Digital replicas of real things help us make all kinds of decisions in our daily lives.
Online street maps help us get around and know how traffic will affect our drive time. Virtual tours allow us to explore a new house without stepping inside it. And virtual seating helps us see how close we’ll be to the action at games and concerts, and even what view we’ll have.
A new kind of digital replica known as a digital twin has recently taken hold in the business world. It’s a virtual, model-based recreation of a physical thing — like a product, machine or even an entire production facility. And it can help you make better design, production and maintenance decisions in some profound ways.
If you’re a machine builder, you can design and prototype machines, and test their performance using real-world physics, long before you cut steel. You can also virtually commission machines to avoid last-minute surprises when you’re on-site with an end user.
If you’re a manufacturer or other industrial firm, a digital twin can help you design and validate a new line or production site before you buy equipment. You can also better prepare operators by using a digital twin for virtual training. And you can use a digital twin to help inform and guide maintenance activities to reduce downtime.
A Living, Learning Model
Simulation is already used in manufacturing to examine important factors like the key performance indicators (KPIs) that you want to improve. For example, you might simulate the operation of a car to predict its fuel efficiency.
A digital twin takes that idea and builds on it, because it’s a living digital replica that learns and changes over time. So, with a digital twin, not only could you predict the car’s fuel efficiency when a buyer takes the keys, but you also could predict how and when that efficiency will degrade.
You could even predict when the buyer should take the car in for maintenance. A digital twin could not only use measurables like mileage driven and time elapsed, along with a model of the car’s wear based on other factors such as temperature and driver behavior, to predict maintenance needs.