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This post is a rewrite of an article I wrote on LinkedIn two years ago and modified it to my current understanding. When you are following my blog, in particular, the posts related to the business change needed to transform a company towards a data-driven digital enterprise, one of the characteristics of digital is about the real-time availability of information. This has an impact on everyone working in such an organization. My conversations are in the context of PLM (Product Lifecycle Management) however I assume my observations are valid for other domains too.

Real-time visibility is going to be the big differentiator for future businesses, and in particular, in the PLM domain, this requires a change from document-centric processes towards data-driven processes.

Documents have a lot of disadvantages.  Documents lock information in a particular format and document handling results in sequential processes, where one person/one discipline at the time is modifying or adding content. I described the potential change in my blog post: From a linear world to fast and circular?

From a linear world to fast and circular

In that post, I described that a more agile and iterative approach to bring products and new enhancements to the market should have an impact on current organizations. A linear organization, where products are pushed to the market, from concept to delivery, is based on working in silos and will be too slow to compete against future, modern digital enterprises. This because departmental structures with their own hierarchy block fast moving of information, and often these silos perform filtering/deformation of the information.  It becomes hard to have a single version of the truth as every department, and its management will push for their measured truth.

A matching business model related to the digital enterprise is a matrix business model, where multi-disciplinary teams work together to achieve their mission. An approach that is known in the software industry, where parallel and iterative work is crucial to continuous deliver incremental benefits.

Image:  21stcenturypublicservant.wordpress.com/

In a few of my projects, I discovered this correlation with software methodology that I wanted to share. One of my clients was in the middle of moving from a document-centric approach toward a digital information backbone, connecting the RFQ phase and conceptual BOM through design, manufacturing definition, and production. The target was to have end-to-end data continuity as much as possible, meanwhile connecting the quality and project tasks combined with issues to this backbone.

The result was that each individual had a direct view of their current activities, which could be a significant quantity for some people engaged in multiple projects.  Just being able to measure these numbers already lead to more insight into an individual’s workload. At the time we discussed with the implementation team the conceptual dashboard for an individual, it lead to questions like: “Can the PLM system escalate tasks and issues to the relevant manager when needed?” and  “Can this escalation be done automatically? “

And here we started the discussion. “Why do you want to escalate to a manager?”  Escalation will only give more disruption and stress for the persons involved. Isn´t the person qualified enough to make a decision what is important?

One of the conclusions of the discussion was that currently, due to lack of visibility of what needs to be done and when and with which urgency, people accept things get overlooked. So the burning issues get most of the attention and the manager’s role is to make things burning to get it done.

When discussing further, it was clear that thanks to the visibility of data, real critical issues will appear at the top of an individual’s dashboard. The relevant person can immediately overlook what can be achieved and if not, take action. Of course, there is the opportunity to work on the easy tasks only and to ignore the tough ones (human behavior) however the dashboard reveals everything that needs to be done – visibility. Therefore if a person learns to manage their priorities, there is no need for a manager to push anymore, saving time and stress.

The ultimate conclusion of our discussion was: Implementing a modern PLM environment brings first of all almost 100 % visibility, the single version of the truth. This new capability breaks down silos, a department cannot hide activities behind their departmental wall anymore. Digital PLM allows horizontal multidisciplinary collaboration without the need going through the management hierarchy.

It would mean Power to People, in case they are stimulated to do so. And this was the message to the management: “ you have to change too, empower your people.”

What do you think – will this happen? This was my question in 2015.  Now two years later I can say some companies have seen the potential of the future and are changing their culture to empower their employees working in multidisciplinary teams. Other companies, most of the time with a long history in business, are keeping their organizational structure with levels of middle management and maintain a culture that consolidates the past.

Conclusion

A digital enterprise empowers individuals allowing companies to become more proactive and agile instead of working within optimized silos. In silos, it appears that middle management does not trust individuals to prioritize their work.  The culture of a company and its ability to change are crucial for the empowerment of individuals The last two years there is progress in understanding the value of empowered multidisciplinary teams.

Is your company already empowering people ? Let us know !

Note: After speaking with Simon, one of my readers who always gives feedback from reality, we agreed that multidisciplinary teams are very helpful for organizations. However you will still need a layer of strategic people securing standard ways of working and future ways of working as the project teams might be to busy doing their job. We agreed this is the role for modern middle management.
DO YOU AGREE ?

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PDT Europe is over, and it was this year a surprising aligned conference, showing that ideas and concepts align more and more for modern PLM. Håkan Kårdén opened the conference mentioning the event was fully booked, about 160 attendees from over 19 countries. With a typical attendance of approx. 120 participants, this showed the theme of the conference: Continuous Transformation of PLM to support the Lifecycle Model-Based Enterprise was very attractive and real. You can find a history of tweets following the hashtag #pdte17

Setting the scene

Peter Bilello from CIMdata kicked-off by bringing some structure related to the various Model-Based areas and Digital Thread. Peter started by mentioning that technology is the least important issue as organization culture, changing processing and adapting people skills are more critical factors for a successful adoption of modern PLM. Something that would repeatedly be confirmed by other speakers during the conference.

Peter presented a nice slide bringing the Model-Based terminology together on one page. Next, Peter took us through various digital threads in the different stages of the product lifecycle. Peter concluded with the message that we are still in a learning process redefining optimal processes for PLM, using Model-Based approaches and Digital Threads and thanks (or due) to digitalization these changes will be rapid. Ending with an overall conclusion that we should keep in mind:


It isn’t about what we call digitalization; It is about delivering value to customers and all other stakeholders of the enterprise

Next Marc Halpern busted the Myth of Digital Twins (according to his session title) and looked into realistic planning them. I am not sure if Marc smashed some of the myths although it is sure Digital Twin is at the top of the hype cycle and we are all starting to look for practical implementations. A digital twin can have many appearances and depends on its usage. For sure it is not just a 3D Virtual model.

There are still many areas to consider when implementing a digital twin for your products. Depending on what and how you apply the connection between the virtual and the physical model, you have to consider where your vendor really is in maturity and avoid lock in on his approach. In particular, in these early stages, you are not sure which technology will last longer, and data ownership and confidentially will play an important role. And opposite to quick wins make sure your digital twin is open and use as much as possible open standards to stay open for the future, which also means keep aiming for working with multiple vendors.

Industry sessions

Next, we had industry-focused sessions related to a lifecycle Model-Based enterprise and later in the afternoon a session from Outotec with the title: Managing Installed Base to Unlock Service opportunities.

The first presentation from Väino Tarandi, professor in IT in Construction at KTH Sweden presented his findings related to BIM and GIS in the context of the lifecycle, a test bed where PLCS meets IFC. Interesting as I have been involved in BIM Level 3 discussions in the UK, which was already an operational challenge for stakeholders in the construction industry now extended with the concept of the lifecycle. So far these projects are at the academic level, and I am still waiting for companies to push and discover the full benefits of an integrated approach.

Concepts for the industrial approach could be learned from Outotec as you might understand later in this post. Of course the difference is that Outotec is aiming for data ownership along the lifecycle, where in case of the construction industries, each silo often is handled by a different contractor.

Fredrik Ekström from Swedish Transport Administration shared his challenges of managing assets for both road and railway transport – see image on the left. I have worked around this domain in the Netherlands, where asset management for infrastructure and asset management for the rail infrastructure are managed in two different organizations. I believe Fredrik (and similar organizations) could learn from the concepts in other industries. Again Outotec’s example is also about having relevant information to increase service capabilities, where the Swedish Transport Administration is aiming to have the right data for their services. When you look at the challenges reported by Fredrik, I assume he can find the answers in other industry concepts.

Outotec’s presentation related to managing installed base and unlock service opportunities explained by Sami Grönstrand and Helena Guiterrez was besides entertaining easy to digest content and well-paced. Without being academic, they explained somehow the challenges of a company with existing systems in place moving towards concepts of a digital twin and the related data management and quality issues. Their practical example illustrated that if you have a clear target, understanding better a customer specific environment to sell better services, can be achieved by rational thinking and doing, a typical Finish approach. This all including the “bi-modal approach” and people change management.

Future Automotive

Ivar Hammarstadt, Senior Analyst Technology Intelligence for Volvo Cars Corporation entertained us with a projection toward the future based on 160 years of automotive industry. Interesting as electrical did not seem to be the only way to go for a sustainable future depending on operational performance demands.

 

Next Jeanette Nilsson and Daniel Adin from Volvo Group Truck shared their findings related to an evaluation project for more than one year where they evaluated the major PLM Vendors (Dassault Systemes / PTC / Siemens) on their Out-of-the-box capabilities related to 3D product documentation and manufacturing.

They concluded that none of the vendors were able to support the full Volvo Truck complexity in a OOTB matter. Also, it was a good awareness project for Volvo Trucks organization to understand that a common system for 3D geometry reduces the need for data transfers and manual data validation. Cross-functional iterations can start earlier, and more iterations can be performed. This will support a shortening of lead time and improve product quality. Personally, I believe this was a rather expensive approach to create awareness for such a conclusion, pushing PLM vendors in a competitive pre-sales position for so much detail.

Future Aerospace

Kenny Swope from Boeing talked us through the potential Boeing journey towards a Model-Based Enterprise. Boeing has always been challenging themselves and their partners to deliver environments close to what is possible. Look at the Boeing journey and you can see that already in 2005 they were aiming for an approach that most of current manufacturing enterprises cannot meet. And now they are planning their future state.

To approach the future state Boeing aims to align their business with a single architecture for all aspects of the company. Starting with collecting capabilities (over 400 in 6 levels) and defining value streams (strategic/operational) the next step is mapping the capabilities to the value streams.  Part of the process would be to look at the components of a value stream if they could be fulfilled by a service. In this way you design your business for a service-oriented architecture, still independent from any system constraints. As Kenny states the aerospace and defense industry has a long history and therefore slow to change as its culture is rooted in the organization. It will be interesting to learn from Kenny next hear how much (mandatory) progress towards a model-based enterprise has been achieved and which values have been confirmed.

Gearing up for day 2

Martin Eigner took us in high-speed mode through his vision and experience working in a bi-modular approach with Aras to support legacy environments and a modern federated layer to support the complexity of a digital enterprise where the system architecture is leading. I will share more details on these concepts in my next post as during day 2 of PDT Europe both Marc Halpern and me were talking related to this topic, and I will combine it in a more extended story.

The last formal presentation for day one was from Nigel Shaw from Eurostep Ltd where he took us through the journey of challenges for a model-based enterprise. As there will not be a single model that defines all, it will be clear various models and derived models will exist for a product/system.  Interesting was Nigel’s slide showing the multiple models disciplines can have from an airplane (1948). Similar to the famous “swing” cartoon, used to illustrate that every single view can be entirely different from the purpose of the product.

Next are these models consistent and still describing the same initial specified system. On top of that, even the usage of various modeling techniques and tools will lead to differences in the system. And the last challenge on top is managing the change over the system’s lifecycle. From here Nigel stepped into the need for digital threads to govern relations between the various views per discipline and lifecycle stage, not only for the physical and the virtual twin.  When comparing the needs of a model-based enterprise through its lifecycle, Nigel concluded that using PLCS as a framework provides an excellent fit to manage such complexity.

Finally, after a panel discussion, which was more a collection of opinions as the target was not necessary to align in such a short time, it was time for the PDT dinner always an excellent way to share thoughts and verify them with your peers.

Conclusion

Day 1 was over before you knew it without any moment of boredom and so I hope is also this post. Next week I will close reviewing the PDT conference with some more details about my favorite topics.

 

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

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)

 

simpleMy recent posts were around the words Simple (PLM is not simple) and Simplicity  (Human Beings, PLM and Simplicity).  Combined with a blog dialogue with Oleg Shilovitsky (Small manufacturers and search of simple solutions)  and comments to these posts, the theme Simple has been discussed in various ways. Simple should not be confused with Simplicity. The conclusion: A PLM implementation should reduce complexity for an organization, aiming for increasing simplicity. The challenge: Achieving more simplicity is not simple (the picture related to this paragraph)

What does simplicity mean in the context of PLM?

My definition would be that compared to the current state, the future state should bring measurable benefits by reducing or eliminating non-value added activities. Typical non-value added PLM activities are collecting data from various disciplines to get a management understanding, conversion of file formats to support other disciplines or collecting and distributing data for change and approval processes.

If you can reduce or eliminate these steps, significant benefits can be achieved: reducing iterations, increasing quality and (re)acting faster to changes. These benefits are the whole idea behind Digital PLM. See Accenture’s explanation or read my post: Best Practices or Next Practices.DigitalPLM

Simplicity comes from the fact that the user does not need to depend on intermediate people or data formats to have an understanding of “the best so far truth.” Empowered users are a characteristic of modern digital processes. Empowered users need to have different skills than persons working in a traditional environment where exchange and availability of information are more controlled through communication between silos.  Some people can make the change, some will never make the change.

What can you do?

On LinkedIn, I found some good suggestions from Peter Weis in his CIO article: The most painful, gut-wrenching part of leading transformation. Peter’s post is about the challenges within a company going through a transformation and to keep the pace. My favorite part:

For me, the most difficult and gut-wrenching part of leading our transformation was not the technology involved. It was making and acting on those tough decisions about who was not going to succeed. In some cases, people had been with the company for decades and had been rewarded and encouraged for the very work they were no longer required to do. These were good people, skilled talent, who provided a great service to the company – but the technology and the cultural gap were just too wide for them to bridge.

Peter describes a dilemma that many of us consultants should face when implementing a business change. Keeping on board all employees is a mission impossible. But what if you want to keep them all on board?

Reducing complexity by making the system rigid?

One of the companies, I am currently working with, decided to keep all employees on board by demanding for a PLM system that is so rigid and automated that a user cannot make mistakes or wrong decisions. For example: Instead of allowing the user to decide which approval path should be chosen, the predefined workflow should be started where all participants are selected by automation. The idea: reducing the complexity for the (older) user. The user does not have to learn how to navigate in a new environment to decide what is the best option. There is always one option. Simple isn’t it?

I believe it reduces any user to a person that clicks on buttons and writes some comments. It is not about real empowerment.

There are two downsides to this approach

  • To make the PLM system, so incredibly rigid additional customizations are needed (which come with a cost). However more costly will be the upgrades in the future and the maintenance of every change in business process which is hard coded currently.
  • The system will be so rigid that even future, more digital native users, will dislike the system as it does not challenge them to think. Implementing the past or pushing for the future?

My challenge:

  • A rigid system creates the illusion that the system is secure and simple for the existing employees (who you do not want to challenge to change)
  • A rigid system leads by default to complexity in the future with high costs of change.

I am curious to learn how you would approach my challenge (a PLM consultant’s challenge)
Making the customer happy or being the “bad news” guy who creates fear for the future?
I assume a topic many PLM consultants should face nowadays – your opinion?

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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