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I wrote in my previous posts about the various aspects of a model-based enterprise. In case you missed this post you can find it here: Model-Based an introduction. In this post I will zoom in on the aspects related to the 3D model, probably in the context of PLM, the most anticipated approach.

3D CAD vs 3D CAD Model

At the time 3D CAD was introduced for the mid-market, the main reason why 3D CAD was introduced was to provide a better understanding of the designed product. Visualization and creating cross-sections of the design became easy although the “old” generation of 2D draftsmen had to a challenge to transform their way of working. This lead often to 3D CAD models setup with the mindset to generate 2D Manufacturing drawings,  not taking real benefits from the 3D CAD Model. Let’s first focus on Model-Based Definition.

Model-Based Definition

We talk about Model-Based Definition when the product and manufacturing information is embedded / connected to the 3D CAD model, allowing the same source of information to be used downstream for manufacturing, analysis and inspection. The embedded information normally contains geometric dimensions, annotations, surface finish and material specifications. Instead of generating easy to distribute 2D drawings, you would be using the 3D model now with its embedded information.

According to an eBook, sponsored by SolidWorks and published by Tech-Clarity: “The How-to Guide for Adopting Model-Based Definition MBD”, Tech-Clarity’s research discovered that 33 percent of design time is spent on drawing generation. Imagine you do not need this time anymore to specify manufacturing processes and operations.  Does this mean the design activities can be reduced by 30 % ? Probably not, the time could be used to spend on design alternatives too, at the end contributing to better designs.

Still this is not the reason why companies would move to MBD. Companies that have implemented MBD report fewer manufacturing mistakes/less rework (61 %) – here is where the value becomes visible. In addition, improved communication with suppliers was reported by 50 % of the companies. More clarity in the communication, however as some of the suppliers are not used to MBD either, this excuse is used not to implement MBD. Instead of creating a win-win situation a status-quo is created.

Read the eBook to demystify Model-Based Definition and realize that although it might look like a complex change, within 8 to 9 months the company might have gone through this change, assuming you have found the proper trainers / coaches for that.

When discussion a roadmap towards a digital enterprise, this is one of the “easier” steps to take as it does not force the organization to change their primary processes. They become more efficient, lean and integrated, delivering rapid benefits within a year.

In the same context of MBD, in my post: Digital PLM requires a Model-Based Enterprise I referred to two articles in engineering.com written by Dick Bourke with the support from Jennifer Herron.  The first article: How Model-based Definition Can Fix Your CAD Models digs into more detail and provides additional insights into benefits realizable by implementing MBD. As I am not the expert, I would recommend if you agree on the benefits and necessity for your company’s future, find the right literature. There is a lot of information related to MBD coming from vendors but also vendor-neutral sources. Technology Is not the issue. You just have to study, digest and implement it  with your suppliers.

Beyond MDB using a 3D CAD Model

Although the post gets long, it is crucial to understand that the 3D CAD model should also be built in a more sophisticated manner. Using parameters in the model instead of hard-coded values allows the model to be used and interact with other disciplines in a digital manner.

A parametric model, combined with business rules can be accessed and controlled by other applications in a digital enterprise. In this way, without the intervention of individuals a set of product variants can be managed and not only from the design point of view. Geometry and manufacturing parameters are also connected and accessible. This is one of the concepts where Industry 4.0 is focusing on: intelligent and flexible manufacturing by exchanging parameters

The 3D CAD Model and Simulation

The last (short) part related to the 3D CAD Model is about its relation to simulation. If you do no use simulation together with your 3D CAD Models, you are still designing in the past. No real advantage between 2D and 3D, just better understanding?

In engineering we often talk about Form, Fit and Function – the three dimensions to decide on a change.  With 2D (and 3D without simulation) we manage Form and Fit disconnected from Function. Once we use 3D combined with Simulation we are able to manage these three parameters in relation.

For example, when designing product, first simulations can provide direct feedback on shape and dimension constraints. Where to save material costs, choose from another design solution? The ultimate approach is Generative Design where the Functional constraints and the Fit are the given constraints and the Form is optimized based on artificial intelligence rules.

In case a company has a close relation between 3D Design and Simulation, the concept of Design of Experiments (DOE) will help to find the optimal product constraints. The more integrated the 3D CAD model and the simulation are, the more efficient alternatives can be evaluated and optimized.

Conclusion

In this post we focused on model-based in relation to the 3D CAD Model. Without going to the expert level for each of the topics discussed, I hope it creates the interest and enthusiasm for further investment in model-based practices.  One commonality for all model-based practices: it is about parameters. Parameters provide digital continuity where each discipline (design, simulation, manufacturing) can build upon in almost real-time without the need for people to convert or adjust information. Digital Continuity – one of the characteristics of the future digital enterprise

 

 

 

 

 

 

 

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The recent years I have been mentioning several times addressing the term model-based in the context of a modern, digital enterprise. Posts like: Digital PLM requires a model-based enterprise (Sept 2016) or Item-Centric or Model-Centric (Sept 2017) describe some of the aspects of a model-based approach. And if you follow the PLM vendors in their marketing messages, everyone seems to be looking for a model-based environment.

This is however in big contrast with reality in the field. In February this year I moderated a focus group related to PLM and the Model-Based approach and the main conclusion from the audience was that everyone was looking at it, and only a few started practicing. Therefore, I promised to provide some step-by-step education related to model-based as like PLM we need to get a grip on what it means and how it impacts your company. As I am not an academic person, it will be a little bit like model-based for dummies, however as model-based in all aspects is not yet a wide-spread common practice, we are all learning.

What is a Model?

The word Model has various meanings and this is often the first confusion when people speak about Model-Based. The two main interpretations in the context of PLM are:

  • A Model represents a 3D CAD Model – a virtual definition of a physical product
  • A Model represents a scientific / mathematical model

And although these are the two main interpretations there are more aspects to look at model-based in the context of a digital enterprise. Let’s explore the 3D CAD Model first

The role of the 3D CAD Model in a digital enterprise

Just designing a product in 3D and then generating 2D drawings for manufacturing is not really game-changing and bringing big benefits. 3D Models provide a better understanding of the product, mechanical simulations allow the engineer to discover clashes and/or conflicts and this approach will contribute to a better understanding of the form & fit of a product. Old generations of designers know how to read a 2D drawing and in their mind understand the 3D Model.

Modern generations of designers are no longer trained to start from 2D, so their way of thinking is related 3D modeling. Unfortunate businesses, in particular when acting in Eco-systems with suppliers, still rely on the 2D definition as the legal document.  The 3D Model has brought some quality improvements and these benefits already justify most of the companies to design in 3D, still it is not the revolution a model-based enterprise can bring.

A model-based enterprise has to rely on data, so the 3D Model should rely on parameters that allow other applications to read them. These parameters can contribute to simulation analysis and product optimization or they can contribute to manufacturing. In both cases the parameters provide data continuity between the various disciplines, eliminating the need to create new representations in different formats. I will come back in a future post to the requirements for the 3D CAD model in the context of the model-based enterprise, where I will zoom in on Model-Based Definition and the concepts of Industry 4.0.

The role of mathematical models in a digital enterprise

The mathematical model of a product allows companies to analyze and optimize the behavior of a product. When companies design a product they often start from a conceptual model and by running simulations they can optimize the product and define low-level requirements within a range that optimizes the product performance. The relation between design and simulation in a virtual model is crucial to be as efficient as possible. In the current ways of working, often design and simulation are not integrated and therefore the amount of simulations is relative low, as time-to-market is the key driver to introduce a new product.

In a digital enterprise, design and simulations are linked through parameters, allowing companies to iterate and select the optimal solution for the market quickly. This part is closely related to model-based systems engineering (MBSE) , where the focus is on defining complex systems. In the context of MBSE I will also zoom in on the relation between hardware and software, which at the end will deliver the desired functionality for the customer. Again in this part we will zoom in on the importance of having a parameter model, to ensure digital continuity.

Digital Twin

There is still a debate if the Digital Twin is part of PLM or should be connected to PLM. A digital twin can be based on a set of parameters that represent the product performance in the field. There is no need to have a 3D representation, despite the fact that many marketing videos always show a virtual image to visualize the twin.

Depending on the business desire, there can be various digital twins for the same products in the field, all depending on the parameters that you want to monitor. Again it is about passing parameters, in this case from the field back to R&D and these parameters should be passed in a digital manner. In a future post I will zoom in on the targets and benefits of the digital twin.

Conclusion

There are various aspects to consider related to “model-based”.  The common thread for each of the aspects is related to PARAMETERS.  The more you can work with parameters to connect the various usages of a product/system, the closer you are related to the digital enterprise. The real advantages of a digital enterprise are speed (information available in real-time), end-to-end visibility (as data is not locked in files / closed systems).

PARAMETERS the objects to create digital continuity

 

 

 

 

When PLM – Product Lifecycle Management – was introduced, one of the main drivers was to provide an infrastructure for collaboration and for sharing product information across the whole lifecycle. The top picture shows my impression of what PLM could mean for an organization at that time. The PLM circle was showing a sequential process from concept, through planning, development, manufacturing towards after sales and/or services when relevant. PLM would provide centralization and continuity of data. Through this continuity we could break down the information silos in a company.

Why do we want to break down the silos?

You might ask yourself what is wrong with silos if they perform in a consistent matter? Oleg Shilovitsky recently wrote about it: How PLM can separate data and organization silos.  Read the post for the full details, I will stay at Oleg’s conclusion:

Keep process and organizational silos, but break data silos. This is should be a new mantra by new PLM organization in 21st century. How to help designers, manufacturing planners and support engineers to stay on the same BOM? By resolving this problem, organization will preserve current functional structure, but will make their decisions extremely data drive and efficient. The new role of PLM is to keep organizational and process silos, but connect data silos. This is a place where new cloud based multi-tenant technologies will play key role in the future organization transformation from the vision of no silo extended enterprise to organized functional silos connected by common understanding of data.

When I read this post I had so much to comment, which lead to this post. Let me share my thoughts related to this conclusion and hopefully it helps in future discussions. Feel free to join the discussion:

Keep process and organizational silos, but break data silos. This is should be a new mantra by new PLM organization in 21st century

For me “Keep process and organizational silos ….. “ is exactly the current state of classical PLM, where PLM concepts are implemented to provide data continuity within a siloed organization. When you can stay close to the existing processes the implementation becomes easier. Less business change needed and mainly a focus on efficiency gains by creating access to information.

Most companies do not want to build their data continuity themselves and therefore select and implement a PLM system that provides the data continuity, currently mainly around the various BOM-views. By selecting a PLM system, you have a lot of data integration done for you by the vendor. Perhaps not as user-friendly as every user would expect, however no company has been able to build a 100% user-friendly PLM system yet, which is the big challenge for all enterprise systems. Therefore PLM vendors provide a lot of data continuity for you without the need for your company to take responsibility for this.

And if you know SAP, they go even further. Their mantra is that when using SAP PLM, you even do not need to integrate with ERP.  You can still have long discussions with companies when it comes to PLM and ERP integrations.  The main complexity is not the technical interface but the agreement who is responsible for which data sets during the product lifecycle. This should be clarified even before you start talking about a technical implementation. SAP claims that this effort is not needed in their environment, however they just shift the problem more towards the CAD-side. Engineers do not feel comfortable with SAP PLM when engineering is driving the success of the company. It is like the Swiss knife; every tool is there but do you want to use it for your daily work?

In theory a company does not need to buy a PLM system. You could build your own PLM-system, based on existing infrastructure capabilities. CAD integrations might be trickier, however this you could solve by connecting to their native environments.  For example, Microsoft presented at several PDT conferences an end-to-end PLM story based on Microsoft technology.  Microsoft “talks PLM” during these conferences, but does not deliver a PLM-system – they deliver the technologies.

The real 21st-century paradigm

What is really needed for the 21st century is to break down the organizational silos as current ways of working are becoming less and less applicable to a modern enterprise. The usage of software has the major impact on how we can work in the future. Software does not follow the linear product process. Software comes with incremental deliveries all the time and yes the software requires still hardware to perform. Modern enterprises try to become agile, being able to react quickly to trends and innovation options to bring higher and different value to their customers.  Related to product innovation this means that the linear, sequential go-to-market process is too slow, requires too much data manipulation by non-value added activities.

All leading companies in the industry are learning to work in a more agile mode with multidisciplinary teams that work like startups. Find an incremental benefit, rapidly develop test and interact with the market and deliver it. These teams require real-time data coming from all stakeholders, therefore the need for data continuity. But also the need for data quality as there is no time to validate data all the time – too expensive – too slow.

Probably these teams will not collaborate along the various BOM-views, but more along digital models, both describing product specifications and system behavior. The BOM is not the best interface to share system information. The model-based enterprise with its various representations is more likely to be the backbone for the new future in the 21st century. I wrote about this several times, e.g. item-centric or model-centric.

And New cloud-based multi-tenant technologies …

As Oleg writes in his conclusion:

This is a place where new cloud-based multi-tenant technologies will play key role in the future organization transformation from the vision of no silo extended enterprise to organized functional silos connected by common understanding of data.

From the academic point of view, I see the beauty of new cloud-based multi-tenant technologies. Quickly build an environment that provides information for specific roles within the organization – however will this view be complete enough?  What about data dictionaries or is every integration a customization?

When talking with companies in the real world, they are not driven by technology – they are driven by processes. They do not like to break down the silos as it creates discomfort and the need for business transformation. And there is no clear answer at this moment. What is clear that leading companies invest in business change first before looking into the technology.

Conclusion

Sometimes too much academic and wishful thinking from technology providers is creating excitement.  Technology is not the biggest game changer for the 21st century. It will be the new ways of working and business models related to a digital enterprise that require breaking organizational silos. And these new processes will create the demand for new technologies, not the other way around.

Break down the walls !

At the moment this post is published I have had time to digest the latest PLMx conference organized by MarketKey. See the agenda here. For me it was a conference with mixed feelings this time and I will share more details a little further on.

Networking during the conference was excellent, good quality of conversations, however the number of people attending was smaller than previous conferences, perhaps due to too much diversification in the PI conferences?

There were several inspiring sessions and as I participated in three sessions myself, I missed a lot of potential exciting sessions that were in parallel at the same time. I believe four parallel tracks is too much and downloading the presentations later does not give you the real story.  Now some of the notable sessions I attended:

Building a Better Urban Mobility Future

The first keynote session was meant to inspire us and think of solving issues differently. Lewis Horne from a Swedish automotive startup explained their different approach to designing an electrical vehicle. Not based on classical paradigms – you do not need a steering wheel – you can navigate differently. And switching the indicator on when going left or right is now a swipe. Of course these were not the only differences.

Unity will not certify for the highest safety classes like other vehicles as car safety rules are a lot based on mechanical / human handling and responses. A fully computerized and full of sensors has complete different dynamics. And a light city car does not ride on the high-speed way. Based on the first prototype there are already more than 1000 pre-orders but Unity does not have a manufacturing facility. This will be franchised. Unity used the Apple mode – focus on an unmatched user-experience instead of manufacturability. Let’s see what happens when the first Unity’s start riding – current target prices 20.000 Euro. Will it be the new hype for modern citizens?

Focus on quality – not on happy engineers

Not only the title of this paragraph but also other statements were made by Hilmer Brunn, head of global PLM from Mettler-Toledo related to their PLM implementation strategy.  As Hilmer stated:

We should not focus to give engineers more time to design only. The job of engineering is more comprehensive than just creating designs. Engineers also need to solve issues that are related to their design – not leave it to the others.

Another interesting statement:

As long as you do not connect simulation to your design in 3D, you are actually working with 3D as if you do it with 2D. The value of 3D is more than just representation of geometry.

And the last quote I want to share from Hilmer was again related to engineering.

Engineering should consider themselves as a service provider of information to the rest of the company, providing the full information associated with a design, instead of behaving like extreme, intelligent people who need more resources to translate and complete their work.

Grand statements although during Q&A it became clear that also Mettler-Toledo did not have the magic bullet to get an organization work integrated.

Working towards a Model-Based Enterprise with PLM

I consider Model-Based practices as one of the essential needs for future PLM as this approach reduces the amount of derived information related to a product/ system. And it provides a digital continuity. In the last PDT conference in Gothenburg this topic was shared on a quit extensive matter. Have a read to fresh-up your memory here:  The weekend after PDT Europe – part 1 and part 2

The focus group  which I moderated was with approximate 20 attendees and the majority was looking for getting a better understanding what model-based would mean for their organization. Therefore, the discussion was at the end more around areas where a few persons had the experience while others still tried to grasp the concepts. For me a point to take action related to education and in future posts I will go deeper into the basics.

PLMPulse Survey results and panel discussion

Nick Leeder presented the context of the PLMPulse survey and the results in a precise manner, where perhaps the result was not that surprising to the audience as many of us are involved in PLM. Two recurring points: PLM is still considered as an engineering tool and: The value related to PLM is most of the time not clear. You can register and download the full report from here.

Next Nick lead a panel discussion where people from the audience could participate.  And here we got into a negative spiral where it became an inward-looking discussion why PLM has never been able to show the value and get out of the engineering domain. It was a someone said like an anonymous PLM meeting where members stood up and confessed they were also part of the group that could not change this behavior.

Was it the time of the day? Was it the mood of the audience? Too much old experiences?  I believe it has to do with the fact that in PLM projects and conferences we focus too much on what we do and how we do things, not connecting it to tangible benefits that are recognized at the board level. And we will see an example later.

Solar Stratos

The food and drinks at the end of day 1 probably washed away the PLMPulse feedback session and Raphael Domjan inspired us with his SolarStratos project – a mission to develop a plane that can fly on solar energy on the heights of the stratosphere. Raphael is working hard with a team now to get there.

Designing an airplane, more a glider, that can take off en reach the stratosphere on solar energy requires solving a combination of so many different challenges. The first test flight reached an altitude of 500 m, but you can imagine challenges with the stratosphere – lack of oxygen / air pressure need to be solved. Raphael is looking for funding and you can find more details here. Back to the relative easy PLM challenges

The future of PLM Consultancy

Together with Oleg Shilovitsky we had a discussion related to the ways PLM could be realized in different manners thanks to changing technology. The dialogue started through our blogs – read it here. In this session there was a good dialogue with the audience and MarketKey promised to share the video recording of this session soon.  Stay tuned to Oleg’s blog or my blog and you can watch it.

PLM in the context of digitization

This was my main personal contribution to the conference. Sharing insights why we have to approach PLM in a different manner. Not the classical linear engineering approach but as a mix of system of record and system of engagement. You can see the full presentation on SlideShare here.

My main conclusions are that PLM consultants / experts focus too much on what and how they do PLM, where the connection to WHY is missing. (See also my post PLM WHY?).

In addition I defended the statement that old and new PLM are incompatible and therefore you need to accept they will exist both in your organization. For a while or for a long time, depending on your product lifecycle.  In order to reduce the gap between old and new PLM, there is a need for data governance, model-based ways of working, which allow the company to connect at some stages the old/record data and the new data. And don’t do pilots anymore experimenting new ways of working and then stop because the next step seems to be overwhelming. Start your projects in small, multidisciplinary teams and make them real. The only way to be faster in the future.

PLM in Manufacturing as Backbone of the Smart Factory

Susanne Lauda, Director, Global Advanced Manufacturing Technology, AGCO Corporation provided an overview related to AGCO’s new PLM journey and how they were benefiting from a digital thread towards manufacturing. It felt like a smooth vendor demo as everything looked nice and reasonable. It was all about the WHAT. However two points that brought the extra:

When moving to the new system the tried to bring in the data from an existing product into then new system. According to Susanne a waste of time as the data required so much rework – there was no real value added for that. This confirms again my statement that old and new PLM are incompatible and one should not try to unify everything again in one system.

Second, I got excited at the end when we discussed the WHY for PLM and the business value of PLM. Here Suzanne mentioned PLM started as a “must-do strategic” project.  PLM lead to a reduction of time to market with almost 50 %. Suzanne did not give exact number, but you can imagine I have heard these numbers from other companies too. Why aren’t we able to connect these benefits in the mindset of the management to PLM ? Perhaps still too much engineering focused.

Next Susanne explained that they investigated the cost for quality for their manufacturing plants. What if something was produced wrong, the wrong parts were ordered, the delays to fix it, the changes needed to be made on the shop floor?  These results were so high that people were even afraid to report them. This is the case at many companies I worked with – even their PLM consultants do not receive these numbers – you just have to imagine they are big.

At AGCO they were able to reduce the cost for quality in a significant manner and Susanne explain that PLM was a main contributor to that success. However, success always has many fathers – so if your PLM team does not claim loud (and we are modest people not used to talk finance) – the success will not be recognized.

PLM’s Place Within an Enterprise Application Architecture

Peter Bilello from CIMData in the closing keynote speech gave an excellent summary and overview of where and which capabilities fit in an enterprise architecture and the positioning of a product innovation platform. A blueprint that can be used for companies to grasp the holistic view before jumping into the details of the tools.

Conclusion

PLMx Hamburg 2018 was an event with valuable highlights for me and potential I missed several more due to the fact of parallel streams. I hope to catch-up with these sessions in the upcoming month and share interesting thoughts that I discover with you. What remains crucial I believe for all vendor-neutral events is to find new blood. New companies, new experiences that are focused on the future of PLM and connect to the WHY or the WHAT WE LEARNED values.

If you have followed my blog over the past 10 years, I hope you realize that I am always trying to bring sense to the nonsense and still looking into the future where new opportunities are imagined. Perhaps due to my Dutch background (our motto: try to be normal – do not stand out) and the influence of working with Israeli’s (a country where almost everyone is a startup).

Given this background, I enjoy the current discussion with Oleg Shilovitsky related to potential PLM disruptions. We worked for many years together at SmarTeam, a PDM/PLM disruptor at that time, in the previous century. Oleg has continued his passion for introducing potential disruptive solutions  (Inforbix / OpenBOM) where I got more and more intrigued by human behavior related to PLM. For that reason, I have the human brain in my logo.

Recently we started our “The death of ….” Dialogue, with the following episodes:

Jan 14thHow to democratize PLM knowledge and disrupt traditional consulting experience

Jan 21stThe death of PLM Consultancy

Jan 22ndWhy PLM consultants are questioning new tools and asking about cloud exit strategy?

Here is episode 4  – PLM Consultants are still alive and have an exit strategy

Where we agree

We agreed on the fact that traditional consultancy practices related to PLM ranking and selection processes are out of time. The Forester Wave publication was the cause of our discussion. For two reasons:

  1. All major PLM systems cover for 80 percent the same functionalities. Therefore there is no need to build, send and evaluate lengthy requirements lists to all potential candidates and then recommend on the preferred vendor. Waste of time as the besides the requirements there is much more to evaluate than just performing tool selection.
  2. Many major consultancy firms have PLM practices, most of the time related to the major PLM providers. Selecting one of the major vendors is usually not a problem for your reputation, therefore the importance of these rankings. Consultancy firms will almost never recommend disruptive tool-sets.

PLM businesses transformation

At this point, we are communicating at a different wavelength. Oleg talks about PLM business transformation as follows:

Cloud is transforming PLM business. Large on-premise PLM projects require large capital budget. It is a very good foundation for existing PLM consulting business. SaaS subscription is a new business model and it can be disruptive for lucrative consulting deals. Usually, you can see a lot of resistance when somebody is disrupting your business models. We’ve seen it in many places and industries. It happened with advertising, telecom and transportation. The time is coming to change PLM, engineering and manufacturing software and business.

I consider new business models less relevant compared to the need for a PLM practice transformation. Tools like Dropbox, perhaps disruptive for PDM systems, are tools that implement previous century methodology (document-driven / file-based models). We are moving from item-centric towards a model-driven future.

The current level of PLM practices is related to an item-centric approach, the domain where also OpenBOM is bringing disruption.
The future, however, is about managing complex products, where products are actually systems, a combination of hardware and software. Hardware and software have a complete different lifecycle, and all major PLM vendors are discovering an overall solution concept to incorporate both hardware and software. If you cannot manage software in the context of hardware in the future, you are at risk.  Each PLM vendor has a different focus area due to their technology history. I will address this topic during the upcoming PLMx conference in Hamburg. For a model-driven enterprise, I do not see an existing working combination of disruptors yet.

Cloud security and Cloud exit strategy

Oleg does not really see the impact of the cloud as related to the potential death of PLM consulting as you can read here:

I agree, cloud might be still not for everyone. But the adoption of cloud is growing and it is becoming a viable business model and technology for many companies. I wonder how “cloud” problem is related to the discussion about the death of PLM consulting. And…  here is my take on this. It is all about business model transformation.

I am not convinced that in the PLM cloud is the only viable business model. Imagine an on-premise rigid PLM system. Part of the cloud-based implementation benefits come from low upfront costs and scalable IT. However, cloud also pushes companies to defend a no-customization strategy – configuration of the user interface only.  This is a “secret” benefit for cloud PLM vendors as they can say “NO” to the end users of course within given usability constraints. Saying “NO” to the customer is lesson one for every current PLM implementation as everyone knows the problem of costly upgrades later

Also, make a 5-10 years cost evaluation of your solution and take the risk of raising subscription fees into account. No vendor will drop the price unless forced by the outside world. The initial benefits will be paid back later because of the other business model.

Cloud exit strategy and standards

When you make a PLM assessment, and usually experienced PLM consultants do this, there is a need to consider an exit strategy. What happens if your current PLM cloud vendor(s) stops to exist or migrate to a new generation of technology and data-modeling? Every time when new technology was introduced, we thought it was going to be THE future. The future is unpredictable. However, I can predict that in 10 years from now we live with different PLM concepts.

There will be changes and migrations and cloud PLM vendors will never promote standardized exports methods (unless forced) to liberate the data in the system. Export tools could be a niche market for PLM partners, who understand data standards. Håkan Kårdén, no finders fee required, however, Eurostep has the experience in-house.

 

Free downloads – low barriers to start

A significant difference in opinion between Oleg and me is Oleg’s belief in bottom-up, DIY PLM as part of PLM democratization and my belief in top-down business transformation supported by PLM. When talking about Aras, Autodesk, and OpenBOM,  Oleg states:

All these tools have one thing in common. You can get the tool or cloud services for free and try it by yourself before buying. You can do it with Aras Innovator, which can be downloaded for free using enterprise open source. You can subscribe for Autodesk Fusion Lifecycle and OpenBOM for trial and free subscriptions. It is different from traditional on-premise PLM tools provided by big PLM players. These tools require months and sometimes even years of planning and implementation including business consulting and services.

My experience with SmarTeam might influence this discussion. SmarTeam was also a disruptive PDM solution thanks to its easy data-modeling and Microsoft-based customization capabilities like Aras. Customers and implementers could build what they want, you only needed to know Visual Basic. As I have supported the field mitigating installed SmarTeam implementations, often the problem was SmarTeam has been implemented as a system replicating/automating current practices.

Here Henry Ford’s statement as shown below applies:

Implementations became troublesome when SmarTeam provided new and similar business logic. Customers needed to decide to use OOTB features and de-customize or not benefits from new standard capabilities. SmarTeam had an excellent business model for service providers and IT-hobbyists/professionals in companies. Upgrade-able SmarTeam implementations where those that remained close to the core, but meanwhile we were 5 – 8 years further down the line.

I believe we still need consultants to help companies to tell and coach them towards new ways of working related to the current digitization. Twenty years old concepts won’t work anymore. Consultants need a digital mindset and think holistic. Fitting technology and tools will be there in the future.

Conclusion

The discussion is not over, and as I reached already more than 1000 words, I will stop. Too many words already for a modern pitch, not enough for a balanced debate. Oleg and I will continue in Hamburg, and we both hope others will chime in, providing balanced insights in this discussion.

To be continued …..?

 

Dear readers, it is time for me to relax and focus on Christmas and a New Year upcoming. I realize that not everyone who reads my posts will be in the same mood. You might have had your New Year three months ago or have New Year coming up in a few months. This is the beauty and challenge of a global, multicultural diverse society. Imagine we are all doing the same, would you prefer such a world ? Perhaps it would give peace to the mind (no surprises, everything predictable) however for human survival we need innovation and new ways of life.

This mindset is also applicable to manufacturing companies. Where in the past companies were trying to optimize and standardize their processes driven by efficiency and predictability, now due to the dynamics of a globally connected world, businesses need to become extremely flexible however still reliable and profitable.

How will they make the change ?

Digital transformation is one of the buzz words pointing to the transition process. Companies need to go through a change to become flexible for the future and deliver products or solutions for the individual customer. Currently companies invest in digital transformation, most of the time in areas that bring direct visibility to the outside world or their own management, not necessarily delivering profitable results as a recent article from McKinsey illustrated: The case for digital reinvention.

And for PLM ?

I have investigated digital transformation in relation to PLM  with particular interest this year as I worked with several companies that preached to the outside world that they are changing or were going to make a change. However what is happening at the PLM level ? Most of the time nothing. Some new tools, perhaps some new disciplines like software engineering become more critical. However the organization and people do not change their ways of working as in particular the ongoing business and related legacy are blocking the change.

Change to ?

This is another difficult question to answer.  There is no clearly defined path to share. Yes, modern PLM will be digital PLM, it will be about data-driven connected information. A final blueprint for digital PLM does not exist yet. We are all learning and guessing.  You can read my thoughts here:

Software vendors in various domains are all contributing to support a modern digital product innovation management future. But where to start?  Is it the product innovation platform? Is it about federated solutions? Model-Based? Graph-databases? There are even people who want to define the future of PLM.  We can keep throwing pieces of the puzzle on the table, but all these pieces will not lead to a single solved puzzle. There will be different approaches based on your industry and your customers. Therefore, continuous learning and investing time to understand the digital future is crucial. This year’s PDT Europe conference was an excellent event to learn and discuss the themes around a model-based lifecycle enterprise. You can read my reviews here: The weekend after PDT Europe 2017 part 1 and part 2.

The next major event where I plan to discuss and learn about modern PLM topics is the upcoming PI PLMx event in Hamburg on February 19-20 organized by MarketKey. Here I will discuss the Model-Based Enterprise and lecture about the relation between PLM and digital transformation. Hoping to see some of you there for exciting discussions and actions.

Conclusion

Merry Christmas for those who are celebrating and a happy, healthy and prosperous 2018 to all of you. Thanks for your feedback. Keep on asking questions or propose other thoughts as we are all learning. The world keeps on turning, however for me the next two weeks will the time relax.

Talk to you in 2018 !

 

When I started working with SmarTeam Corp.  in 1999, the company had several product managers, who were responsible for the whole lifecycle of a component or technology. The Product Manager was the person to define the features for the new release and provide the justification for these new features internally inside R&D.  In addition the Product Manager had the external role to visit customers and understand their needs for future releases and building and explaining a coherent vision to the outside and internal world. The product manager had a central role, connecting all stakeholders.

In the ideal situation the Product Manager was THE person who could speak in R&D-language about the implementation of features, could talk with marketing and documentation teams to explain the value and expected behavior and could talk with the customer describing the vision, meanwhile verifying the product’s vision and roadmap based on their inputs.All these expected skills make the role of a product manager challenging. Is the person too “techy” than he/she will enjoy working with R&D but have a hard time understanding customer demands. From the other side if the Product Manager is excellent in picking-up customer and market feedback he/she might not be heard and get the expected priorities from R&D. For me, it has always been clear that in software world a “bi-directional” Product Manager is crucial to success.

Where are the Product Managers in the Manufacturing Industry?

Approximate four years ago new concepts related to digitalization for PLM became more evident. How could a digital continuity connect the various disciplines around the product lifecycle and therefore provide end-to-end visibility and traceability? When speaking of end-to-end visibility most of the time companies talked about the way they designed and delivered products, visibility of what is happening stopped most of the time after manufacturing. The diagram to the left, showing a typical Build To Order organization illustrates the classical way of thinking. There is an R&D team working on Innovation, typically a few engineers and most of the engineers are working in Sales Engineering and Manufacturing Preparation to define and deliver a customer specific order. In theory, once delivered none of the engineers will be further involved, and it is up to the Service Department to react to what is happening in the field.

A classical process in the PLM domain is the New Product Introduction process for companies that deliver products in large volumes to the market, most of the time configurable to be able to answer to various customer or pricing segments. This process is most of the time linear and is either described in one stream or two parallel streams. In the last case, the R&D department develops new concepts and prepares the full product for the market. However, the operational department starts in parallel, initially involved in strategic sourcing, and later scaling-up manufacturing disconnected from R&D.

I described these two processes because they both illustrate how disconnected the source (R&D/ Sales)  are from the final result in the field. In both cases managed by the service department. A typical story that I learned from many manufacturing companies is that at the end it is hard to get a full picture from what is happening across the whole lifecycle, How external feedback (market & customers) have the option to influence at any stage is undefined. I used the diagram below even  before companies were even talking about a customer-driven digital transformation. Just understanding end-to-end what is happening with a product along the lifecycle is already a challenge for a company.

Putting the customer at the center

Modern business is about having customer or market involvement in the whole lifecycle of the product. And as products become more and more a combination of hardware and software, it is the software that allows the manufacturer to provide incremental innovation to their products. However, to innovate in a manner that is matching or even exceeding customer demands, information from the outside world needs to travel as fast as possible through an organization. In case this is done in isolated systems and documents, the journey will be cumbersome and too slow to allow a company to act fast enough. Here digitization comes in, making information directly available as data elements instead of documents with their own file formats and systems to author them. The ultimate dream is a digital enterprise where date “flows”, advocated already by some manufacturing companies for several years.

In the previous paragraph I talked about the need to have an infrastructure in place for people in an organization to follow the product along the complete lifecycle, to be able to analyze and improve the customer experience. However, you also need to create a role in the organization for a person to be responsible for combining insights from the market and to lead various disciplines in the organization, R&D, Sales, Services. And this is precisely the role of a Product Manager.

Very common in the world of software development, not yet recognized in manufacturing companies. In case a product manager role exists already in your organization, he/she can tell you how complicated it currently is to get an overall view of the product and which benefits a digital infrastructure would bring for their job. Once the product manager is well-supported and recognized in the organization, the right skill set to prioritize or discover actions/features will make the products more attractive for consumers. Here the company will benefit.

Conclusion

If your company does not have the role of a product manager in place, your business is probably not yet well enough engaged in the customer journey.  There will be broken links and costly processes to get a fast response to the market.  Consider the role of a Product Manager, which will emerge as seen from the software business.

NOTE 1: Just before publishing this post I read an interesting post from Jan Bosch: Structure Eats Strategy. Well fitting in this context

NOTE 2: The existence of a Product Manager might be a digital maturity indicator for a company, like for classical PLM maturity, the handling of the MBOM (PDM/PLM/ERP) gives insight into PLM maturity of a company.

Related to the MBOM, please read: The Importance of a PLM data model – EBOM and MBOM

 

 

 

 

 

Last week I posted my first review of the PDT Europe conference. You can read the details here: The weekend after PDT Europe (part 1).  There were some questions related to the abbreviation PDT. Understanding the history of PDT, you will discover it stands for Product Data Technology. Yes, there are many TLA’s in this world.

Microsoft’s view on the digital twin

Now back to the conference. Day 2 started with a remote session from Simon Floyd. Simon is Microsoft’s Managing Director for Manufacturing Industry Architecture Enterprise Services and a frequent speaker at PDT. Simon shared with us Microsoft’s viewpoint of a Digital Twin, the strategy to implement a Digit Twin, the maturity status of several of their reference customers and areas these companies are focusing. From these customers it was clear most companies focused on retrieving data in relation to maintenance, providing analytics and historical data. Futuristic scenarios like using the digital twin for augmented reality or design validation. As I discussed in the earlier post, this relates to my observations, where creating a digital thread between products in operations is considered as a quick win. Establishing an end-to-end relationship between products in operation and their design requires many steps to fix. Read my post: Why PLM is the forgotten domain in digital transformation.

When discussing the digital twin architecture, Simon made a particular point for standards required to connect the results of products in the field. Connecting a digital twin in a vendor-specific framework will create a legacy, vendor lock-in, and less open environment to use digital twins. A point that I also raised in my presentation later that day.

Simon concluded with a great example of potential future Artificial Intelligence, where an asset based on its measurements predicts to have a failure before the scheduled maintenance stop and therefore requests to run with a lower performance so it can reach the maintenance stop without disruption.

Closing the lifecycle loop

Sustainability and the circular economy has been a theme at PDT for some years now too. In his keynote speech, Torbjörn Holm from Eurostep took us through the global megatrends (Hay group 2030) and the technology trends (Gartner 2018) and mapped out that technology would be a good enabler to discuss several of the global trends.

Next Torbjörn took us through the reasons and possibilities (methodologies and tools) for product lifecycle circularity developed through the ResCoM project in which Eurostep participated.

The ResCoM project (Resource Conservative Manufacturing) was a project co-funded by the European Commission and recently concluded. More info at www.rescom.eu

Torbjörn concluded discussing the necessary framework for Digital Twin and Digital Thread(s), which should be based on a Model-Based Definition, where ISO 10303 is the best candidate.

Later in the afternoon, there were three sessions in a separate track, related to design optimization for value, circular and re-used followed by a panel discussion. Unfortunate I participated in another track, so I have to digest the provided materials still. Speakers in that track were Ola Isaksson (Chalmers University), Ingrid de Pauw & Bram van der Grinten (IDEAL&CO) and Michael Lieder (KTH Sweden)

Connecting many stakeholders

Rebecca Ihrfors, CIO from the Swedish Defense Material Administration (FMV) shared her plans on transforming the IT landscape to harmonize the current existing environments and to become a broker between industry and the armed forces (FM). As now many of the assets come with their own data sets and PDM/PLM environments, the overhead to keep up all these proprietary environments is too expensive and fragmented. FWM wants to harmonize the data they retrieve from industry and the way they offer it to the armed forces in a secure way. There is a need for standards and interoperability.

The positive point from this presentation was that several companies in the audience and delivering products to Swedish Defense could start to share and adapt their viewpoints how they could contribute.

Later in the afternoon, there were three sessions in a separate track rented to standards for MBE inter-operability and openness that would fit very well in this context. Brian King (Koneksys), Adrian Murton (Airbus UK) and Magnus Färneland (Eurostep) provided various inputs, and as I did not attend these parallel sessions I will dive deeper in their presentations at a later time

PLM something has to change – bimodal and more

In my presentation, which you can download from SlideShare here: PLM – something has to change. My main points were related to the fact that apparently, companies seem to understand that something needs to happen to benefit really from a digital enterprise. The rigidness from large enterprise and their inhibitors to transform are more related to human and incompatibility issues with the future.

How to deal with this incompatibility was also the theme for Martin Eigner’s presentation (System Lifecycle Management as a bimodal IT approach) and Marc Halpern’s closing presentation (Navigating the Journey to Next Generation PLM).

Martin Eigner’s consistent story was about creating an extra layer on top of the existing (Mode 1) systems and infrastructure, which he illustrated by a concept developed based on Aras.

By providing a new digital layer on top of the existing enterprise, companies can start evolving to a modern environment, where, in the long-term, old Mode 1 systems will be replaced by new digital platforms (Mode 2). Oleg Shilovitsky wrote an excellent summary of this approach. Read it here: Aras PLM  platform “overlay” strategy explained.

Marc Halpern closed the conference describing his view on how companies could navigate to the Next Generation PLM by explaining in more detail what the Gartner bimodal approach implies. Marc’s story was woven around four principles.

Principle 1 The bimodal strategy as the image shows.

Principle 2 was about Mode 1 thinking in an evolutionary model. Every company has to go through maturity states in their organization, starting from ad-hoc, departmental, enterprise-based to harmonizing and fully digital integrated. These maturity steps also have to be taken into account when planning future steps.

Principle 3 was about organizational change management, a topic often neglected or underestimated by product vendors or service providers as it relates to a company culture, not easy to change and navigate in a particular direction.

Finally, Principle 4 was about Mode 2 activities. Here an organization should pilot (in a separate environment), certify (make sure it is a realistic future), adopt (integrate it in your business) and scale (enable this new approach to exists and grow for the future).

Conclusions

This post concludes my overview of PDT Europe 2017. Looking back there was a quiet aligned view of where we are all heading with PLM and related topics. There is the hype an there is reality, and I believe this conference was about reality, giving good feedback to all the attendees what is really happening and understood in the field. And of course, there is the human factor, which is hard to influence.

Share your experiences and best practices related to moving to the next generation of PLM (digital PLM ?) !

 

 

 

As I am preparing my presentation for the upcoming PDT Europe 2017 conference in Gothenburg, I was reading relevant experiences to a data-driven approach. During PDT Europe conference we will share and discuss the continuous transformation of PLM to support the Lifecycle Model-Based Enterprise. 

One of the direct benefits is that a model-based enterprise allows information to be shared without the need to have documents to be converted to a particular format, therefore saving costs for resources and bringing unprecedented speed for information availability, like what we are used having in a modern digital society.

For me, a modern digital enterprise relies on data coming from different platforms/systems and the data needs to be managed in such a manner that it can serve as a foundation for any type of app based on federated data.

This statement implies some constraints. It means that data coming from various platforms or systems must be accessible through APIs / Microservices or interfaces in an almost real-time manner. See my post Microservices, APIs, Platforms and PLM Services. Also, the data needs to be reliable and understandable for machine interpretation. Understandable data can lead to insights and predictive analysis. Reliable and understandable data allows algorithms to execute on the data.

Classical ECO/ECR processes can become highly automated when the data is reliable, and the company’s strategy is captured in rules. In a data-driven environment, there will be much more granular data that requires some kind of approval status. We cannot do this manually anymore as it would kill the company, too expensive and too slow. Therefore, the need for algorithms.

What is understandable data?

I tried to avoid as long as possible academic language, but now we have to be more precise as we enter the domain of master data management. I was triggered by this recent post from Gartner: Gartner Reveals the 2017 Hype Cycle for Data Management. There are many topics in the hype cycle, and it was interesting to see Master Data Management is starting to be taken seriously after going through inflated expectations and disillusionment.

This was interesting as two years ago we had a one-day workshop preceding PDT Europe 2015, focusing on Master Data Management in the context of PLM. The attendees at that workshop coming from various companies agreed that there was no real MDM for the engineering/manufacturing side of the business. MDM was more or less hijacked by SAP and other ERP-driven organizations.

Looking back, it is clear to me why in the PLM space MDM was not a real topic at that time. We were still too much focusing and are again too much focusing on information stored in files and documents. The only area touched by MDM was the BOM, and Part definitions as these objects also touch the ERP- and After Sales-  domain.

Actually, there are various MDM concepts, and I found an excellent presentation from Christopher Bradley explaining the different architectures on SlideShare: How to identify the correct Master Data subject areas & tooling for your MDM initiative. In particular, I liked the slide below as it comes close to my experience in the process industry

Here we see two MDM architectures, the one of the left driven from ERP. The one on the right could be based on the ISO-15926 standard as the process industry has worked for over 25 years to define a global exchange standard and data dictionary. The process industry was able to reach such a maturity level due to the need to support assets for many years across the lifecycle and the relatively stable environment. Other sectors are less standardized or so much depending on new concepts that it would be hard to have an industry-specific master.

PLM as an Application Specific Master?

If you would currently start with an MDM initiative in your company and look for providers of MDM solution, you will discover that their values are based on technology capabilities, bringing data together from different enterprise systems in a way the customer thinks it should be organized. More a toolkit approach instead of an industry approach. And in cases, there is an industry approach it is sporadic that this approach is related to manufacturing companies. Remember my observation from 2015: manufacturing companies do not have MDM activities related to engineering/manufacturing because it is too complicated, too diverse, too many documents instead of data.

Now with modern digital PLM, there is a need for MDM to support the full digital enterprise. Therefore, when you combine the previous observations with a recent post on Engineering.com from Tom Gill: PLM Initiatives Take On Master Data Transformation I started to come to a new hypotheses:

For companies with a model-based approach that has no MDM in place, the implementation of their Product Innovation Platform (modern PLM) should be based on the industry-specific data definition for this industry.

Tom Gill explains in his post the business benefits and values of using the PLM as the source for an MDM approach. In particular, in modern PLM environments, the PLM data model is not only based on the BOM.  PLM now encompasses the full lifecycle of a product instead of initially more an engineering view. Modern PLM systems, or as CIMdata calls them Product Innovation Platforms, manage a complex data model, based on a model-driven approach. These entities are used across the whole lifecycle and therefore could be the best start for an industry-specific MDM approach. Now only the industries have to follow….

Once data is able to flow, there will be another discussion: Who is responsible for which attributes. Bjørn Fidjeland from plmPartner recently wrote: Who owns what data when …?  The content of his post is relevant, I only would change the title: Who is responsible for what data when as I believe in a modern digital enterprise there is no ownership anymore – it is about sharing and responsibilities

 

Conclusion

Where MDM in the past did not really focus on engineering data due to the classical document-driven approach, now in modern PLM implementations, the Master Data Model might be based on the industry-specific data elements, managed and controlled coming from the PLM data model

 

Do you follow my thoughts / agree ?

 

 

At this moment there are two approaches to implement PLM. The most common practice is item-centric and model-centric will be potentially the best practice for the future. Perhaps your company still using a method from the previous century called drawing-centric. In that case, you should read this post with even more attention as there are opportunities to improve.

 

The characteristics of item-centric

In an item-centric approach, the leading information carrier is an item also known as a part. The term part is sometimes confusing in an organization as it is associated with a 3D CAD part. In SAP terminology the item is called Material, which is sometimes confusing for engineering as they consider Material the raw material. Item-centric is an approach where items are managed and handled through the whole lifecycle. In theory, an item can be a conceptual item (for early estimates), a design item (describing the engineering intent), a manufacturing item (defining how an item is consumed) and potentially a service item.

The picture below illustrates the various stages of an item-centric approach. Don’t focus on the structure, it’s an impression.

It is clear these three structures are different and can contain different item types. To read more about the details for an EBOM/MBOM approach read these post on my blog:

Back to item-centric. This approach means that the item is the leading authority of the product /part. The id and revision describe the unique object in the database, and the status of the item tells you in the current lifecycle stage for the item. In some cases, where your company makes configurable products also the relation between two items can define effectivity characteristics, like data effectivity, serial number effectivity and more. From an item structure, you can find its related information in context. The item points to the correct CAD model, the assembly or related manufacturing drawings, the specifications. In case of an engineering item, it might point towards approved manufacturers or approved manufacturing items.

Releasing an item or a BOM means the related information in context needs to validated and frozen too. In case your company works with drawings for manufacturing, these drawings need to be created, correct and released, which sometimes can be an issue due to some last-minute changes that can happen. The above figure just gives an impression of the potential data related to an item. It is important to mention that reports, which are also considered documents, do not need an approval as they are more a snapshot of the characteristics at that moment of generation.

The advantages of an item-centric approach are:

  • End-to-end traceability of information
  • Can be implemented in an evolutionary approach after PDM-ERP without organizational changes
  • It enables companies to support sharing of information
  • Sharing of information forces companies to think about data governance
    (not sure if a company wants to invest on that topic)

The main disadvantages of an item-centric approach are:

  • Related information on the item is not in context and therefore requires its own management and governance to ensure consistency
  • Related information is contained in documents, where availability and access is not always guaranteed

Still, the item-centric approach brings big benefits to a company that was working in a classical drawing-driven PDM-ERP approach. An additional remark needs to be made that not every company will benefit from an item-centric approach as typically Engineering-to-Order companies might find this method creating too much overhead.

The characteristics of Model-Centric

A model-centric approach is considered the future approach for modern enterprises as it brings efficiency, speed, multidisciplinary collaboration and support for incremental innovation in an agile way. When talking about a model-centric approach, I do not mean a 3D CAD model-centric approach. Yes, in case the product is mature, there will be a 3D Model serving as a base for the physical realization of the product.

However, in the beginning, the model can be still a functional or logical model. In particular, for complex products, model-based systems engineering might be the base for defining the solution. Actually, when we talk about products that interact with the outside world through software, we tend to call them systems. This explains that model-based systems engineering is getting more and more a recommended approach to make sure the product works as expected, fulfills all the needs for the product and creates a foundation for incremental innovation without starting from scratch.

Where the model-based architecture provides a framework for all stakeholders, the 3D CAD model will be the base for a digital thread towards manufacturing. Linking parameters from the logical and functional model towards the physical model a connection is created without the need to create documents or input-files for other disciplines. Adding 3D Annotations to the 3D CAD model and manufacturing process steps related to the model provides a direct connection to the manufacturing process.

The primary challenge of this future approach is to have all these data elements (requirements, functions, components, 3D design instances, manufacturing processes & resources to be connected in a federated environment (the product innovation platform). Connecting, versioning and baselining are crucial for a model-centric approach. This is what initiatives like Industry 4.0 are now exploring through demonstrators, prototypes to get a coherent collection of managed data.

Once we are able to control this collection of managed data concepts of digital twin or even virtual twin can be exploited linking data to a single instance in the field.

Also, the model can serve as the foundation for introduction incremental innovation, bringing in new features.  As the model-based architecture provides direct visibility for change impact (there are no documents to study), it will be extremely lean and cost-efficient to innovate on an existing product.

Advantages of model-centric

  • End-to-end traceability of all data related to a product
  • Extremely efficient in data-handling – no overhead on data-conversions
  • Providing high-quality understanding of the product with reduced effort compared to drawing-centric or item-centric approaches
  • It is scalable to include external stakeholders directly (suppliers/customers) leading to potential different, more beneficial business models
  • Foundation for Artificial Intelligence at any lifecycle step.

Disadvantages of model-centric

  • It requires a fundamentally different way of working compared to past. Legacy departments, legacy people, and legacy data do not fit directly into the model-centric approach. A business transformation is required, not evolution.
  • It is all about sharing data, which requires an architecture that is built to share information across Not through a service bus but as a (federated) platform of information.
    A platform requires a strong data governance, both from the dictionary as well as authorizations which discipline is leading/following.
  • There is no qualified industrial solution from any vendor yet at this time. There is advanced technology, there are demos, but to my knowledge, there is no 100% model-centric enterprise yet. We are all learning. Trying to distinguish reality from the hype.

 

Conclusions

The item-centric approach is the current best practice for most PLM implementations. However, it has the disadvantage that it is not designed for a data-driven approach, the foundation of a digital enterprise. The model-centric approach is new. Some facets already exist. However, for the total solution companies, vendors, consultants, and implementers are all learning step-by-step how it all connects. The future of model-centric is promising and crucial for survival.

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