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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 …..?

 

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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.

PLM holiday thoughts

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

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

Moving to Model-Based processes

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

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

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

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

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

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

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

PLM and ALM

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

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

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

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

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

Conclusion

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

 

Bye for now

Last week I published a dialogue I had with Flip van der Linden, a fellow Dutchman and millennial, eager to get a grip on current PLM. You can read the initial post here: A PLM dialogue.  In the comments, Flip continued the discussion (look here).  I will elaborate om some parts of his comments and hope some others will chime in. It made me realize that in the early days of blogging and LinkedIn, there were a lot of discussions in the comments. Now it seems we become more and more consumers or senders of information, instead of having a dialogue. Do you agree? Let me know.

Point 1

(Flip) PLM is changing – where lies the new effort for (a new generation of) PLM experts.  I believe a huge effort for PLM is successful change management towards ‘business Agility.’ Since a proper response to an ECR/ECO would evidently require design changes impacting manufacturing and even after-sales and/or legal.  And that’s just the tip of the iceberg.

 

You are right, the main challenge for future PLM experts is to explain and support more agile processes, mainly because software has become a major part of the solution. The classical, linear product delivery approach does not match the agile, iterative approach for software deliveries. The ECR/ECO process has been established to control hardware changes, in particular because there was a big impact on the costs. Software changes are extremely cheap and possible fast, leading to different change procedures. The future of PLM is about managing these two layers (hardware/software) together in an agile way. The solution for this approach is that people have to work in multi-disciplinary teams with direct (social) collaboration and to be efficient this collaboration should be done in a digital way.

A good article to read in this context is Peter Bilello’s article: Digitalisation enabled by product lifecycle management.

 

(Flip) What seems to be missing is an ‘Archetype’ of the ideal transformed organization. Where do PLM experts want to go with these businesses in practice? Personally, I imagine a business where DevOps is the standard, unique products have generic meta-data, personal growth is an embedded business process and supply chain related risks are anticipated on and mitigated through automated analytics. Do you know of such an evolved archetypal enterprise model?

I believe the ideal archetype does not exist yet. We are all learning, and we see examples from existing companies and startups pitching their story for a future enterprise. Some vendors sell a solution based on their own product innovation platform, others on existing platforms and many new vendors are addressing a piece of the puzzle, to be connected through APIs or Microservices. I wrote about these challenges in Microservices, APIs, Platforms and PLM Services.  Remember, it took us “old PLM experts” more than 10-15 years to evolve from PDM towards PLM, riding on an old linear trajectory, caught up by a new wave of iterative and agile processes. Now we need a new generation of PLM experts (or evolving experts) that can combine the new concepts and filter out the nonsense.

Point 2

(Flip) But then given point 2: ‘Model-based enterprise transformations,’ in my view, a key effort for a successful PLM expert would also be to embed this change mgt. as a business process in the actual Enterprise Architecture. So he/she would need to understand and work out a ‘business-ontology’ (Dietz, 2006) or similar construct which facilitates at least a. business processes, b. Change (mgt.) processes, c. emerging (Mfg.) technologies, d. Data structures- and flows, e. implementation trajectory and sourcing.

And then do this from the PLM domain throughout the organization per optimization.  After all a product-oriented enterprise revolves around the success of its products, so eventually, all subsystems are affected by the makeup of the product lifecycle. Good PLM is a journey, not a trip. Or, does a PLM expert merely facilitates/controls this enterprise re-design process? And, what other enterprise ontologism tools and methods do you know of?

Only this question could be a next future blog post. Yes, it is crucial to define a business ontology to support the modern flow of information through an enterprise. Products become systems, depending on direct feedback from the market. Only this last sentence already requires a redefinition of change processes, responsibilities. Next, the change towards data-granularity introduces new ways of automation, which we will address in the upcoming years. Initiatives like Industry 4.0 / Smart Manufacturing / IIoT all contribute to that. And then there is the need to communicate around a model instead of following the old documents path. Read more about it in Digital PLM requires a Model-Based Enterprise. To close this point:  I am not aware of anyone who has already worked and published experiences on this topic, in particular in the context of PLM.

 

Point 3

(Flip) Where to draw the PLM line in a digital enterprise? I personally think this barrier will vanish as Product Lifecycle Management (as a paradigm, not necessarily as a software) will provide companies with continuity, profitability and competitive advantage in the early 21st century. The PLM monolith might remain, but supported by an array of micro services inside and outside the company (next to IoT, hopefully also external data sets).

I believe there is no need to draw a PLM line. As Peter’s article: Digitalisation enabled by product lifecycle management already illustrated there is a need for a product information backbone along the whole (circular) lifecycle, where product information can interact with other enterprise platforms, like CRM, ERP and MES and BI services. Sometimes we will see overlapping functionality, sometimes we will see the need to bridge the information through Microservices. As long as these bridges are data-driven and do not need manual handling/transformation of data, they fit in the future, lean digital enterprise.

Conclusion:

This can be an ongoing dialogue, diving into detailed topics of a modern PLM approach. I am curious to learn from my readers, how engaged they are in this topic? Do you still take part in PLM dialogues or do you consume? Do you have “tips and tricks” for those who want to shape the future of PLM?


Let your voice be heard! (and give Flip a break)

 

simple

In my previous post, I shared my thoughts Why PLM is the forgotten domain in digital transformation. Legacy data, (legacy) people and slow organizations are the main inhibitors to moving forward. Moreover, all this legacy makes it hard to jump on the digital wagon.

When you talk with vendors and implementers of PLM solutions, they will all focus on the fact that with their solution and support PLM is simple. It is simple because:plm-vendor_thumb.jpg

  • We have the largest market share in your industry segment
  • We have the superior technology
  • We are cloud-based
  • We are insane customizable
  • Gartner is talking about us
  • We have implemented at 100+ similar companies

For my customers, implementing PLM was never simple as every PLM implementation was driving a business change. In the early days of SmarTeam, we had the theme “We work the way you work”, which is in hindsight a very bad statement. You do not want to automate the way a company is currently working. You want to use a PLM implementation to support a business change.

Never implement the past, implement the future

And there are changes ……

When I was discussing PLM with my potential customers ten years ago, the world was different. PLM was in a transition from being a PDM-tool from engineering into an extended PDM-tool centered around product development. A major theme for this kind of implementations was to move from a document-driven environment towards an item-centric environment. Instead of managing documents (CAD files and other files like Excel) the implementation was based on providing a data continuity, where the item (the physical part or in SAP terms the material) would be the main information placeholder. The continuity is implemented around EBOMs and MBOMs and thanks to automation the MBOM can be connected to the ERP system in a continuous flow.

Just search for item-centric or BOM-centric, and you will find many references from vendors and consultants for this approach.  Implementing PLM item-centric is already a big step forward in efficiency and quality for companies. However,…

Never implement the past, implement the future

And there will be changes …..

youtube

Digital Transformation & PLM on YouTube

Digital transformation is changing the way we do business and is changing the way companies should organize their data. A BOM-centric approach is no longer the ultimate implementation concept. To support a digital enterprise, the next step is a model-based enterprise. The model (not necessary the 3D-model) and its maturity and configurations are intended to be the reference for an organization. The model and its representation can connect hardware and software in a data-driven environment through the whole lifecycle. A model is needed to support smart manufacturing and the digital twin concept.There are many impressive marketing movies on YouTube explaining how companies/vendors implement digital continuity. Unfortunate the gap between marketing and reality is big at this time because moving to a model based enterprise is not an easy step. Coming back to the LEGACY-statement at the beginning of this post, it is not simple.

We all have to learn

PDT2017Digital transformation is just starting in the domain of PLM. Sharing and collecting knowledge is crucial, independent from particular solutions. For me, the upcoming PDT-conference in October is going to be a reference point where we are on this journey. In case your company has the experience to share related to this topic, please react to this link: http://pdteurope.com/call-for-abstract-now-open/

In case you want to learn and believe it is not simple, wait till the program it will be announced. The PDT conference has always been a conference where details are discussed. Looking forward and discuss with you.

Conclusion

Implementing and continuing with PLM is not simple for a company due to changes in paradigms. Digital transformation forces companies to investigate the details how to make it happen. Implementing PLM in scope of a digital transformation requires learning and time, not products first.

A month ago I attended PI Berlin 2017 and discussed how digital transformation should affect PLM. You can find the presentation here on Slideshare.  One of the conclusions of my presentation was that PLM is the forgotten domain in digital transformation, which lead to the tweet below from Nick Leeder from SKF.

PI-tweet

I am from the generation who believes answering complex issues through tweets is not a best practice. Therefore, I dedicate this post to answer Nick’s question.

Digital Transformation

OldTicket.pngA digital enterprise is the next ultimate dream after the paperless office. Where the paperless office was focusing on transforming paper-based information into electronic information, there was not a mind-shift in the way people could work. Of course, when information became available in an electronic format, you could easily centralize it and store in places accessible to many others. Centralizing and controlling electronic information is what we did in the previous century with document management, PDM, and classical PLM.  An example: your airline ticket now provided as a PDF-file – electronic, not digital.

This process is not a digital transformation

dig_ticketDigital Transformation means that information is broken down into granular information objects that can be stored in a database in the context of other information objects. As they have a status and/or relation to other information objects, in a certain combination they bring, in real-time, relevant information to a user. The big difference with electronic information is that the content does not need a person to format, translate or pre-process the data. An example: your boarding app, showing the flight, the departure time, the gate all in real-time. If there is a change, you are immediately updated.

 

Digital Transformation for an enterprise

In a digital enterprise, information needs to be available as granular information objects related to each other providing the end-to-end continuity of data. End-to-end continuity does not mean that all data is stored in a single environment. The solution can be based on digital platforms working together potentially enriched by “micro-services” to cover specific gaps the digital platforms do not deliver.

ERP platformERP systems by nature have been designed to be digital. Logistical information, financial information, part information for scheduling, etc., all is managed in database tables, to allow algorithms and calculations to take place in real-time. Documents are generated to store snapshots of information (a schedule / a report), or there are pointers to documents that should contain digital, unmanaged information, like contracts, drawings, models. Therefore, the digital transformation does not impact ERP so much.

IOTCustomer connected platforms are a typical new domain for manufacturers, as this is where the digital transformation takes place in business. Connecting either to your products in the field or connecting to your consumers in the market have been the typical business changes almost every manufacturer is implementing, thanks to IoT and thanks to global connectivity. As this part of the business is new for a company, there is no legacy to deal with and therefore exciting to present to the outside world and the management.

The problem of legacy

And here comes the problem why companies try to neglect their PLM environments. There is so much legacy data, stored in documents (electronic formats) that cannot be used in a digital PLM environment. Old PLM quality processes were about validating documents, the container of information, not about the individual information objects inside the document. And when information changes, there is no guarantee the document is going to be updated, due to economic reasons (time & resources)

IntNumber.jpgTo give an example. A year ago I wrote a post:  The Impact of Non-Intelligent Part Numbers where I explained in a digitally connected enterprise part numbers no longer need to have a meaning. As long as they are unique throughout the enterprise, automation will take care PLM, and ERP are connected. In one of the comments to this post, a reader mentioned that they were implementing now non-intelligent numbers in their company and the ERP consultant recommended to renumber all the old part numbers to have a clean start. From the ERP point of view, no issue. The consultant probably never had learned about the fact that part numbers are used in drawings, instructions, spare part manuals, which are all documents in the engineering domain. Renumbering them would be a waste of resources and money, just to have a “pure” part number. In the world of PLM, you have to deal with legacy.

The need for business transformation

Companies currently do not fully recognize that the old way of working in PLM, based on a document-driven approach, is not compatible with a modern data-driven approach. The old approach makes documents the formal decision carrier for product information. Documents are reviewed and approved and once approved stored. When information is changing, documents are most of the time not updated due to the cost of maintaining all these versions of documents in the context of the related products. Documents lock information and do not guarantee the information inside the document remains actual.

In a data-driven environment, we work in a much more granular manner, directly with the data. Working data-driven reduces the need for people in the organization to collect and transform information into documents for further communication.

GartnerWorkforce

As both approached do not match in a single business process or a single PLM system, the challenge for companies is to decide how to keep the old environment available and meanwhile introducing the new data-driven approach for PLM. Customizing this upon your old PLM environment would be a problem for the future as customizations are hard to maintain, in particular, if these are the customizations that need to support the future.

Building everything in a new environment, designed for a data-driven approach, will also be a guarantee for failure. The old data, stored in documents, does not have the granular quality a data-driven environment needs.

Combined with the fact that different people will be needed to support old or new businesses, the topic of solving PLM for the future is not an easy one.

And when things are not easy, it is hard to find the right support for changes. Management usually does not spend enough time to understand the big picture; politics come into play.

Unfortunately, it’s usually safer and better for one’s career to cut costs a little further than to try to hit the rare innovation homerun

Quote from Political Realities of PLM-Implementation Projects in Engineering.com

Conclusion

Why PLM is the forgotten domain in digital transformation is quite understandable, although it requires more than a tweet to picture the full story.  Understanding the reasons is the first step, making PLM part of the digital transformation is the main challenge – who has the energy and power to lead?

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