More and more companies are discovering the value of this technology. They are using digital twins to cut costs, prevent downtime and optimize processes. But what exactly is a digital twin? And how do you ensure that such a project actually delivers what you expect?
What is a digital twin?
A digital twin is a digital representation of a physical system. That sounds simple, but what exactly is a physical system? It can range from a single bearing in one machine to a complete supply chain. The commonality: there is always a coupling between the physical and digital worlds.
That link may be sensors that measure vibration, temperature or pressure. But it can also be data on material flows, production numbers or suppliers. Most importantly, the data must contain enough information to understand what is happening in the real system.
Take an assembly line where transport robots (AVGs) bring parts to different workstations. A digital twin of such a system collects data about where each AVG is, how much battery it has left and what route it is taking. But also about the parts being transported and the inventory in the warehouse.
With all that information, you can see in real time how the process is going. Are there delays? Are there bottlenecks developing? But you can also simulate what happens when you add more AVGs or choose a different route. That way you test changes digitally before implementing them in practice.
Different twins for different purposes
The big misconception about digital twins is that there would be one universal solution. In reality, there are as many variants as there are applications.
- Asset twins focus on individual machines or components. For example, a digital twin of an engine collects data on vibration, temperature and energy consumption. Machine learning algorithms analyze that data to predict wear and tear. That way you know when maintenance is needed before something actually breaks down, and you avoid replacing parts too early.
- System twins look at entire manufacturing processes. Here it is more about material flows, capacity and scheduling than the physical properties of individual machines. Algorithms optimize routes, predict when new inventory is needed and identify bottlenecks.
- Process wins focus on the supply chain. This involves logistics, delivery times and inventory management. Data comes from ERP systems, carriers and suppliers.
The difference is in the details that are and are not important. If you want to model a bearing, it requires knowledge of materials science and physics. Modeling a supply chain is about statistics and logistics. Too much detail in the wrong model creates clutter with no added value. Compare it to the stylized rail maps hanging at stations: perfect for travelers who want to quickly see how to get from A to B, but useless for ProRail that needs to know exactly where a switch box is located.
What does a digital twin look like?
A frequently asked question is whether a digital twin should always be modeled in 3D. The answer depends on your objective. For designing new products or training operators, a 3D environment can provide valuable insights. You can then virtually assemble, test different designs or allow personnel to practice without risk.
But for many applications, 3D is unnecessary. If you want to optimize a supply chain or predict energy consumption, you don't need a three-dimensional view. Indeed, it can distract from actual data analysis.
Algorithms and AI
Collecting data is one thing, extracting value from it is another. That's where algorithms and AI come in. Machine learning plays an important role in many digital twin applications.
- Pattern recognition helps identify anomalies. An algorithm learns how a healthy machine sounds, moves and maintains the right temperature. If those patterns change, the system can issue a warning before problems arise.
- Predictive models use historical data to predict future events. When will a pump need maintenance? How much energy will a building use tomorrow? Based on past patterns and current conditions, algorithms can make reasonably accurate predictions.
- Optimization algorithms look for the best solution within certain constraints. How do you control AVGs so that congestion does not occur? How do you schedule maintenance without stopping production? These algorithms test thousands of scenarios to find the optimal solution.
The power of AI is primarily in processing large amounts of data and discovering patterns that humans would miss. But those algorithms are only as good as the data they are trained on.
Deploying digital twins successfully
A digital twin is not a panacea. Success depends on a number of important conditions.
First, data must be complete and current. If information arrives too late or in the wrong order, you may be basing your decisions on outdated data. That can be dangerous when you're automatically intervening in processes.
Observability is also important. That's the degree to which you can say something about what's happening inside a system based on the data you measure on the outside. Too few sensors means too little insight. Too many sensors means a lot of noise and high costs.
A digital twin must also prove that it accurately represents reality before you can use it to simulate nonexistent scenarios. That requires time and real-world data to validate the model.
Data ownership also deserves attention. Some vendors offer turnkey digital twin solutions. That's convenient because as an organization you don't have to reinvent the wheel. A disadvantage can be that those vendors keep your data trapped in their systems. It is important to ensure that you retain ownership of your data, otherwise it will be difficult to use it later for other applications.
Finally, the organization must be ready. Implementing a digital twin often means changes in processes and practices. Employees must learn to work with new systems and gain confidence in the technology.
The real value of digital twins lies in gradual improvements. Less downtime through predictive maintenance. Lower energy costs through smart control. Better planning through insight into bottlenecks.
For many companies, that's more than enough to recoup the investment. The trick is to set realistic goals and work toward them step by step. Digital twins are not hype, but neither are they a panacea for every issue. They are a powerful tool for companies looking to understand and improve their processes.
This blog post is a contribution from TMC, their data science experts help organizations improve their decision-making and predictive capabilities, for Data Expo readers. Find more inspiration at www.themembercompany.com or visit TMC during Data Expo at booth #5 & #40.
