Why has digitalisation become essential for industry?
In an industrial world marked by uncertainty, growing operational complexity, and the demand for agility, digitalisation is establishing itself as a lever for competitiveness. Anticipating demand fluctuations, reducing downtime, optimising supply chains, and minimising production costs are all challenges that digital tools make it possible to address.
For example, in aeronautical design, an aircraft’s flight performance is simulated to identify potential issues and optimise its design before manufacturing. Similarly, in supply chain management, modelling the entire logistics network allows companies to anticipate demand fluctuations and adjust their operations accordingly.
Digital transformation is not limited to the dematerialisation of processes. It relies above all on the ability to understand, simulate, and adjust system behavior in real time. In this context, two major tools come into play: the digital model, often static, and the digital twin, dynamic and interactive.
What is the difference between a digital model and a digital twin?
The digital model is a virtual representation of a product or process. It allows the structure to be visualised, certain properties to be analysed, or one-off simulations to be performed. However, it is not connected to the real object.
The digital twin, on the other hand, is a model enriched with real-time data, sourced from sensors, APIs, or automation systems. It permanently reflects the physical system’s actual state and enables predictive analyses.
But is it always necessary to go as far as a digital twin? Not necessarily. The choice depends on the objectives, available resources, and the desired level of interaction.
How to choose between a digital model and a digital twin?
| Criterion | Digital Model | Digital Twin |
|---|---|---|
| Objective | Design, one-off simulation | Monitoring, continuous optimisation |
| Connection to reality | None or historical | Real-time data |
| Cost and complexity | Low to moderate | High (sensors, IoT, cloud platform) |
| Responsiveness | Low (offline analysis) | High (live analysis) |
| Use cases | CAD, planning, simulation | Predictive maintenance, real-time logistics, and industrial monitoring |
How to build a digital model or digital twin?
Creating a digital model or digital twin follows a structured approach. Although the final objectives differ, the initial steps are shared. Here is a clear guide to building your own model or twin, step by step:
Define the objectives
First and foremost, the right questions need to be asked:
- What do you want to observe, analyse or improve?
- Do you need a static snapshot of a system, or a tool that can react in real time?
This scoping exercise makes it possible to choose the right approach (model or twin) and to target the critical parameters to include in the modelling.
Collect and structure the data.
This involves collecting the necessary data to represent the system accurately. For a model, the data may be historical, technical or design-based. For a digital twin, it is also necessary to anticipate continuous access to real-time data, including sensors, event logs, and operational flows. It is essential to validate the quality, frequency and reliability of this data.
Build the virtual model.
This step involves translating physical or logical elements into a digital representation. Shapes, behaviours, interactions or flows are modelled here. It is important at this stage to determine the required level of detail: too simple, and the model will be of little use; too complex, and it will become difficult to maintain.
Connect real-time data (for a twin only)
A digital twin must be alive: it must continuously reflect the system’s actual state. This requires setting up a connectivity infrastructure (networks, APIs, sensors…) to integrate data into the model continuously. This step is crucial for enabling adjustments, alerts and predictions based on the current state.
Test and validate the model
Before any deployment, the digital model or twin must be tested against reality:
- Does it faithfully reproduce the behaviour of the real system?
- Does it respond correctly to simulated scenarios?
This phase enables correcting inconsistencies, adjusting assumptions, and ensuring the tool is reliable for decision-making.
Optimise and adjust
Once validated, the model can be used to identify levers for improvement: cost reduction, better resource allocation, and performance gains. In a digital twin, these adjustments can be automated from real-time data, enabling dynamic and continuous adaptation of the system.
Deploy and monitor
The digital model or twin is now integrated into the target environment (factory, supply chain, building, etc.). It is then necessary to set up monitoring mechanisms, performance indicators and, in the case of the digital twin, alert or autonomous decision-making systems. Continuous monitoring ensures that the tool remains relevant as reality evolves.
Use case: What roles do digital twins and digital models play in the supply chain?
The modern supply chain is a complex, multi-stakeholder and volatile ecosystem. A digital model enables simulation of warehouse organisation, logistics flows, or demand planning. It is very useful for:
- Evaluating a new distribution scheme,
- Planning a warehouse’s capacity,
- Comparing different supply scenarios.
The digital twin, on the other hand, enables real-time observation of the chain, detecting delays, anticipating stockouts and reacting to unexpected events such as a carrier breakdown or a sudden shift in demand. Example: an e-commerce company can automatically readjust its stock levels based on real-time sales data thanks to its digital twin.
Use case: And in the factory, how are digital twins and digital models used for production and planning?
In a factory, a digital model enables the design of a production line, simulation of throughput rates, and optimisation of workstation layout. It is widely used during industrial planning phases or when designing new workshops.
The digital twin, on the other hand, enables real-time monitoring of machines, detection of performance drift, prevention of breakdowns, and dynamic reallocation of resources (personnel, machines, raw materials). An automotive manufacturer can, for example, adjust a line’s throughput rate in real time based on sensor feedback and quality data.
Conclusion: an indispensable duo for informed decision-making
Digital models and digital twins are not in opposition. They complement each other and respond to different levels of digital maturity and operational needs. The former is an excellent starting point; the latter is an extension toward a connected, agile, and resilient industry.
Digitalisation is not about doing everything in real time, but about intelligently choosing the right tools for your priorities.
