Until recently, the digital twin was mainly associated with industry and advanced engineering. Now, the term is increasingly coming up in conversations about technology strategy, digital product development and data management. The problem is that digital twin is still sometimes treated as a trendy buzzword, and it’s a useful decision-making tool.
In this article you will find answers to the questions: what is a digital twin, how does it work, what are its limitations and examples that show the business value it can bring.
A digital twin is a dynamic, digital representation of a physical object, system or process that is continuously updated based on real data.
It is not:
The digital twin “lives” together with its physical counterpart: it analyzes data, predicts scenarios and supports decision-making. In simplest terms, a digital twin is a digital test environment for reality.
Definitions of digital twin often focus solely on technology. In reality, however, its value comes not from the model itself, but from how the organization makes decisions based on data.
A digital twin is a continuously updated digital model that replicates real processes or systems and allows you to test decisions before implementing them on a physical or production level.
The most important elements here are three:
The combination of technology and strategy makes digital twin not just a visualization, but a tool to support enterprise decision-making and reduce waste.
Digital twin is still defined solely as technology, and it is a model for thinking about data and decisions.
Most common mistakes:
The digital twin works best where the goal is:
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The differences between these concepts is an often sought and underestimated issue. So let’s look at these three concepts and summarize them.
The difference is not in the graphics or technology, but in the constant connection to reality.
Behind the digital twin is a combination of several areas:
Action scheme:
This is a change in approach from reporting the past to simulating the future.
Examples will best demonstrate the value of this concept.
Digital machine models can predict failures and optimize performance without stopping production.
Digital twin helps simulate the flow of goods and respond to disruptions before they affect operations.
Cities are using digital twins for traffic, energy and investment planning.
A digital twin can map user behavior or system architecture, helping product teams make decisions faster.
In technology projects, digital twin allows to combine business strategy with real data.
Teams can:
This is especially important where technology, UX and business must work as one ecosystem.
The most significant change, however, is the shift from reactive to predictive management.
Most common barriers:
The digital twin works best as the next stage of digital maturity. It is worth preceding it with incremental implementations of systems that can freely transfer data between them.
In short, a digital twin is a digitized layer of reality that allows companies to test future results instead of guessing them.
From industry to logistics to digital product development, the digital twin is becoming a strategic tool for organizations that want to make faster, smarter, data-driven decisions.
A digital twin is a digital model of a real object, process or system that is continuously updated based on data. Unlike ordinary models, it allows not only to observe the current state, but also to test scenarios and predict future events.
In business, digital twin is a decision support tool. Companies use it to simulate changes, analyze risks and optimize processes without having to test everything in a real operating environment.
No. The Internet of Things (IoT) is responsible for collecting data from devices and systems, while the digital twin uses this data to create a dynamic model to analyze and simulate scenarios.
Most commonly, the digital twin is used in manufacturing, logistics, smart cities and digital product development. Companies use it to predict machine failures, optimize the supply chain or analyze user behavior in applications, among other things.
Not always. A digital twin makes the most sense in organizations with lots of data and complex processes. For many companies, a better first step is to clean up the technology architecture and implement robust data analytics.
It’s best to start by identifying a business objective, such as cost reduction, process optimization or improving user experience. Next, it’s a good idea to identify available data sources, integrate systems and only then build a digital model.
