.
This leads to a reduced Return on Investment (ROI).
Therefore a well-defined skills transformation framework is critical for any company that wants to grow and thrive in the digital world. Finally, a skills transformation framework needs to be tied directly to an organization’s digital implementation roadmap and structure, state of the process, and technology maturity to maximize success.
Training for every size of the company?
When CIMdata conducts PLM training, is there a difference, for example, when working with a big global enterprise or a small and medium enterprise?
You might think the complexity might be similar; however, the amount of internal knowledge might differ. So how are you dealing with that?
We basically find that the amount of training/education required mostly depends on the implementation scope. Meaning the scope of the proposed digital transformation and the current maturity level of the impacted user community.
It is important to measure the current maturity and establish appropriate metrics to measure the success of the training (e.g., are people, once trained, using the tools correctly).
CIMdata has created a three-part PLM maturity model that allows an organization to understand its current PLM-related organizational, process, and technology maturity.
The three-part PLM maturity model
The PLM maturity model provides an important baseline for identifying and/or developing the appropriate courses for execution.
This also allows us, when we are supporting the definition of a digital skills transformation framework, to understand how the level of internal knowledge might differ within and between departments, sites, and disciplines. All of which help define an organization-specific action plan, no matter its size.
Where is CIMdata training different?
Most of the time, PLM implementers offer training too for their prospects or customers. So, where is CIMdata training different?
For this, it is important to differentiate between education and training. So, CIMdata provides education (the why) and training and education strategy development and planning.
We don’t provide training on how to use a specific software tool. We believe that is best left to the systems integrator or software provider.
While some implementation partners can develop training plans and educational strategies, they often fall short in helping an organization to effectively transform its user community. Here we believe training specialists are better suited.
Digital Transformation and PLM
One of my favorite topics is the impact of digitization in the area of product development. CIMdata introduced the Product Innovation Platform concept to differentiate from traditional PDM/PLM. Who needs to get educated to understand such a transformation, and what does CIMdata contribute to this understanding.
We often start with describing the difference between digitalization and digitization. This is crucial to be understood by an organization’s management team. In addition, management must understand that digitalization is an enterprise initiative.
It isn’t just about product development, sales, or enabling a new service experience. It is about maximizing a company’s ROI in applying and leveraging digital as needed throughout the organization. The only way an organization can do this successfully is by taking an end-to-end approach.
The Product Innovation Platform is focused on end-to-end product lifecycle management. Therefore, it must work within the context of other enterprise processes that are focused on the business’s resources (i.e., people, facilities, and finances) and on its transactions (e.g., purchasing, paying, and hiring).
As a result, an organization must understand the interdependencies among these domains. If they don’t, they will ultimately sub-optimize their investment. It is these and other important topics that CIMdata describes and communicates in its education offering.
The Product Innovation Platform in a digital enterprise
More than Education?
As a former teacher, I know that a one-time education, a good book or slide deck, is not enough to get educated. How does CIMdata provide a learning path or coaching path to their customers?
Jos, I fully agree. Sustainability of a change and/or improved way of working (i.e., long-term sustainability) is key to true and maximized ROI. Here I am referring to the sustainability of the transformation, which can take years.
With this, organizational change management (OCM) is required. OCM must be an integral part of a digital transformation program and be embedded into a program’s strategy, execution, and long-term usage. That means training, education, communication, and reward systems all have to be managed and executed on an ongoing basis.
For example, OCM must be executed alongside an organization’s digital skills transformation program. Our OCM services focus on strategic planning and execution support. We have found that most companies understand the importance of OCM, often don’t fully follow through on it.
A model-based future?
During the CIMdata Roadmap & PDT conferences, we have often discussed the importance of Model-Based Systems Engineering methodology as a foundation of a model-based enterprise. What do you see? Is it only the big Aerospace and Defense companies that can afford this learning journey, or should other industries also invest? And if yes, how to start.
Jos, here I need to step back for a minute. All companies have to deal with increasing complexity for their organization, supply chain, products, and more.
So, to optimize its business, an organization must understand and employ systems thinking and system optimization concepts. Unfortunately, most people think of MBSE as an engineering discipline. This is unfortunate because engineering is only one of the systems of systems that an organization needs to optimize across its end-to-end value streams.
The reality is all companies can benefit from MBSE. As long as they consider optimization across their specific disciplines, in the context of their products and services and where they exist within their value chain.
The MBSE is not just for Aerospace and Defense companies. Still, a lot can be learned from what has already been done. Also, leading automotive companies are implementing and using MBSE to design and optimize semi- and high-automated vehicles (i.e., systems of systems).
The starting point is understanding your systems of systems environment and where bottlenecks exist.
There should be no doubt, education is needed on MBSE and how MBSE supports the organization’s Model-Based Enterprise requirements.
Published work from the CIMdata administrated A&D PLM Action Group can be helpful. Also, various MBE and systems engineering maturity models, such as one that CIMdata utilizes in its consulting work.
Want to learn more?
Thanks, Peter, for sharing your insights. Are there any specific links you want to provide to get educated on the topics discussed? Perhaps some books to read or conferences to visit?
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Jos, as you already mentioned:
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- the CIMdata Roadmap & PDT conferences have provided a wealth of insight into this market for more than 25 years.
[Jos: Search for my blog posts starting with the text: “The weekend after ….”] - In addition, there are several blogs, like yours, that are worth following, and websites, like CIMdata’s pages for education or other resources which are filled with downloadable reading material.
- Additionally, there are many user conferences from PLM solution providers and third-party conferences, such as those hosted by the MarketKey organization in the UK.
These conferences have taken place in Europe and North America for several years. Information exchange and formal training and education are offered in many events. Additionally, they provide an excellent opportunity for networking and professional collaboration.
What I learned
Talking with Peter made me again aware of a few things. First, it is important to differentiate between education and training. Where education is a continuous process, training is an activity that must take place at the right time. Unfortunately, we often mix those two terms and believe that people are educated after having followed a training.
Secondly, investing in education is as crucial as investing in hard- or software. As Peter mentioned:
We often start with describing the difference between digitalization and digitization. This is crucial to be understood by an organization’s management team. In addition, management must understand that digitalization is an enterprise initiative.
System Thinking is not just an engineering term; it will be a mandate for managing a company, a product and even a planet into the future
Conclusion
This time a quote from Albert Einstein, supporting my PLM coaching intentions:
“Education is not the learning of facts
but the training of the mind to think.”
After two quiet weeks of spending time with my family in slow motion, it is time to start the year.
First of all, I wish you all a happy, healthy, and positive outcome for 2022, as we need energy and positivism together. Then, of course, a good start is always cleaning up your desk and only leaving the relevant things for work on the desk.
Still, I have some books at arm’s length, either physical or on my e-reader, that I want to share with you – first, the non-obvious ones:
The Innovators Dilemma
A must-read book was written by Clayton Christensen explaining how new technologies can overthrow established big companies within a very short period. The term Disruptive Innovation comes up here. Companies need to remain aware of what is happening outside and ready to adapt to your business. There are many examples even recently where big established brands are gone or diminished in a short period.
In his book, he wrote about DEC (Digital Equipment Company) market leader in minicomputers, not having seen the threat of the PC. Or later Blockbuster (from video rental to streaming), Kodak (from analog photography to digital imaging) or as a double example NOKIA (from paper to market leader in mobile phones killed by the smartphone).
The book always inspired me to be alert for new technologies, how simple they might look like, as simplicity is the answer at the end. I wrote about in 2012: The Innovator’s Dilemma and PLM, where I believed cloud, search-based applications and Facebook-like environments could disrupt the PLM world. None of this happened as a disruption; these technologies are now, most of the time, integrated by the major vendors whose businesses are not really disrupted. Newcomers still have a hard time to concur marketspace.
In 2015 I wrote again about this book, The Innovator’s dilemma and Generation change. – image above. At that time, understanding disruption will not happen in the PLM domain. Instead, I predict there will be a more evolutionary process, which I would later call: From Coordinated to Connected.
The future ways of working address the new skills needed for the future. You need to become a digital native, as COVID-19 pushed many organizations to do so. But digital native alone does not bring success. We need new ways of working which are more difficult to implement.
Sapiens
The book Sapiens by Yuval Harari made me realize the importance of storytelling in the domain of PLM and business transformation. In short, Yuval Harari explains why the human race became so dominant because we were able to align large groups around an abstract theme. The abstract theme can be related to religion, the power of a race or nation, the value of money, or even a brand’s image.
The myth (read: simplified and abstract story) hides complexity and inconsistencies. It allows everyone to get motivated to work towards one common goal. A Yuval says: “Fiction is far more powerful because reality is too complex”.
Too often, I have seen well-analyzed PLM projects that were “killed” by management because it was considered too complex. I wrote about this in 2019 PLM – measurable or a myth? claiming that the real benefits of PLM are hard to predict, and we should not look isolated only to PLM.
My 2020 follow-up post The PLM ROI Myth, eludes to that topic. However, even if you have a soundproof business case at the management level, still the myth might be decisive to justify the investment.
That’s why PLM vendors are always working on their myths: the most cost-effective solution, the most visionary solution, the solution most used by your peers and many other messages to influence your emotions, not your factual thinking. So just read the myths on their websites.
If you have no time to read the book, look at the above 2015 Ted to grasp the concept and use it with a PLM -twisted mind.
Re-use your CAD
In 2015, I read this book during a summer holiday (meanwhile, there is a second edition). Although it was not a PLM book, it was helping me to understand the transition effort from a classical document-driven enterprise towards a model-based enterprise.
Jennifer Herron‘s book helps companies to understand how to break down the (information) wall between engineering and manufacturing.
At that time, I contacted Jennifer to see if others like her and Action Engineering could explain Model-Based Definition comprehensively, for example, in Europe- with no success.
As the Model-Based Enterprise becomes more and more the apparent future for companies that want to be competitive or benefit from the various Digital Twin concepts. For that reason, I contacted Jennifer again last year in my post: PLM and Model-Based Definition.
As you can read, the world has improved, there is a new version of the book, and there is more and more information to share about the benefits of a model-based approach.
I am still referencing Action Engineering and their OSCAR learning environment for my customers. Unfortunately, many small and medium enterprises do not have the resources and skills to implement a model-based environment.
Instead, these companies stay on their customers’ lowest denominator: the 2D Drawing. For me, a model-based definition is one of the first steps to master if your company wants to provide digital continuity of design and engineering information towards manufacturing and operations. Digital twins do not run on documents; they require model-based environments.
The book is still on my desk, and all the time, I am working on finding the best PLM practices related to a Model-Based enterprise.
It is a learning journey to deal with a data-driven, model-based environment, not only for PLM but also for CM experts, as you might have seen from my recent dialogue with CM experts: The future of Configuration Management.
Products2019
This book was an interesting novelty published by John Stark in 2020. John is known for his academic and educational books related to PLM. However, during the early days of the COVID-pandemic, John decided to write a novel. The novel describes the learning journey of Jane from Somerset, who, as part of her MBA studies, is performing a research project for the Josef Mayer Maschinenfabrik. Her mission is to report to the newly appointed CEO what happens with the company’s products all along the lifecycle.
Although it is not directly a PLM book, the book illustrates the complexity of PLM. It Is about people and culture; many different processes, often disconnected. Everyone has their focus on their particular discipline in the center of importance. If you believe PLM is all about the best technology only, read this book and learn how many other aspects are also relevant.
I wrote about the book in 2020: Products2019 – a must-read if you are new to PLM if you want to read more details. An important point to pick up from this book is that it is not about PLM but about doing business.
PLM is not a magical product. Instead, it is a strategy to support and improve your business.
System Lifecycle Management
Another book, published a little later and motivated by the extra time we all got during the COVID-19 pandemic, was Martin Eigner‘s book System Lifecycle Management.
A 281-page journey from the early days of data management towards what Martin calls System Lifecycle Management (SysLM). He was one of the first to talk about System Lifecycle Management instead of PLM.
I always enjoyed Martin’s presentations at various PLM conferences where we met. In many ways, we share similar ideas. However, during his time as a professor at the University of Kaiserslautern (2003-2017), he explored new concepts with his students.
I briefly mentioned the book in my series The road to model-based and connected PLM (Part 5) when discussing SLM or SysLM. His academic research and analysis make this book very valuable. It takes you in a very structured way through the times that mechatronics becomes important, next the time that systems (hardware and software) become important.
We discussed in 2015 the applicability of the bimodal approach for PLM. However, as many enterprises are locked in their highly customized PDM/PLM environments, their legacy blocks the introduction of modern model-based and connected approaches.
Where John Stark’s book might miss the PLM details, Martin’s book brings you everything in detail and with all its references.
It is an interesting book if you want to catch up with what has happened in the past 20 years.
More Books …..
More books on my desk have helped me understand the past or that helped me shape the future. As this is a blog post, I will not discuss more books this time reaching my 1500 words.
Still books worthwhile to read – click on their images to learn more:
I discussed this book two times last year. An introduction in PLM and Modularity and a discussion with the authors and some readers of the book: The Modular Way – a follow-up discussion
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A book I read this summer contributed to a better understanding of sustainability. I mentioned this book in my presentation for the Swedish CATIA Forum in October last year – slide 29 of
System Thinking becomes crucial for a sustainable future, as I addressed in my post PLM and Sustainability.
Sustainability is my area of interest at the PLM Green Global Alliance, an international community of professionals working with Product Lifecycle Management (PLM) enabling technologies and collaborating for a more sustainable decarbonized circular economy.
Conclusion
There is a lot to learn. Tell us something about your PLM bookshelf – which books would you recommend. In the upcoming posts, I will further focus on PLM education. So stay tuned and keep on learning.
Image http://www.mdux.net
As promised in my early November post – The road to model-based and connected PLM (part 9 – CM), I come back with more thoughts and ideas related to the future of configuration management. Moving from document-driven ways of working to a data-driven and model-based approach fundamentally changes how you can communicate and work efficiently.
Let’s be clear: configuration management’s target is first of all about risk management. Ensuring your company’s business remains sustainable, efficient, and profitable.
By providing the appropriate change processes and guidance, configuration management either avoids costly mistakes and iterations during all phases of a product lifecycle or guarantees the quality of the product and information to ensure safety.
Companies that have not implemented CM practices probably have not observed these issues. Or they have not realized that the root cause of these issues is a lack of CM.
Similar to what is said in smaller companies related to PLM, CM is often seen as an overhead, as employees believe they thoroughly understand their products. In addition, CM is seen as a hurdle to innovation because of the standardization of practices. So yes, they think it is normal that there are sometimes problems. That’s life.
I already wrote about this topic in 2010 PLM, CM and ALM – not sexy 😦 – where ALM means Asset Lifecycle Management – my focus at that time.
Hear it from the experts
To shape the discussion related to the future of Configuration Management, I had a vivid discussion with three thought leaders in this field: Lisa Fenwick, Martijn Dullaart and Maxime Gravel. A short introduction of the three of them:
Lisa Fenwick, VP Product Development at CMstat, a leading company in Configuration Management and Data Management software solutions and consulting services for aviation, aerospace & defense, marine, and other high-tech industries. She has over 25 years of experience with CM and Deliverables Management, including both government and commercial environments.
Ms. Fenwick has achieved CMPIC SME, CMPIC CM Assessor, and CMII-C certifications. Her experience includes implementing CM software products, CM-related consulting and training, and participation in the SAE and IEEE standards development groups
Martijn Dullaart is the Lead Architect for Enterprise Configuration Management at ASML (Our Dutch national pride) and chairperson of the Industry 4.0 committee of the Institute Process Excellence (IPX) Congress. Martijn has his own blog mdux.net, and you might have seen him recently during the PLM Roadmap & PDT Fall conference in November – his thoughts about the CM future can be found on his blog here
Maxime Gravel, Manager Model-Based Engineering at Moog Inc., a worldwide designer, manufacturer, and integrator of advanced motion control products. Max has been the director of the model-based enterprise at the Institute for Process Excellence (IPX) and Head of Configuration and Change Management at Gulfstream Aerospace which certified the first aircraft in a 3D Model-Based Environment.
What we discussed:
We had an almost one-hour discussion related to the following points:
- The need for Enterprise Configuration Management – why and how
- The needed change from document-driven to model-based – the impact on methodology and tools
- The “neural network” of data – connecting CM to all other business domains, a similar view as from the PLM domain,
I kept from our discussion the importance of planning – as seen in the CMstat image on the left.
To plan which data you need to manage and how you will manage the data. How often are you doing this in your company’s projects?
Next, all participants stressed the importance of education and training on this topic – get educated. Configuration Management is not a topic that is taught at schools. Early next year, I will come back on education as the benefits of education are often underestimated. Not everything can be learned by “googling.”
Conclusion
The journey towards a model-based and data-driven future is not a quick one to be realized by new technologies. However, it is interesting to learn that the future of connected data (the “neural network”) allows organizations to implement both CM and PLM in a similar manner, using graph databases and automation. When executed at the enterprise level, the result will be that CM and PLM become natural practices instead of other siloed system-related disciplines.
Most of the methodology is there; the implementation to make it smooth and embedded in organizations will be the topics to learn. Join us in discussing and learning!
In my previous post, I discovered that my header for this series is confusing. Although a future implementation of system lifecycle management (SLM/PLM) will rely on models, the most foundational change needed is a technical one to create a data-driven infrastructure for connected ways of working.
My previous article discussed the concept of the dataset, which led to interesting discussions on LinkedIn and in my personal interactions. Also, this time Matthias Ahrens (HELLA) shared again a relevant but very academic article in this context – how to harmonize company information.
For those who want to dive deeper into the concept of connected datasets, read this article: The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information.
The article illustrates that the topic is relevant for all larger enterprises (and it is not an easy topic).
This time I want to share my thoughts about the two statements from my introductory post, i.e.:
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.
A model-based approach with connected datasets
We discussed connected datasets in the previous post; now, let’s explore why models and datasets are related. In the traditional CAD-centric PLM domain, most people will associate the word model with a CAD model, to be more precise, the 3D CAD Model. However, there are many other types of models used related to product development, delivery and operations.
A model can be a:
Physical Model
- A smaller-scale object for the first analysis, e.g., a city or building model, an airplane model
Conceptual Model
- A conceptual model describes the entities and their relations, e.g., a Process Flow Diagram (PFD)
A mathematical model describes a system concept using a mathematical language, e.g., weather or climate models. Modelica and MATLAB would fall in this category- A CGI (Computer Generated Imagery) or 3D CAD model is probably the most associated model in the mind of traditional PLM practitioners
- Functional and Logical Models describing the services and components of a system are crucial in an MBSE
Operational Model
- A model providing performance analysis based on (real-time) data coming from selected data sources. It could be an operational business model, an asset performance model; even my Garmin’s training performance model is such an operating model.
The list of all models above is not extensive nor academically defined. Moreover, some model term definitions might overlap, e.g., where would we classify software models or manufacturing models?
All models are a best-so-far approach to describing reality. Based on more accurate data from observations or measurements, the model comes closer to what happens in reality.
A model and its data
Never blame the model when there is a difference between what the model predicts and the observed reality. It is still a model. That’s why we need feedback loops from the actual physical world to the virtual world to fine-tune the model.
Part of what we call Artificial Intelligence is nothing more than applying algorithms to a model. The more accurate data available, the more “intelligent” the artificial intelligence solution will be.
By using data analysis complementary to the model, the model may get better and better through self-learning. Like our human brain, it starts with understanding the world (our model) and collecting experiences (improving our model).
There are two points I would like to highlight for this paragraph:
- A model is never 100 % the same as reality – so don’t worry about deviations. There will always be a difference between virtual predicted and physical measured – most of the time because reality has much more influencing parameters.
- The more qualified data we use in the model, the closer to reality – so focus on accurate (and the right) data for your model. Although, as most of the time, it is impossible to fully model a system, focus on the most significant data sources.
The ultimate goal: THE DIGITAL TWIN
The discussion related to data-driven and the usage of models might feel abstract and complex (and that’s the case). However the term “digital twin” is well known and even used in board rooms.
The great benefits of a digital twin for business operations and for sustainability are promoted by many software vendors and consultancy firms.
My statement and reason for this series of blog posts: Digital Twins do not run on documents, you need to have a data-driven, model-based infrastructure to efficiently benefit from digital twin concepts.
Unfortunate a reliable and sustainable implementation of a digital twin requires more than software – it is a learning journey to connect the right data to the right model.
A puzzle every company has to solve as there is no 100 percent blueprint at this time.
Are Low Code platforms the answer?
I mentioned the importance of accurate data. Companies have different systems or even platforms managing enterprise data. The digital dream is that by combining datasets from different systems and platforms, we can provide to any user the needed information in real-time. My statement from my introductory post was:
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?
Let’s look into some of the low-code platform messages mentioned by Low-Code advocates:
You will have an increasingly hard time finding developers to keep up with global app development demands (reason #1 for PEGA)
This statement reminded me of the early days of SmarTeam implementations. With a Data model Wizard, a Form Designer, and a Visual Basic COM API, you could create any kind of data management application with SmarTeam. By using its built-in behaviors for document lifecycle management, item lifecycle management, and CAD integrations combined with easy customizations.
The sky was the limit to satisfy end users. No need for an experienced partner or to be a skilled programmer (this was 2003+). SmarTeam was a low-code platform the marketing department would say now.
A lot of my activities between 2003 and 2010 were related fixing the problems related to flexibility, making sense (again) of customizations. I wrote about this in a 2015 post: The importance of a (PLM) data model sharing the experiences of “fixing” issues created to flexibility.
Think first
The challenge is that an enthusiastic team creates a (low code) solution rapidly. Immediate success is celebrated by the people involved. However, the future impact of this solution is often forgotten – we did the job, right?
Documentation and a broader visibility are often lacking when implementing such a solution.
For example, suppose your product data is going to be consumed by another app. In that case, you need to make sure that the information you consume is accurate. On the other hand, perhaps the information was valid when you created the app.
However, if your friendly co-worker has moved on to another job and someone with different data standards becomes responsible for the data you consume, the reliability might fail. So how do you guarantee its quality?
Easy tools have often led to spaghetti, starting from Clipper (the old days), Visual Basic (the less old days) to highly customizable systems (like Aras is promoting) and future low-code platforms (and Aras is there again).
However, the strength of being highly flexible is also the weaknesses if not managed and understood correctly. In particular, in a digital enterprise architecture, you need skilled people who guarantee a reliable anchorage of the solution.
The HBR article When Low-Code/No-Code Development Works — and When It Doesn’t mentions the same point:
There are great benefits from LC/NC software development, but management challenges as well. Broad use of these tools institutionalizes the “shadow IT” phenomenon, which has bedeviled IT organizations for decades — and could make the problem much worse if not appropriately governed. Citizen developers tend to create applications that don’t work or scale well, and then they try to turn them over to IT. Or the person may leave the company, and no one knows how to change or support the system they developed.
The fundamental difference: from coordinated to connected
For the moment, I remain skeptical about the low-code hype, because I have seen this kind of hype before. The most crucial point companies need to understand is that the coordinated world and the connected world are incompatible.
Using new tools based on old processes and existing data is not a digital transformation. Instead, a focus on value streams and their needed (connected) data should lead to the design of a modern digital enterprise, not the optimization and connectivity between organizational siloes.
Before buying a tool (a medicine) to reduce the current pains, imagine your future ways of working, discover what is possible with your existing infrastructure and identify the gaps.
Next, you need to analyze if these gaps are so significant that it requires a technology change. Probably it does, as historically, systems were not designed to share data horizontally in an organization.
In this context, have a look at Lionel Grealou’s s article for Engineering.com:
Data Readiness in the new age of digital collaboration.
Conclusion
We discussed the crucial relation between models and data. Models have only value if they acquire the right and accurate data (exercise 1).
Next, even the simplest development platforms, like low-code platforms, require brains and a long-term strategy (exercise 2) – nothing is simple at this moment in transformational times.
The next and final post in this series will focus on configuration management – a new approach is needed. I don’t have the answers, but I will share some thoughts
A recommended event and an exciting agenda and a good place to validate and share your thoughts.
I will be there and look forward to meeting you at this conference (unfortunate still virtually)
Jos Voskuil's Weblog 
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