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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 ?

 

 

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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

Potential digital transformation is everywhere. This time I want to share a personal story based on my IoT cycling device from Garmin. Several years ago I became an enthusiastic cyclist, mainly because it clears your mind and cycling keeps you in good shape after enjoying customer visits with great dinners and excellent breakfasts. As the Dutch lack real mountains, we challenge ourselves with through open fields with strong winds to suffer a little too.

 

Four years ago, started tracking my cycling performance, with a Garmin Edge 810. The story of my Garmin is a real IoT story. GPS trackers, in the beginning, did not communicate with the outside world. Now, this device connects to sensors registering my speed, my location, my heart rate, pedal cadence and produced power at any time, finally uploading it to the Garmin Connect platform.

The IoT platform

The Garmin Connect platform gives me insights on my performance, activities, and segments. The segment demonstrates the social part of the platform. Here you can see how you rank with others who have cycled the same track segment over time. And you can register your own preferred segment too, where you challenge yourself and others in your area. So the number of segments is growing continuously. Imagine all these cyclists around the world virtually sharing and taking the same track. I am curious to learn from Garmin how many people are connected to the platform.
I could not find these numbers. You?

The fun of segments

Digital Twin

Through the platform, Garmin collects huge amounts of data of connected users. Each data set of the connected user could be considered a simple digital twin. The Connect platform provides me insights about my overall performance through the years through various reports. Garmin could offer as a (paid) service to deliver insights of my performance compared to other users and propose predictive enhancements similar to the GE Predix platform. The difference of course that 1 % performance improvement for me in cycling does not bring the same value as 1 % performance improvement of a GE product (turbine, jet engine, train, …). However, the concept is the same and GE is promoting themselves as the next Digital Industrial Company, leading in digital transformation. Read more here.

Digital Twin performance

Connecting to the customer

Tthe change from moving from a document-driven approach towards a data-driven approach to collect and store information is not the main concept behind a digital transformation. The data-driven approach is an enabler to connect directly to the customer and change the current business model from delivering products into a business model delivering services or even more advanced delivering experiences. Services and experiences create a closer relation to the customer, more loyalty, but also the challenge that you need to connect to the customer in such a way that the customer sees value. Otherwise, the customer will switch to another service or experience. The Apple, Nespresso, Uber experiences are all known for their new ways of connecting to the customer, differentiating from traditional product sales. Garmin could also be on that list. However, I discovered they are not there yet, despite an IoT-platform and connected devices. What is missing?

Why Garmin is not a digital enterprise.

Two years ago my Garmin Edge started crashing in the middle of a ride. The system rebooted after some minutes, and the recordings were lost or at least unreadable.  When I contacted Garmin support their standard response was: “Please reset the device and update to the latest software.” Two years ago the software had still bug fixes. After two years you would expect a stable experience.

However, a year ago the problems started to become more frequent. I started to send log files illustrating where the error occurred. Still, the Garmin response was the same: “Please reset the device and update to the latest software.”
However as there were no new software updates, there must be another reason why the device failed more and more.

After pushing for a resolution, the service department concluded I needed a new device. There might be an issue with the hardware. A little bit skeptical I agreed on a hardware switch again, and as expected this did not solve the crashes. My guess is that due to the increasing amount of segments at some places, the software gets confused where the rider is exactly located and in which direction the rider is going. These are the moments when the crash happens, and this is probably a software issue.

Still, the Garmin help desk believes there is a hardware problem (preferably swap the device) where I kept on providing evidence data of crashes to support Garmin in their error-discovery. Till now there is no resolution. The good news is that Garmin support mentioned investigating further.

For me, the interaction with Garmin illustrates that the company internally is not yet digital transformed. The service desk probably has KPIs (Key Performance Indicators) related to their response time and problem resolution time. Although I can debate the response time, it is clear that the problem resolution approach: Update to the latest software and if this does not work swap to a new device is not increasing the knowledge from Garmin as a company what their customers are experiencing.

Apparently, their software management is disconnected from the service department and customers. Only clear bugs during the first launch are fixed. Next, it is a disconnected world again.

A must for a digital enterprise is to dive into customer issues and to connect them back to R&D, both for the hardware part and software part. Something a modern product manager would do. If a company is not able to understand the multidisciplinary dependencies and solve issues from the field (with some effort), they will keep on making the same mistakes again with new product launches and lose customers who are looking for a better experience.

My conclusion

PLM should be part of the digital enterprise too as this is the only way to deliver consistent customer value and positive experience. It requires companies to break down silos and create multidisciplinary teams that are capable of supporting the full customer journey. A digital device and a digital customer platform are just facades to the outside world – the inside needs to change too.

What do you think?
Does your company understand the challenges to transform across all disciplines?
Are you managing PLM, ALM, and IoT in context of the product and across the whole lifecycle?
I am curious !

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)

 

GettyImages-157335388[1]Last week I shared my observation from day 1 of the PI Berlin 2017 conference. If you have not read this review look here: The weekend after PI Berlin 2017.

Day 1 was the most significant day for me. I used the second day more for networking and some selective sessions that I wanted to attend. The advantage for the reader, this post is not as long as the previous one. Some final observations from day 2

PLM: The Foundation for Enterprise Digitalization

Peter Bilello from CIMdata gave an educational speech about digitalization and the impact of digitalization on current businesses. Peter considers digitalization as a logic next step in the PLM evolution process. See picture below.

clip_image002

Although it is an evolution process, the implementation of this next step requires a revolution. Digitalization will create a disruption in companies as the digital approach will reshape business models, internal business processes, roles and responsibilities. Peter further elaborated on the product innovation platform and its required characteristics. Similar to what I presented on the first day Peter concluded that we are in a learning stage how to build new methodology/infrastructure for PLM. For example, a concept of creating and maintaining a digital twin needs a solid foundation.
His conclusion: Digitalization requires PLM:

Boosting the value of PLM through
Advanced Analytics Assessment

autolivPaul Haesman from Autoliv introduced the challenges they have as a typical automotive company. Digitalization is reshaping the competitive landscape and the demands on more technology, still guaranteeing the highest safety levels of their products. In that context, they invited Tata Technologies to analyze their current PLM implementation and from there to provide feedback about their as-is readiness for the future.

Chris Hind from Tata Technologies presented their methodology where they provide benchmark information, a health check, impact and potential roadmap for PLM. A method that is providing great insights for both parties and I encourage companies that haven´t done such an assessment to investigate in such an activity. The major value of a PLM assessment is that it provides an agreed baseline for the company that allows management to connect the Why to the What and How. Often PLM implementations focus on What and How with not a real alignment to the Why, which results in unrealistic expectations or budgets due to the perceived value.

clip_image004

An interesting point address by Chris (see picture above) is that Document Management is considered as a trending priority !!!

It illustrates that digitalization in PLM has not taken off yet and companies still focusing on previous century capabilities 😦

The second highlight rating Manufacturing Process Management as the most immature PLM pillar can be considered in the same context. PLM systems are still considered engineering systems and manufacturing process management is in the gray area between PLM systems and ERP systems.

The last two bullets are clear. The roots of PLM are in managing quality and compliance and improving time to market.

Overcoming integration challenges –
Outotec´s Digital Journey

Outotec_RGBHelena Gutiérrez and Sami Grönstand explained in an entertaining manner the Outotec (providing technologies and services for the metal and mineral processing industries) company and their digital journey. Outotec has been working already for several years on simplifying their IT-landscape meanwhile trying to standardize in a modern, data-driven manner the flow of information.

Sami provided with great detail how the plant process definition is managed in PLM. The process definition is driven by the customer´s needs and largely defines the costs of a plant to build. Crucial for the quotation phase but also important if you want to create a digital continuity. Next, the process definition is further detailed with detailed steps, defining the key parameters characteristics of the main equipment.

ElephantAndAnts

And then the challenge starts. In the context of the plant structure, the right equipment needs to be selected. Here it is where plant meets product or as the Outotec team said where the elephant and ants do the tango.

In the end, as much as possible standardized products need to match the customer specific solution. The dream of most of these companies: combining Engineering To Order and Configure To Order and remember this in the context of digital continuity.

So far, a typical EPC (Engineering Procurement Construction) project, however, Outotec wants to extend the digital continuity to support also their customer´s installed plant. I remembered one of their quotes for the past: “Buy one (plant) and get two (a real one and a virtual one). “This concept managed in a digital continuity is something that will come up in many other industries – the digital twin.

clip_image008

Where companies like Outotec are learning to connect all data from the initiation of their customer specific solution through delivery and services, other product manufacturing companies are researching the same digital continuity for their product offerings to the field of consumers. Thanks to digitization these concepts become more and more similar. I wrote about this topic recently in my post PLM for Owner/Operators.

Final conclusion from PI Berlin 2017

It is evident participants and speakers are talking about the strategic value and role PLM can have an organization.

With digitalization, new possibilities arise where the need and value for end-to-end connectivity pop up in every industry.

We, the PLM community, are all learning and building new concepts. Keep sharing and meeting each other in blogs, forums, and conferences.

clip_image002At this moment I am finalizing my session for PDT2016 where I will talk about the importance of accurate data. Earlier this year I wrote a post about that theme: The importance of accurate data. Act now!

My PDT session will be elaborating on this post, with a focus on why and how we need this change in day-to-day business happen. So if you are interested in a longer story and much more interesting topics to learn and discuss, come to Paris on 9 and 10 November.

Dreaming is free

Recently I found a cartoon on LinkedIn and shared it with my contacts, illustrating the optimistic view companies have when they are aiming to find the best solution for their business, going through an RFI phase, the RFP phase, and ultimately negotiation the final deal with the PLM solution provider or vendor. See the image below:

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All credits to the author – I found this image here

The above cartoon gives a humoristic view of the (PLM) sales process (often true). In addition, I want to share a less optimistic view related to PLM implementations after the deal has been closed. Based on the PLM projects if have been coaching in the past, the majority of these projects became in stress mode once the stakeholders involved only focused on the software, the functions and features and centralizing data. Implementing the software without a business transformation caused a lot of discomfort.

clip_image005Users started to complain that the system did not allow them to do their day-to-day work in the same way. And they were right! They should have a new day-to-day work in the future, with different priorities based on the new PLM infrastructure.

This cultural change (and business change) was often not considered as the PLM system was implemented from an IT-perspective, not with a business perspective.

Over time, a better understanding of PLM and the fact that vendors and implementers have improved their portfolio and implementation skills, classical PLM implementations are now less disruptive.

A classical PLM implementation can be done quickly is because the system most of the time does not change the roles and responsibilities of people. Everyone remains working in his/her own silo. The difference: we store information in a central place so it can be found. And this approach would have worked if the world was not changing.

The digital enterprise transformation.

With the upcoming digitization and globalization of the market, enterprises are forced to adapt their business to become more customer-driven. This will have an impact on how PLM needs to be implemented. I wrote about this topic in my post: From a linear world to fast and circular. The modern digital enterprise has new roles and responsibilities and will eliminate roles and responsibilities that can be automated through a data-driven, rule-based approach. Therefore implementing PLM in a modern approach should be related (driven) by a business transformation and not the other way around!

Benefits realization

In the past two years, I have explained this story to all levels inside various organizations. And nobody disagreed. Redefining the processes, redefining roles was the priority. And we need a team to help people to make this change – these people are change management experts. The benefits diagram from Gartner as shown below was well understood, and most companies agreed the ambition should be to the top curve, in any case, stay above the red curve

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But often reality relates to the first cartoon. In the majority of the implementations I have seen the past two years, the company did not want to invest in change management, defining the new process and new roles first for an optimum flow of information. They spent the entire budget on software and implementation services. With a minimum of staff, the technology was implemented based on existing processes – no change management at all. Disappointing, as short-term thinking destroyed the long-term vision and benefits were not as large as they had been dreaming.

Without changing business processes and cultural change management, the PLM team will fight against the organization, instead of surfing on the wave of new business opportunities and business growth.

Conclusion

If your company is planning to implement modern PLM which implicit requires a business transformation, make sure cultural change management is part of your plan and budget. It will bring the real ROI. Depending on your company´s legacy, if a business transformation is a mission impossible, it is sometimes easier to start a new business unit with new processes, new roles and potentially new people. Otherwise, the benefits will remain (too) low from your PLM implementation.

I am curious to learn your experience related to (the lack) of change management – how to include it into the real scope – your thoughts ?

Addition:
As a reaction to this post, Oleg Shilovitsky wrote a related blog post: PLM and the death spiral of cultural change.  See my response below to this post as it will contribute to the understanding of this post

Oleg, thanks for contributing to the theme of cultural change. Your post illustrates that my post was not clear enough, or perhaps too short. I do not believe PLM is that difficult because of technology, I would even claim that technology is a the bottom of my list of priorities. Not stating it is not important, but meaning that when you are converging with a company to a vision for PLM, you probably know the kind of technologies you are going to use.

The highest priority to my opinion is currently the business transformation companies need to go through in order to adapt their business to remain relevant in a digital world. The transformation will require companies to implement PLM in a different manner, less silo-oriented, more focus on value flows starting from the customer.

Working different means cultural change and a company needs to allocate time, budget and energy to that. The PLM implementation is supporting the cultural change not driving the cultural change.

And this is the biggest mistake I have seen everywhere. Management decides to implement a new PLM as the driver for cultural change, instead of the result of cultural change. And they reason this is done, is most of the time due to budget thinking as cultural change is ways more complex and expensive than a PLM implementation.

 

 

myplmSorry guys, I am aware of the fact that the definition of PLM is very ambiguous. Every vendor, implementor and probably PLM consultant has a favorite definition. Just to illustrate this statement,  read Brain Soaper´s recent post: What are the top 5 things to know about PLM ?

Interesting Brian starts with stating the definition of PLM is priority #1, however as you can see from the comment session, it is all about having inside your company a common definition of PLM.

And now I start writing about digital PLM, again a definition. You might have read in my blog about classical PLM and modern PLM.

Classical PLM

classical PLMFor me, classical PLM is the way PLM has been implemented in the past 15 years, often as an extension of engineering with the purpose of centralizing and sharing information.

In particular for CAD data, classical PLM is focusing on managing files in a controlled way, through check-in and check-out mechanisms. On top of file management, classical PLM provides more data-driven functionality, like project management, process governance (workflows / approvals / ECx processes) and BOM management (to link to ERP).

Classical PLM can still bring great benefits to a company as time for searching, paper-based processes and data retyping in ERP can be avoided, leading to reuse and fewer errors. The ROI time for a classical PLM implementation lays between two years to three years; my observations from the past. This time can still vary a lot as not every company or implementor/vendor uses the ideal approach to implement PLM, due to cultural issues, wrong expectations or lack of experience from both parties.

The connotations I have with classical PLM are:
linear, rigid, mechanical,(old) automotive, previous century

Modern PLM = Digital PLM

InfoInContextModern PLM is based on the vision that all information should be managed and stored as data objects, not necessary in a single system. Still the PLM infrastructure, using structured and unstructured data, should give each user in the organization with almost real-time information in context of other relevant information.

My non-stop blog buddy Oleg recently wrote a post in that context: Data as a platform & future manufacturing intelligence. Oleg is nicely describing some of the benefits of a data-driven approach.

Accenture provides insight with their infographic related to Digital PLM. Read it here as it is very concise and gives you a quick impression what Digital PLM means for an organization. Here is my favorite part, showing the advantages.

accenture digital PLM

The substantial advantages from digital PLM are all coming from the fact that information is stored as data objects, all having their individual versions, relations and status. The advantage of data elements is that they are not locked in a document or specific file format. Information can flow to where or whom needed without translation.

The connotations I have with digital PLM are:
real-time, data continuity, flexible, software and future.

 

Still some caution:

Reported ROI numbers for digital PLM are significant larger than classical PLM and I observed some facets of that. Digital PLM is not yet established and requires a different type of workforce. See other blog post I wrote about this theme: Modern PLM brings Power to the People.

But what about digital PLM – where is the word digital relevant ?

ETO – model-based engineering

Where to focus first depends very much on your company´s core business process. Companies with an Engineering To Order (ETO) process will focus on delivering a single product to their customer and most of the time the product is becoming more like a system, interacting with the outside world.

Big challenges in ETO are to deliver the product as required, to coordinate all disciplines preferable in a parallel and real-time manner – in time – on budget. Here a virtual model that can be accessed and shared with all stakeholders should be the core. The construction industry is introducing BIM for this purpose (a modern version of DMU). The virtual model allows the company to measure progress, to analyze and simulate alternatives without spending money for prototypes. In the ideal world engineering and simulation are done on the same model, not losing time and quality on data translations and iterations.

The virtual model linked to requirements, functions and the logical definition allows virtual testing – so much cheaper and faster and therefore cost efficient. Of course this approach requires a change in how people work together, which is characteristic for any digital business. Breakdown the silos.

Typical industries using the ETO model: Construction, Energy, Offshore, Shipbuilding, Special Equipment

 

CTO – model-based manufacturing

In a Configure To Order (CTO) business model you do not spend time for engineering anymore. All options and variants are defined and now the focus is on efficient manufacturing. The trend for CTO companies is that they have to deliver more and more variants in a faster and more demanding global market. Here the connectivity between engineering data and manufacturing data becomes one of the cornerstones of digital PLM. Digital PLM needs to make sure that all relevant data for execution (ERP and MES) is flowing through the organization without reformatting or reworking the data.

The digital thread is the dream. Industry 4.0 is focusing on this part. Also in the CTO environment it is crucial to work with a product model, so all downstream disciplines can consume the right data. Although in CTO the company´s attention might go to MES and ERP, it is crucial that the source of the product model is well specified and under control from (dgital) PLM.

Typical CTO industries are: Automotive, Consumer Goods, High-Tech, Industrial Equipment

BTO – models everywhere

flexibleIf your company has a Build To Order main delivery process, the optimum for digital PLM lies in the middle of ETO and CTO, depending on the type of products your company delivers.

In BTO there is always engineering to do. It can be customer specific engineering work (only once) or it can be changing/ adding new features to the product.

Modularity of the product portfolio might be the answer for the first option, where the second option requires strong configuration management on the engineering side, similar to the ETO model. Although the dream of many BTO companies is to change a CTO company, I strongly believe change in technology and market requirements will always be faster than product portfolio definition.

pointETO, BTO and CTO are classical linear business models. The digital enterprise is changing these models too. Customer interaction (myProduct), continuous upgrade and feedback of products (virtual twin), different business models (performance as a service) all will challenges organizations to reconsider their processes.

Digital PLM utilizing a model-based or model-driven backbone will be the (potential) future for companies as data can be flowing through the organization, not locked in documents and classical processes. In my upcoming blog post I will spend some more time on the model-based enterprise.

Conclusion:
It depends on your company´s core business process where the focus on a model-based enterprise supported by (digital) PLM benefits the most. In parallel business models are changing which means the future must be flexible.

Digital PLM should be one of your company´s main initiatives in the next 5 years if you want to stay competitive (or relevant)

 

What do you think ? Am I too optimistic or too pessimistic ?

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