You manage a factory. Maybe you want to use machine learning to improve your efficiency, but you’re worried about taking a model’s predictions as gospel truth without some sort of real-world validation. After all, tweaking the flow of materials and goods through your factory to test the model’s ideas could end up costing you hundreds of thousands of dollars if the model is wrong.
Or maybe you want to develop a system that will accurately detect upstream problems early on so that you can make adjustments as early as possible. You need some way of understanding the whole system and how changing inputs and outputs affect it so that you can take the right action at the right time.
You need a way to assess the impact, positive or negative, that implementing any changes will have on your bottom line before you make them. Currently, the risks associated with a bad model present a real problem for model implementation—because of resistance, many models that could be having an impact on business aren’t being put into production, contributing to the model impact epidemic. So how do you solve this problem and ensure that you’re having a positive impact, quickly and effectively?
Enter the digital twin.
Understanding the twin
The concept of the digital twin is simple to understand, but that simplicity belies the potential impact that it can have. You can think of a digital twin as a virtual representation of the processes and flows of a complex system. Unlike a static virtualization, however, the digital twin is constantly getting live updates from the system that it is modeling. By uniting this live data with the historical information, you can build a digital twin that’s a dead ringer for the live systems—thereby avoiding any unfortunate differences like those that give rise to Danny DeVito and Arnold Schwarzenegger in the classic 80s movie Twins.
Benefits of using a digital twin
But how does having an accurate digital representation help you? It means that instead of making changes to your actual factory workflow—whether it’s as simple as minor adjustments to a mixture or setting up a new assembly line—and seeing what happens, you can implement the changes first in the digital twin and see the effects. The combination of historical and live data allows the twin to give you the most accurate prediction possible about the outcome of implementing those changes.
This flexibility, as well as the direct matching to the real-world operations, opens a wide variety of possible benefits and uses for your digital twin.
- Experiment with possibilities — By manipulating the digital twin, you can test hundreds or even thousands of possible workflow changes quickly and easily, without any risk of a negative impact on your current processes.
- More effectively manage processes — Because the digital twin incorporates live data as well as historical data, it can quickly identify possible problems in production as soon as they arise, letting you react before the problems impact downstream processes.
- Reduce costs — With the ability to test changes on the fly, as well as detecting deviations in currently running processes, you can substantially reduce your costs by only implementing the changes that are the most likely to have a positive impact.
The intersection of digital twins and machine learning
In addition to helping you adjust your processes for efficiency and impact, and thus saving you money, your digital twin can also help you create data to train additional models. As our own Martin Schmitz discussed in the recent whitepaper A Human’s Guide to Machine Learning Projects, sometimes it’s not possible to create a machine learning model because there simply isn’t enough data to train an accurate model, nor to test it. With a digital twin, you can generate additional data—perhaps paired with active learning—to generate data that you can then use to supplement the training of other models.
How digital twins & machine learning work together
As we touched on above, digital twins can be valuable source of data – especially when you’re following Martin’s advice and ‘going supervised’. Having labeled data from a digital twin makes it much easier to utilize supervised models for your machine learning use cases. That’s not the only way that digital twins can deliver an impact on your machine learning projects:
- You can test models in a virtual environment, utilizing a form of A/B testing. This is especially handy in highly regulated environments when you can’t put a model directly into production.
- You can tackle more use cases because you have easier access to labeled data – this affords more opportunities to optimize processes, cut costs and reduce risk.
- You can cut down on the cost of data gathering. Sometimes gathering the data is costly in and of itself because it may require lab tests and human intervention in a real-world environment. This can really add up, especially if you need lots of data for your model.
Want to learn more about how data science and artificial intelligence can impact your manufacturing processes? Sign up for our virtual optimizer webinar and explore how RapidMiner can help you better view and implement changes to your processes, all with low risk and a high possibility of impact.
If you’d like to talk about how AI can help your manufacturing business, you can also register for a free AI assessment where we’ll help you analyze the feasibility and impact that AI can have on your bottom line.
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