What is a digital twin?

At a glance

• The concept applies to physical goods just as it does to supply chains, business processes, or entire organizational units.
• Sensors, ERP systems, or other data sources continuously supply the model with current measurements and KPIs.
• Changes can be tested in the digital twin before they affect the real world, for example for planning and risk assessment.

Digital twin definition

The term "digital twin" describes a virtual counterpart of a real object, process, or system that is continuously linked to current data from physical reality. Unlike a static model or a one-time report, the digital twin evolves along with its real counterpart: it continuously receives data, processes it, and returns insights – as a basis for monitoring, analysis, and decision-making.

In a corporate context, the concept has moved far beyond its industrial origins. Today, digital twins describe not only machines or production facilities, but also supply chains, business processes, and entire organizations. For controlling, this means: a data-driven model of the company that brings together operational reality and commercial management figures – and thereby creates a basis for decision-making that goes beyond the classic look back at historical data.

The value of a digital twin lies not in the technology itself, but in what it makes possible: understanding states before they become a problem. Playing through scenarios before decisions are made. Seeing chains of effects, not just key figures.

How does a digital twin work?

At its core, a digital twin consists of three elements that are in permanent interplay:

• 1. The physical object or system: The starting point is always a real counterpart: a machine, a vehicle, a building, a production process or – in a commercial context – a business system such as a supply chain or a sales channel.
• 2. The digital model: The virtual representation describes the structure, behavior, and state of the real counterpart in machine-readable form. It can contain geometric data, map physical laws, or model business key figures and sets of rules. What is decisive is that the model is not static: it learns, it updates itself, and it evolves along with its physical counterpart.
• 3. The data coupling: Sensors, APIs, ERP and MES systems, IoT platforms, or manual inputs ensure that the digital twin is continuously or at defined intervals supplied with current data. This bidirectional connection is the actual distinguishing feature compared to a simple simulation model or a static data model: the digital twin responds to changes in the real world – and can conversely also send control commands back to the physical system.

The insight cycle: From the interplay of these three elements, a continuous insight cycle emerges: data from the physical world flows into the digital model, the model analyzes, simulates, and forecasts, and the insights gained flow back into the real world as a basis for decisions. This cycle closes – and becomes more precise with each iteration.

What types of digital twins are there?

A distinction is made between various types, which depend on the mapped object and the degree of abstraction:

In controlling and corporate management, the process and enterprise twins are especially relevant: they do not primarily reproduce physical properties, but business structures, KPIs, dependencies, and chains of cause and effect.

Is a digital twin AI? – Distinction from related terms

What can digital twins do in controlling?

For controlling, the digital twin is a conceptual framework that changes the discipline's self-image. Traditionally, controlling works with historical data: actual figures are aggregated, compared with the plan, and variances are explained. This cycle is necessary, but inherently backward-looking.

A digital corporate twin shifts the focus: it integrates real-time operational data with commercial models and thereby enables continuous, forward-looking management. In concrete terms, this means:

• Integrated planning: Instead of isolated planning rounds in financial, sales, and production controlling, a networked model emerges that makes interactions between areas visible.
• Simulation-based decision-making: What happens if a major customer drops out? How does a rise in raw material prices affect the margin? Such questions can be played through in the model before they turn into a crisis.
• Continuous forecasting: The rolling forecast, already a central instrument of modern corporate management, gains quality and timeliness through a digital twin: the model updates itself instead of waiting for manual data entry.
• Transparency about chains of effects: Controllers see not only key figures, but the connections behind them – which operational drivers produce which financial results.

The digital twin in controlling is therefore not science fiction, but the consistent further development of what modern BI systems, integrated planning tools, and data-driven management models already achieve today.

What are the advantages and challenges of digital twins?

Advantages

• Better bases for decisions: Decisions are based on a more complete, more up-to-date picture of reality, rather than on monthly reports or one-off analyses.
• Early warning and resilience: Variances and risks become visible before they manifest in poor figures.
• Efficiency gains: Simulations replace costly real-world trials; planning processes become shorter and more accurate.
• Cross-organizational transparency: A shared model bridges silos between controlling, production, sales, and IT.

Challenges

• Data quality as a foundation: A digital twin is only as good as the data that feeds it. An inconsistent, incomplete, or faulty data basis leads to a precise representation of a false reality.
• Model complexity: The more detailed the model, the more difficult it becomes to keep it up to date and to explain it.
• Organizational prerequisites: Introducing a digital twin is often a transformation project. It requires clear data ownership, procedural discipline, and the will to actually make decisions on a data basis.
• Data protection and governance: Particularly with corporate and HR twins, sensitive questions arise around data protection, data sovereignty, and the handling of personal information.

What are examples of digital twins in a company?

Some examples of typical fields of application in companies are:

Summary

The digital twin is far more than a technical concept: it is a new paradigm of corporate management – from retrospective reporting to continuous, forward-looking management based on a living company model. In controlling, it opens up the possibility of understanding chains of cause and effect, running through scenarios, and making decisions on a more complete picture of reality. Bissantz makes this approach practicable – as a consistent, navigable model of your own company logic.