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This is almost my last planned post related to the concepts of model-based. After having discussed Model-Based Systems Engineering (needed to develop complex products/systems including hardware and software) and Model-Based Definition (creating an efficient connection between Engineering and Manufacturing), my last post will be related to the most over-hyped topic: The Digital Twin

There are several reasons why the Digital Twin is over-hyped. One of the reasons is that the Digital Twin is not necessarily considered as a PLM-related topic. Other vendors like SAP (the network of digital twins), Oracle (Digital Twins for IoT applications)  and GE with their Predix-platform also contributed to the hype related to the digital twin. The other reason is that the concept of Digital Twin is a great idea for marketers to shine above the clouds. Are recent comment from Monica Schnitger says it all in her post 5 quick takeaways from Siemens Automation summit. Monica’s take away related to Digital Twin:

The whole digital twin concept is just starting to gain traction with automation users. In many cases, they don’t have a digital representation of the equipment on their lines; they may have some data from the equipment OEM or their automation contractors but it’s inconsistent and probably incomplete. The consensus seemed to be that this is a great idea but out of many attendees’ immediate reach. [But it is important to start down this path: model something critical, gather all the data you can, prove benefit then move on to a bigger project.]

Monica is aiming to the same point I have been mentioning several times. There is no digital representation and the existing data is inconsistent. Don’t wait: The importance of accurate data – act now !

What is a digital twin?

I think there are various definitions of the digital twin and I do not want to go in a definition debate like we had before with the acronyms MBD/MBE (Model Based Definition/Enterprise – the confusion) or even the acronym PLM (classical PLM or digital PLM ?). Let’s agree on the following high-level statements:

  • A digital twin is a virtual representation of a physical product
  • The virtual part of the digital twin is defined by what you want to analyze, simulate, predict related to the physical product
  • One physical product can have multiple digital twins, only in the ideal world there is potentially a unique digital twin for every physical product in the world
  • When a product interacts with the environment, based on inputs and outputs, we normally call them systems. When I use Product, it will be most of the time a System, in particular in the context of a digital twin

Given the above statements, I will give some examples of digital twin concepts:

As a cyclist I am active on platforms like Garmin and Strava, using a tracking device, heart monitor and a power meter. During every ride my device plus the sensors measure my performance and all the data is uploaded to the platform, providing me with a report where I drove, how fast, my heartbeat, cadence and power during the ride. On Strava I can see the Flybys (other digital twins that crossed my path and their performances) and I can see per segment how I performed considered to others and I can filter by age, by level etc.)

This is the easiest part of a digital twin. Every individual can monitor and analyze their personal behavior and discover trends. Additionally, the platform owner has all the intelligence about all cyclists around the world, how they perform and what would be the best performance per location. And based on their Premium offering (where you pay) they can give you advanced advise on how you can improve. This is the Strava business model bringing value to the individual meanwhile learning from the behavior of thousands. Note in this scenario there is no 3D involved.

Another known digital twin story is related to plants in operation. In the past 10 years I have been advocating for Plant Lifecycle Management (PLM for Owner/Operators), describing the value of a virtual plant model using PLM capabilities combined with Maintenance, Repair and Overhaul (MRO) in order to reduce downtime. In a nuclear environment the usage of 3D verification, simulation and even control software in a virtual environment, can bring great benefit due to the fact that the physical twin is not always accessible and downtime can be up to several million per week.

The above examples provide two types of digital twins. I will discuss some characteristics in the next paragraphs.

Digital Twin – performance focus

Companies like GE and SAP focus a lot on the digital twin in relation to the asset performance. Measuring the performance of assets, compare their performance with other similar assets and based on performance characteristics the collector of the data can sell predictive maintenance analysis, performance optimization guidance and potentially other value offerings to their customers.

Small improvements in the range of a few percents can have a big impact on the overall net results. The digital twin is crucial in this business model to build-up knowledge, analyze and collect it and sell the knowledge again. This type of scenario is the easiest one. You need products with sensors, you need an infrastructure to collect the data and extract and process information in a manner that it can be linked to a behavior model with parameters that influence the model.

Image SAP blogs

This is the model-based part of the digital twin. For a single product there can be different models related to the parameters driving your business. E.g. performance parameters for output, parameters for optimal up-time (preventive maintenance – usage optimization) or parameters related to environmental impact, etc..) Building and selling the results of such a model is an add-on business, creating more value for your customer combined with creating more loyalty. Using the digital twin in the context of performance focus does not require a company to change the way they are working totally.  Yes, you need new skills, data collection and analysis, and more sensor technology but a lot of the product development activities can remain the same (for the moment).

As a conclusion for this type of digital twin I would state, yes there is some PLM involved, but the main focus is on business execution.

Due to the fact that I already reach more than 1000 words, I will focus in my next post on the most relevant digital twin for PLM. Here all disciplines come together. The 3D Mechanical model, the behavior models, the embedded and control software, (manufacturing) simulation and more. All to create an almost perfect virtual copy of a real product or system in the physical world. And there we will see that this is not as easy, as concepts depend on accurate data and reliable models, which is not the case currently in most companies in their engineering environment.

 

Conclusion

Digital Twin is a marketing hype however when you focus on only performance monitoring and tuning it becomes a reality as it does not require a company to align in a digital manner across the whole lifecycle. However this is just the beginning of a real digital twin.

Where are you in your company with the digital twin journey?

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The problem with a TLA is that there is a limited number of combinations that make sense. And even once you have found the right meaning for a TLA, like PLM you discover so many different interpretations.

myplmFor PLM I wrote about this in my post PLM misconceptions –: PLM = PLM ?
I can imagine an (un)certain person, who wants to learn about PLM, might get confused (and should be – if you take it too serious).

At the end your company’s goal should be how to drive innovation, increase profitability and competiveness and not about how it is labeled.

As a frequent reader of my blog, you might have noticed I wrote sometimes about ALM and here a similar confusion might exist as there are three ALMs that might be considered in the context I am blogging.

Therefore this post to clarify which ALM I am dedicated to.
So first I start with the other ALMs:

ALM = Application Lifecycle Management

SWThis is an upcoming discipline in the scope of PLM due to the fact that more and more in the product development world embedded software becomes a part of the product. And like in PLM where we want to manage the product data through its lifecycle, ALM should become a logical part of a modern PLM implementation. Currently most of the ALM applications in this context are isolated systems dealing only with the software lifecycle, see this Wiki Page

ALM = Asset Lifecycle Management (operational)

In 2009 I started to focus on (my type of) ALM, called Asset Lifecycle Management, and I discovered the same confusion as when you talk about a BOM. What BOM really means is only clear when you understand the context. Engineers will usually think of an Engineering BOM, representing product as specified by engineering (managed by PDM). Usually the rest of the organization will imagine the Manufacturing BOM, representing the product the way it will be produced (managed mostly in ERP).

ALM_operational diagramThe same is valid for ALM. The majority of people in a production facility, plant or managed infrastructure will consider ALM as the way to optimize the lifecycle of assets. This means optimizing the execution of the plant, when to service or replace an asset ? What types of MRO activities to perform. Sounds a lot like ERP and as it has direct measurable impact on finance, it is the area that gets most of the attention by the management.

ALM = Asset Lifecycle Management (information management)

alm_1Here we talk about the information management of assets. When you maintain your assets only in a MRO system, it is similar like in a manufacturing company when only using an ERP system. You have the data for operations, but you do not have the process in place to manage the change and quality of data. In the manufacturing world this is done in PDM and PLM system and I believe owners/operators of plant can learn from that.

I wrote a few posts about this topic, see Asset Lifecycle Management using a PLM system, PLM CM and ALM – not sexy or using a PLM system for Asset Lifecycle Management requires a vision  and I am not going to rewrite them in this post. So get familiar with my thoughts if you read the first time about ALM  in my blog.

What I wanted to share is that thanks to modern PLM systems,  IT infrastructure/technologies and SBA it becomes achievable for owner/operators to implement an Asset Lifecycle Management vision for their asset information and I am happy to confirm that in my prospect and customer base, I see companies investing and building this ALM vision.

And why do they do this:

  • imageReduce maintenance time (incidental and planned) by days or weeks due to the fact that people have been working with the right and complete data. Depending on the type of operations, one week less maintenance can bring millions (power generation, high demand/high cost chemicals and more)

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  • imageReduce the failure costs dramatically. As maintenance is often a multi-disciplinary activity errors due to miscommunication are considered as normal in this industry (10 % up and even more).  It is exactly this multi-disciplinary coordination that PLM systems can bring to this world. And the more you can do in a virtual world the more you can assure you do the right thing during real maintenance activities. Here industries similar as for the previous bullet, but also industries where high-costly materials and resources are used, the impact on reducing failure costs is high.

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  • imageImprove the quality of data. Often the MRO system contains a lot of operational parameters that were entered there at a certain time by a certain person with certain skills – the fact that although I used the word certain three times, the result is uncertainty as there is no separate tracing and validation of the parameters per discipline and an uncertain person looking at the data might not discover there is an error, till it goes wrong. Here industries where a human error can be dramatic benefit the most from it (nuclear, complex chemical processes)

Conclusion: The PLM system based ALM implementations are more and more becoming reality next to the ALM operational world.  After spending more then three years focused on this area, I believe we can see and learn from the first results.

Are you interested in more details or do you want to share your experience ? Please let me know and I will be happy to extend the discussion

Note: On purpose I used as much TLA’s to assure it looks like an specialist blog, but you can always follow the hyperlink to the wiki explanation, when the TLA occurs the first time.

JOS

observation Although I am still active most of my time in ‘classical’ PLM, some of the projects I am involved with also deal with Asset Lifecycle Management. In general PLM focuses on a product development process, starting from a conceptual phase, going through planning, development and production. The PLM system serves as a collaboration and information backbone for all product IP (Intellectual Property). One of the main capabilities a PLM system provides is a ‘single version of the truth’.

And it is this capability, which makes a PLM system an excellent choice for Asset Lifecycle Management

Who practices Asset Lifecycle Management ?

alm Asset Lifecycle Management can be found at any location, where a company is maintaining a process – we call these companies Owners/ Operators. Best known industry for Asset Lifecycle Management is the Process & Power industry, where a company produces oil, energy or chemicals. However the same concept is also valid for water companies (water distribution process), food processing and infrastructure companies (railways, airports, roads)

All these companies have in common that they support a certain process and the challenge is, while being in operation, to optimize the process. During operation, maintenance and improvement activities should be as little as disruptive as possible.

A maintenance stop is very costly for Owner/Operators. Imagine a plant not producing fuel for two weeks (millions of liters) or a nuclear reactor not producing electricity for a month (millions of kilowatts) – no income. And no maintenance will lead to unexpected problems and in the worse case, disasters. So it is also about balancing these activities.

Let’s look at a definition of Asset Lifecycle Management

Asset Lifecycle Management is a balanced and active management of assets over the lifecycle, coupled with business objectives.

Simply said it translates into an approach, where based on business objectives (process stability, safety, margin) a company tries to optimize the usage of their assets (a reactor, a pump, a rail track, a road) through their individual lifecycles. This means perform preventive maintenance; renovate a part of the process and perform more parallel activities with a focus on improving the lifecycle of the process

So why not use a MRO system?

mro An MRO (Maintenance, Repair & Overhaul) system can be compared with an ERP system for manufacturing companies. The MRO system manages and schedules activities and resources on the plant, keeping track of maintenance activities done on inventory. But can it serve as the system providing the single version of the truth for all plant information? No!

So why not use an ERP system?

erp An ERP system is mostly used by owner/operators to control all financial transactions (contracts, purchasing, suppliers, projects/resources accounting). Some ERP vendors provide MRO functionality in a single system; still can this system provide the single version of truth for all plant information? Again I am sure it is not the case.

So why not use a document management system?

doc As most of the process information is stored in various types of documents, is seems to be appropriate to store all information in a document management system. And actually this is what owner/operators try to do, however they maintain inside their company different document management systems (paper archives, office documents in a specific system, engineering documents in another system, etc, etc). Each of the systems can provide a single version of the truth for specific content, however there is a consolidated single entry point for all asset data. Often the documents also do not reflect the status of an asset. Is the asset running in, is it active, is it demolished?

The tag number does not show it, and changing the status of an asset forces people to go through the various document systems to change the status there. An inefficient and costly procedure, not reliable and often not done.

So why not an integrated plant engineering system?

plant_eng Engineering plant software is designed to support the design collaboration and is mostly used by EPC contractors. These engineering companies are hired by the owner/operator to design and construct the plant or make major modifications of the plant. EPC contractors need to work as efficient as possible (to get the job), which means for them work as intelligent as possible in an integrated manner with tag numbers, P&IDs, 3D Equipment, Piping, ISOs. This intelligence leads to an application specific format and infrastructure.

During the hand-over of the plant or modification, this intelligence disappears as the owner/operator does not use the engineering plant software. They do not want to be dependent on a single software provider or version of the data. As data has to live for many years, sometimes 30 years or more, application specific data is hard to maintain. So as part of the hand-over data will be provided in neutral formats, worst case paper, but often in PDFs, TIFFs or other publishing format, losing all the intelligence.

15926 There is an intelligent, neutral format based on ISO 15926. This requires an investment from the EPC contractor and an investment from the owner/operator to manage all information in this format. For complex and long-lasting environments, like a nuclear plant, this approach surely pays off; however what you see is that on both sides (EPC and Owner/Operator) they try to minimize the costs on data handling/conversion. This leads in the long term to much more labor time internal at the owner/operator to manage and assure the data is accurate. But these costs somehow come later and are more hidden. And the question remains: can this system serve as the single version of truth for all plant information? No, plant engineering systems are too application specific

In addition, plant engineering software environments are not targeted to work integrated in an owner/operator environment, managing parallel projects and resources, quality processes and inventory statuses related to a certain asset and project.

So why not use a project management software system?

proj As in a plant many projects can run in parallel, it happens that they run on the same assets or locations in the plant. For engineers and maintenance it is important to have visibility on which projects have impact on each other. Project management software is not targeted to make data visible related to a collection of assets or locations. No, project management software can not be the system to serve as the single version of truth for all plant information.

So either we give up for looking a single version of the truth and pay the price for multiple software systems to maintain in the company and take the extra efforts for configuration management for granted, or we look at PLM ?

The PLM based solution

In the past 15 years I have done several projects with ENOVIA and projects where Asset Lifecycle Management was done with ENOVIA. For sure, other flexible PLM systems can do the same, as the solution lies in an adapted data model for ALM.

This picture shows what a PLM system can do:

alm_data_model

It can provide all related information (documents, inventory, locations, and projects) to an asset with one click from within single system. In addition it can also give the actual status of the asset. Assets are often identified by tag numbers, and the lifecycle of an asset can be managed by default in a PLM system, combined with Asset Change processes.

Best Practices coming from the PLM world can be used here too. The major challenge for PLM vendors is to reduce the complexity for data handling, as ALM users will not be engineers experienced to complex CAD environments. They are information workers, who need with a short learning curve, direct access to the data they require (and they should be sure the data is reliable)

Note: the PLM system will need to interface with the MRO and ERP system. Like in the classical PLM concept, MRO and ERP are the transactional systems, controlling the day to day activities, where the PLM system provides the accurate plant information (IP) required for an activity.

Also the PLM system will manage the non-standard activities through projects, change processes and will rely on accurate information from ERP.

The major benefits reported from implementations based on a PLM system are: roi

  • Reduced down-time for the plant, due to better planning and accurate information when preparing a maintenance stop. Less surprises with unforeseen delays of production.
  • More reliable and less effort to be complaint to safety, health, environment and governmental regulations as all information is available in a single, controlled and traceable environment
  • Lower cost of ownership for ALM. Instead of maintaining various silos of information and provide access to certain users, a single system with a common interface is available for most of the users.

 

Conclusion: Owner/Operators should look into the benefits a PLM system can bring for them. Interesting the benefits are not based on the integration of product development, but on providing accurate information from different entry points for different roles

 I am curious to learn who has seen a similar approach – feel free to comment

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