The Digital Twin Time Machine

Attaining individual context is the key to a profitable future

A time machine is an interesting metaphor for the Digital Twin. When built correctly, coupled with constant upkeep – it has the potential to change the way companies approach product design, development, manufacturing, and operation.

Success is dependent on when the Digital Twin can be first created. For value to be created across the lifecycle, a physical product configuration must be captured digitally once the manufacture of the product is finished. All content generated before this, in Engineering, or information recorded during the manufacturing of the product is valuable, but it should be considered part of its past and should be connected to the digital configuration of the physical product. Example this content often mis-represented as Digital Twins are models such as 3D CAD or simulation. This information is not a Digital Twin―it’s just a picture at a given point of time, with limited information. It has value for sure, just not as a Digital Twin configuration.

If you capture an individual product’s configuration correctly, you have context―which enables you to create traceability. In essence you have created the Digital Twin time machine. The context of the Digital Twin, supported by digital thread data, and analysis tools like simulation or analytics, allows you to move backwards and forwards for any product or system of products.

Let’s get started building your Digital Twin time machines and understanding the key parts required for it to be effective, so that you can start realizing the immense value it can provide.

Building your Digital Twin time machine

Before traveling to the past, or moving into the future, the time machine requires structure. This means we need to know what the digital twin configuration is so that we have context in order to travel. So how do we build it?

Anyone interested in creating Digital Twins for products they maintain, doesn’t need to rely on their supplier of these products to provide their Digital Twins. In a perfect world, the supplier would be providing this Digital Twin configuration as part of the purchase―that was captured when manufacturing was complete and would have already started linking Digital Thread data related to the parts used―whether mechanical, electrical, electronics, or software. 

The reality is that many of your products, which last many years, did not come with Digital Twin configurations. This means you will need technologies and processes that enable you to record configurations, based on their maintainable components, down to their serial number. Doing this creates that foundational traceability―when a serialized component is replaced through maintenance activities. That change can be recorded with the new serialized component information, to keep the Digital Twin configuration up to date, as well as show the old component as part of its Digital Thread traceability.

Traveling into the past

For the Digital Twin to travel through time, context is key. But, to create next level of business value, your ticket to ride into the past and future―content will be king. For this you need to tightly couple the Digital Twin configuration with the related critical information that has occurred across the lifecycle―enabling traceability across the product lifecycle.

This requires a sustainable Digital Thread, which provides the ability to track a product and its digital assets all the way from concept through design, manufacturing, quality, and service. This connected information will help your organization gain crucial insights that can inform decisions throughout every aspect of the product lifecycle—improving communication and collaboration and resulting in the creation of better products, with a shorter time to market.

It is this traceability of what has happened, that gives your Digital Twin time machine the ability to travel into the past. For example, if technicians cannot locate or access information related to parts, job plans, or service bulletins―a.k.a. searching the past―productivity suffers, impacting equipment downtime and workforce productivity.

Predicting the future

With context and traceability in place, it’s time to predict the future. There is an emerging opportunity to do this with the many predictive analytics tools out there today, but those become more relevant when you use them to predict behaviors based on individual contact of product or system of products, which you now know how to build.

As simulation moves out to the field, it becomes operational simulation, which is about trying to understand what a product’s been through in its lifetime that might affect its operation once we apply an update. Simulation can have a much bigger presence in the physical world, if the context is there. Based on an individual configuration of an individual asset and use simulations, you then can predict what will happen if we replace a part, or update software.

A good example is simulating the impact of a new over the air (OTA) software update. If you don’t know each individual configuration of the product you are analyzing, you are making a guess as to its impact on the operation of the product, and that is not good for customer retention.

The other opportunity for moving out to the future, is simulating what will happen to a product operating, now. For example, let’s say that we start to see failures in the product once we reach a certain temperature, we can then use that data to predict what will happen if the temperature goes up even further than what it was when we started to see those failures. It's simulating not only the current version of the Digital Twin, but also simulating the future based on the real-time feedback that we're getting from the product.

Look for technology that adds horsepower

If you think you will be able to rip and replace legacy software systems to accelerate the building of Digital Twins, you are mistaken. There can be hundreds of systems and related data globally supporting the various aspects of the product lifecycle, encompassing different disciplines. The answer is a platform approach.

Platforms can enable organizations to move quickly and build flexible individual Digital Twin configurations for any use case, for any industry, and any customer requirement. They can adapt and change as your business, or the environment they operate in, changes, or the profile of the product itself changes―for example upgrades.

These Digital Twin variations depend on the perspective of the end user and use case you want to support. Some examples include a targeted scenario, like a predictive maintenance or performance optimization, where you are in the supply chain, such as an OEM, or an Owner/Operator, or a Business Model―like power by the hour or product as a service.

If you don’t use an overlay approach, the other issue you will encounter is building Digital Twin configurations with their static metadata models. Organizations are quickly finding that when they journey down the build of an individual Digital Twin, and are working with a static data model, they have trouble changing it to meet new needs they didn’t know they had when they started. The data model needs to be flexible to keep the Digital Twin up to speed as new requirements emerge for tracking changes and enabling traceability for new connections.

Existing PLM systems can trap valuable information, or worse, users have stopped capturing some of the data and decisions within them and have resorted to capturing information on their desktops. Some important information can be trapped in existing systems because they are closed. This makes it difficult to create meaningful relationship connections between all of a product’s digital assets and their revisions across the lifecycle― –bill of material(s), parts, software, electronics, CAD models, documents, requirements, process plans, service manuals, maintenance history, for example.

Let’s get started!

If you are ready to travel through time by implementing Digital Twins, there are a couple of ways to get started, depending on who you are and where you reside within the product lifecycle.

If you are a manufacturer, start capturing the “as-built” configuration of every individual product that goes out the door and make sure it is as accurate and as detailed as you possibly can. This means capturing all serialized electrical, mechanical, and software components and linking them back, using the Digital Thread, to engineering parts and their related history such as CAD models, simulations, requirements, change orders, and on and on―based on the value they can provide.

If you are in operations and maintenance, you can start by keeping accurate records of your inspections, track what a product’s configuration looks like when it arrives, or as it is in its operative state right now. This should not be paper based, nor a spreadsheet, as the manual maintenance and upkeep of the Digital Twin configurations would then become a daunting task. It should be in a database that is searchable, inspectable, and maintained.

Whatever the business case, the Digital Twin configuration should then be able to extend out to installation, commissioning, and operation at a customer site, allowing them to update the configuration as things change. Then from there, hopefully you're able to start capturing additional data from the field when your product is maintained. This is the Digital Twin time machine!

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