Digital twins: from buzzword to business case

More and more companies are discovering their value. They use digital twins to reduce downtime, improve efficiency, and make better decisions based on real data. But what exactly is a digital twin? And how do you turn this technology from a concept into measurable business results?

What is a digital twin?

A digital twin is a digital representation of a physical system, anything from a single bearing inside a machine to an entire supply chain. What they all share is a live connection between the physical and digital world.

That connection can be built from sensor data such as vibration, temperature, or pressure. Or from information about material flows, production volumes, or logistics. The goal is simple: gather enough data to truly understand what’s happening in the physical system.

Take, for example, an assembly line where autonomous guided vehicles (AGVs) transport components between workstations. A digital twin of this process tracks where each AGV is, its battery level, and route efficiency, as well as real-time inventory levels. The result: complete visibility into operations and the ability to test changes virtually, before implementing them in the real world.

Different twins for different goals

There’s no such thing as a one-size-fits-all digital twin. Each twin serves a specific purpose.

• Asset twins focus on individual machines or components. For instance, a digital twin of a motor can monitor vibration and temperature data to predict wear and schedule maintenance before failures occur.
• System twins look at entire production processes. This is more about material flows, capacity, and planning than the physical properties of individual machines. Algorithms optimize routes, predict when new stock is needed, and identify bottlenecks.
• Process twins focus on the supply chain. This involves logistics, delivery times, and inventory management. Data comes from ERP systems, carriers, and suppliers.

The key is finding the right level of detail. Too much, and your model becomes complex without adding value; too little, and you miss critical insights.

What does a digital twin look like?

A frequently asked question is whether a digital twin always has to be modelled in 3D. The answer depends on your objective. A 3D environment can offer valuable insights for designing new products or training operators. It allows you to assemble virtually, test different designs, or let staff practice without any risks.

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 representation. In fact, it can distract from the actual data analysis.

Algorithms and AI

Collecting data is one thing; generating value from it is another. That’s where algorithms and AI come in. Machine learning supports digital twin applications through:

• Pattern recognition helps to identify deviations in performance before they cause downtime.
• Predictive models use historical data to predict future events. When does a pump need maintenance? How much energy will a building consume tomorrow? Based on past patterns and current conditions, algorithms can make reasonably accurate predictions.
• Optimization algorithms search for the best solution within certain constraints. How do you control AVGs so that traffic jams don't occur? How do you plan maintenance without shutting down production? These algorithms test thousands of scenarios to find the optimal solution.

The power of AI lies primarily in processing large amounts of data and discovering patterns that humans would miss. But these algorithms are only as good as the data they are trained on.

 

Successfully implementing digital twins

A digital twin isn’t a magic solution. Success depends on several key conditions.

First and foremost, your data must be complete and up to date. If information arrives too late or in the wrong order, you may end up making decisions based on outdated insights. That can be risky, especially when processes are adjusted automatically.

Observability is another critical factor. It refers to how well you can understand what’s happening inside a system based on the data you collect from the outside. Too few sensors mean limited insight; too many create noise and unnecessary costs.

Before a digital twin can be used to simulate new scenarios, it must prove that it accurately reflects reality. That takes time and real-world data to validate the model.

Data ownership also deserves attention. Some providers offer ready-made digital twin solutions, which can be convenient, you don’t have to reinvent the wheel. But the downside is that your data may end up locked inside their systems. It’s essential to remain the owner of your data so you can use it freely for other applications in the future.

Finally, the organization itself needs to be ready. Implementing a digital twin often requires changes in processes and workflows. Employees must learn to work with new systems and build trust in the technology.

The real value of digital twins lies in gradual improvement: reducing downtime through predictive maintenance, lowering energy costs with smarter control, and improving planning by identifying bottlenecks.

For many companies, that’s already enough to justify the investment. The key is to set realistic goals and work toward them step by step. Digital twins aren’t a hype, but they’re not a cure-all either. They’re a powerful tool for companies that want to truly understand and improve how their systems perform.