Digital twins are one of the innovative manufacturing solutions you’ve no doubt heard about in recent months. They’re not brand-new technology, but as manufacturers contend with the ways data has become an increasingly important byproduct of their operations, many are looking to digital twins with new interest.
So what is all the hype about? What exactly can manufacturers do with digital twin systems? Plenty: used right, digital twins can ease the labor shortage, improve operational efficiency, help manufacturers outpace their competition, render complex decisions clearer and easier to make, and more.
In this article, the first in a two-part series, we’ll dig into some specific use cases digital twins have for manufacturers. First, though, let’s define what we mean by “digital twin.”
What is a digital twin?
Let’s start with Wikipedia’s definition: “A digital twin is a digital model of an intended or actual real-world physical product, system, or process that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, integration, testing, monitoring, and maintenance.”
If you’ve ever called an Uber and then watched your phone as the image of your ride inched along a map of your location, you’ve interacted with a digital twin. The digital simulation communicates clearly where your ride is, how soon it will arrive, and whether it missed the one-way sign on the next street over.
If you grew up pre-smartphone, this application may seem unnecessary—why not just wait for your ride?
But given the real-world ways people use their phones, this digital twin can improve safety: it also eliminates a rider’s need to text their driver to ask for updates, which reduces the chances that a driver is looking away from the road.
The key components here are the data sources (the car’s dynamic location and the static map data), the equipment for sending and receiving that data (driver and rider cell phones, cell tower infrastructure), and the interface that translates all that data to useful knowledge (the car-on-map visual).
Now let’s consider what digital twins might look like in a manufacturing context.
Digital twin manufacturing application 1: Get granular with predictive maintenance
Your car’s “check engine” light should appear on your dashboard well before the engine fails to start. Imagine you had predictive maintenance software for every piece of equipment in your factory, with “check x” indicators for the parts most likely to shut down the production line.
This digital twin could ensure that your team performs maintenance exactly where it’s needed, when it’s needed, to minimize downtime and its related expenses.
To do this, the digital twin would need the following data:
Real-time data from sensors on equipment
Historical data about leading indicators of likely breakdown
Organizational data about the cost of downtime of various equipment
With this, the digital twin might set a prioritized task list to ensure maintenance work with the biggest potential impact gets prioritized. In addition to minimizing downtime, this digital twin could also prevent wasted work.
Digital twin manufacturing application 2: Make complex decisions faster
Let’s say you’re trying to decide whether to replace a piece of machinery with a new, more efficient model. This is a complex decision with lots of variables: support infrastructure needed for the new equipment; training costs for each machine; integration costs with the rest of your factory; the likelihood of downtime.
A digital twin could help you analyze costs and benefits over various timeframes, given various conditions. The digital twin can’t actually make the decision for you, but it can render the decision much more concrete and offer scenarios for which each option makes sense.
Digital twin manufacturing application 3: Understand complex systems better
Supply chains are among the most complex systems manufacturers deal with. Digital twins can help streamline decision making after inevitable disruptions.
Rather than manually crunching numbers and considering alternatives, digital twins can automate the processing of various inputs to identify next-best paths after a disruption—or even prioritize alternative courses of action given various scenarios. Which brings us to the next use case…
Digital twin manufacturing application 4: Simulate what-if scenarios
Cyber attacks. Supply chain disruptions. The emergence of a major unexpected competitor. Manufacturers have to be prepared for the unexpected, which means having plans in place for a variety of what-if scenarios.
Digital twins make that planning more concrete and can therefore give manufacturers greater confidence that their contingency plans will actually work. The key here is knowing which sources of data to feed a digital twin and what to model with the twin: supply chain or factory floor? Weather events or labor strike? Combining your industry and business expertise with the expertise of a team skilled in developing and building digital twins can lead to stunning outcomes.
Digital twin manufacturing application 5: Optimize energy use
What are your current energy needs? How might you reduce them? How might you transition to cleaner sources of energy? What if you could easily model multiple scenarios to get you to 2030 targets? It’s possible with digital twins.
Again, the key is to input the right data and build a digital twin of the correct systems. When you do, you can consider multiple concrete paths to achieving energy use goals, then focus your resources on enacting the most viable.
The full-organization ripple effect of digital twins
The challenges facing manufacturers today are daunting in part because they’re so complex. This is the beauty of digital twins: they make it possible to replicate complex systems and test alternative setups in real time.
If your organization is looking for innovative manufacturing solutions that address the interconnected systems—human, mechanical, digital, and otherwise—that power you day to day, you’ll be delighted by the potential of digital twins.
For insight on what manufacturers need to build digital twins, set up an intro call or check out part two in this series.
