Manufacturers: what makes for a good digital twin candidate?

In the first part of this series, we explored various digital twin applications for the manufacturing sector: better predictive maintenance, energy use optimization, and speeding up complex decision-making.

Here, we’ll explain how to know whether something is a good fit for building a digital twin and then look at the data strategy roadmap required to get from where you are today to a digital twin-enabled operating model.

What makes something a good fit for a digital twin?

While there's no simple diagnostic for whether something will make a good digital twin, there are some guiding principles that can point you in the right direction. Think of this as the CPM test.

First, the system should be a specific, contained segment of your manufacturing process or a specific set of assets. This is largely because building and learning from a digital twin is an iterative process, so it’s best to start small.

Second, the system or asset group you choose should have a manageable scope but also be pertinent to your organization as a whole. If, for example, you’re a global organization, the system you choose to replicate via digital twin should have global components (e.g., your shipping processes).

Finally, the system or assets must have parameters you can measure and impact. If, for example, you wanted to build a digital twin of your energy use with the goal of optimizing usage, you’d need a system for gathering data on current use, as well as the flexibility to make changes.

How do digital twins work?

Jason Hehman explains more here.

Manufacturing digital twin example: energy use monitoring system

To better illustrate what that looks like in practice, let’s use the example of a digital twin built to optimize energy usage. We’ll envision a hypothetical facility that provides blade-sharpening services to butchers around the globe.

This system checks all the CPM boxes:

Energy use is contained; we can contain it even further to electricity use within the company’s headquarters, and further still by restricting it to electricity use for blade-sharpening activities (versus, say, administrative operations).
Energy use is pertinent to every component of an organization’s operations, so insights gathered from this project can inform future action.
Energy (and electricity) use is highly measurable.

Once we’ve defined the system the digital twin will replicate, we have to make sure we’ve got the necessary data to build the digital twin. Let’s assume this hypothetical organization has installed internet-connected sensors on its sharpening machines and the electric forklifts it uses to lift blades to those machines.

So here we have a use case that can provide the raw material (aka data) essential to building a digital twin. Now let’s go one level deeper about that raw data: let’s talk data strategy.

Is your company's data ready for a digital twin?

Learn more about what's needed.

Data and data strategy: a roadmap toward digital twins

You’ve probably heard the adage “garbage in, garbage out.” It’s highly relevant when building digital twins: the data that fuels the digital twin must be accurate and timely to output results that you can use. You’ll know your data is useful—and usable to build a digital twin—when it accurately mirrors the pertinent information within the defined scope of your digital twin.

In this case, that means the data needs to accurately reflect the electricity use of the equipment used in blade sharpening; we’ll probably also need data on electricity costs in various configurations.

But data strategy goes beyond ensuring the accuracy of data: a full data strategy involves planning for the long-term management of your organization’s data. This includes the people, technology, processes, and rules around data and data governance.

In the context of a digital twin tracking electricity use in a blade sharpening facility, having a data strategy roadmap means having a plan for…

How and from where you gather data.
How and where you store data.
How you monitor and clean data.
How often and from where you sample data to verify its accuracy.
How you protect data.
Who has access to data, at what levels (view, modify, etc.).
How you use data.

For many manufacturers, creating the data strategy roadmap is the most important phase of building a digital twin because it’s the underlying data strategy that sets you up to embrace all kinds of Industry 4.0 technology applications (including machine learning and AI). It’s also significant because, as manufacturers digitized many parts of their operations in the last decade or so, many now have access to more data than at any point in history.

This data is a valuable asset, but its value can only be realized when it’s organized with a comprehensive data strategy.

If your organization has not yet invested in organizing its data assets, you might benefit from our data maturity accelerator, a one-week engagement that delivers a custom roadmap for improving your data maturity.

What's involved in building a digital product?

It starts with a roadmap.

Building and piloting your digital twin

Once your organization’s data is mature enough to fuel a digital twin, it’s time to actually build one and experiment with it.

For now, I’ll gloss over the building part: the specifics of that process and of the outcome will vary greatly from one application to the next and from one organization to the next. Instead, let’s jump ahead to the potential outcomes.

With a digital representation of energy use, it’s much easier to experiment with alternative setups to assess their impact. For example, you might…

Run a simulation where sharpening machines can sense when an operator is not present and automatically shut off. Would this cut down energy use? Or would it have no impact, given the energy required to get a machine up to speed?
Reconfigure the layout of the factory floor to minimize the distance electric forklifts have to drive. Is there a setup that could reduce electricity usage? Might that setup also reduce wear and tear on the equipment?
Reconfigure the facility layout so machines run idle less often or so that machine operators can switch blades more efficiently.

The beauty of the digital twin becomes apparent during this experimentation phase: you can evaluate dozens – or thousands – of what-if scenarios, accurately assess the impact each would have, and choose the one that yields the greatest ROI.

Consider the factory layout reconfiguration, for example: testing even one alternative in real life would require a monumental amount of effort, and you wouldn’t know until after the fact whether it resulted in any energy savings. With a digital twin, you can test endless layout variations without disturbing a single machine.

As you learn from your digital twin and expand your use of this technology, you can substantially improve the efficiency of your operations.

Digital twins have dozens of manufacturing applications

We walked through a few examples of digital twin applications for manufacturing here, but it’s important to keep in mind that digital twins are extremely versatile and can likely help you solve unexpected challenges.

In some cases, for example, the digital twin can be set up to impact real-world events. One example: if the digital twin we built to track energy use detected a machine running idle for a certain amount of time, it could send a signal to automatically shut down that machine, thus keeping energy use within desired parameters.

The bottom line is that we’ve barely scratched the surface of what digital twins can do. If you’re interested in exploring how this technology might transform your facility—or if you’re curious about how to prepare your data to implement a digital twin—get in touch. We’d love to help you explore the possibilities.

Published by Jason Hehman in Industry 4.0

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