Digital Twin and Manufacturing Process Simulation

In the era of Industry 4.0, manufacturing companies face the challenge of producing more, better and with fewer resources. To achieve this, process simulation and digital twin technologies have become key allies.

Table of contents:

1. Industrial simulation: anticipate before you act
2. Digital twin: from virtual model to live system
3. Simulation vs. digital twin: key differences
4. When does it pay to invest in a digital twin?
5. Real applications in Industry 4.0
   
   

Both allow you to anticipate errors, reduce costs and speed up decision-making, but they are not the same and do not always have the same return.

In this article we explain what differentiates a digital twin from a traditional simulation, when to make the leap and how tools like MES INEXION help integrate both approaches within a profitable digital transformation strategy.

Industrial simulation: Foreseeing before acting

Industrial process simulation consists of creating a virtual model of a production line or work system to predict its behaviour under different conditions. It makes it possible to analyse cycle times, equipment layout, logistical flows or energy consumption without interrupting actual production. In sectors such as automotive, food or pharmaceuticals, engineers use it to test new layouts, estimate capacities or evaluate the effects of a change of machinery before investing.

Typical applications

• Design of assembly lines or plant layout.
• Bottleneck and downtime analysis.
• Evaluation of new shifts or production sequences.
• Validation of lean or Just in Time processes.

Advantages and limitations

Simulation is a strategic planning tool: it reduces risks and accelerates pre-deployment decision-making.

However, its main limitation is the disconnect with operational reality: a simulation does not "learn" from real data nor is it updated in real time. That's where the digital twin comes in.

Digital twin: from virtual model to live system

A digital twin is the virtual replica connected in real time to a physical process or asset. Through IoT sensors, MES platforms and artificial intelligence algorithms, it collects operational data from the real environment and analyses it to predict behaviours, anticipate failures and optimise performance. Rather than being a point-in-time simulation, the digital twin is a dynamic system that evolves along with the plant, reflecting every change in state, pace or condition of the production process.

Essential components

• IoT sensors and PLCs: capture machine parameters, temperature, vibration or consumption.
• MES (Manufacturing Execution System): centralises order, traceability and performance information.
• 3D models + AI: interpret data, simulate predictive scenarios and propose automatic adjustments.

Practical example

Imagine a food packaging plant: the digital twin detects a slight variation in the speed of a conveyor belt. The system, connected via INEXION MES, analyses the probable cause (motor temperature or product accumulation) and adjusts the line to avoid a shutdown.

The result: continuous production, reduced waste and optimised maintenance.

Simulation vs. digital twin: key differences

Although they start from the same base - modelling reality - their purpose and scope are different.

Simulation is a design tool, while the digital twin is an operating system.

The former helps you plan; the latter helps you run better every day.

When does it pay to invest in a digital twin?

The implementation of a digital twin involves technological investment - sensors, software, connectivity - so it is worth evaluating its ROI based on three variables: process complexity, criticality of assets and expected return.

Cases where the return is immediate

• Continuous processes where a shutdown costs thousands of euros per hour (food, energy, chemistry).
• Multi-equipment factories with 24/7 production that need predictive maintenance.
• Robotised or automated lines requiring fine speed and precision adjustments.
• Regulated environments (pharmaceutical, aerospace) requiring full traceability and deviation control.

Common return metrics

• Reduction of unplanned downtime: -30%.
• Productivity increase: +20%.
• Maintenance cost reduction: -25%.
• Reduction of time-to-market: up to 40 %.

A digital twin not only improves efficiency; it democratises data-driven decision making, aligning operators, engineers and management under the same source of information.

Real applications in Industry 4.0

1. Automotive and robotics: Automakers use digital twins to simulate robotic lines, validate configurations and predict breakdowns before they occur, improving overall plant availability.
2. Food and beverage: They allow production to be adjusted according to demand, control temperatures and process times, and ensure real-time batch traceability.
3. Pharmaceuticals: Integrate quality and production data to anticipate deviations and ensure regulatory compliance without stopping manufacturing.
4. Energy: Facilitate predictive maintenance of turbines, power grids or pumping stations, reducing energy consumption and emissions.

In all cases, the combination of simulation + digital twin + MES allows organizations to move from reactive to predictive and optimized management.

Simulation remains the ideal tool for planning and designing processes, but the digital twin has become the driver of operational efficiency and industrial competitiveness. The two technologies complement each other: one analyses the "what if..." and the other acts on the "what is happening now".

With solutions such as MES INEXION, it is possible to unify both worlds: simulate to decide better and connect to execute with precision.

Request a personalised demo with Overtel.