With the level of maturity reached by Artificial Intelligence, the role of industrial simulation is evolving rapidly. It is moving from an analytical tool to an active component of Decision Intelligence systems. Simulation will no longer be limited to representing and analysing a system. It will increasingly influence operational decisions and eventually propose the most effective choices in an automated way.
Those who design, optimize, and manage complex systems are increasingly asking themselves the same question — somewhere between curiosity and genuine expectation:
Could Artificial Intelligence eventually help alleviate — or partially replace — the decision-making process behind operational choices?
In a traditional approach, simulation is used to represent a system, analyse alternative scenarios, and provide data for informed decisions.
With the evolution of Digital Twins, models have already gained the ability to remain consistent with real systems over time. This is also achieved through the continuous updating of operational data.
Today, the distinctive contribution of Artificial Intelligence lies in using simulation not only to analyse a system. It also serves as an environment for training and validating AI systems. One example is reinforcement learning, already integrated into some simulation platforms. These systems can learn decision-making strategies through interaction with the model.
More broadly, the relationship between simulation and AI is evolving. It is moving from a model in which simulation supports AI to one in which AI actively contributes to structuring, guiding, and orienting the simulation and decision-making process.
| Aspect | Traditional Simulation | Simulation → AI | AI → Simulation |
|---|---|---|---|
| Role of Simulation | Scenario analysis | Training environment | Guided and adaptive system |
| Role of AI | Absent | Learning from simulation | Decision generation and selection |
| Output | Evaluation of alternatives | Learned strategies | Suggested and prioritized actions |
| Decision-Making Process | Human-driven | Supported by AI models | Progressively structured by AI |
This evolution brings simulation directly into operational processes, where it becomes an active component of decision-making in the Smart Factory.
Signs of a further evolution are beginning to emerge. In highly variable environments, such as manufacturing, logistics, and automation, the challenge is no longer the ability to generate or analyse scenarios. It is the ability to manage decision complexity.
The role of simulation is therefore shifting. It is no longer only an environment in which AI learns. It is also becoming the structure through which the decision-making process is progressively organized, filtered, and streamlined.
It is desirable for the integration between simulation and Artificial Intelligence to evolve toward models capable of:
Simulation would no longer be used exclusively as an analytical tool, but as a structure capable of organizing and guiding decisions, making the process more explicit, coherent, and manageable, especially in complex environments.
| Dimension | Current Approach | Expected Evolution |
|---|---|---|
| Role of Simulation | Analysis and training environment | Active decision-making structure |
| Role of AI | Learning from simulation | Guidance of decisions |
| Output | Strategies and insights | Suggested and prioritized actions |
This approach makes it possible to manage complex systems with greater awareness. It also reduces uncertainty and makes the decision-making process more structured, even in highly variable operating environments.
An AI-integrated Digital Twin represents an evolution of the simulation model. It not only replicates the real system, but also acts as a dynamic environment capable of observing, interpreting, and interacting with the operational context.
From this perspective, the model tends to become a form of living simulation, connected to real-world data and control systems, including in virtual commissioning scenarios. It can support the entire lifecycle of an asset, from design to operational management.
The integration of decision-making capabilities introduces an additional layer. The digital twin no longer simply represents the system. It also helps make decisions explicit and interact with decision-makers, while preserving the central role of the engineer and the decision-maker.
For these capabilities to be truly effective and scalable, the model must be embedded within a broader ecosystem where people, systems, and data are coordinated throughout the entire asset lifecycle (Digital Factory).
By Digital Factory, we mean an integrated environment where design, simulation, and operational management converge.
Unlike the Smart Factory, which focuses on automation and interconnectivity, the Digital Factory also incorporates the decision-making and predictive dimensions.
In this direction, the integration of simulation and Artificial Intelligence could help reduce the time between observation and action, making the decision-making process faster and more structured, with a direct impact on the ROI of simulation.
In particular, this would translate into the ability to:
| Area | Traditional Simulation | Simulation + AI |
|---|---|---|
| Manufacturing Efficiency | Initial resource optimization | Prioritization of operational actions |
| Costs | Reduction of inefficiencies during the design phase | Reduction of decision uncertainty |
| Layout | Configuration validation | Guided selection of more effective configurations |
| Energy | Analysis of consumption | Decision support for energy optimization |
Towards Decision Intelligence
Decision Intelligence represents an approach focused on engineering the decision-making process, in which data, models, and algorithms contribute to making decisions more informed, contextualized, and verifiable.
Within this paradigm, the interaction between AI and simulation introduces a substantial change in the role of the model. Simulation is no longer limited to describing or analysing a system. It actively participates in managing decisions.
As a result, cognitive models are emerging. They can relate data, decision logics, and system behaviour, making the operational implications of decisions explicit.
At this stage, simulation becomes part of a broader technological framework, associated with Decision Intelligence initiatives within the enterprise, where the decision-making process is progressively structured, made traceable, and engineered.
Artificial Intelligence is transforming simulation from an analytical tool into a decision-support system. It helps support and structure operational decisions, improving the speed, quality, and consistency of decision-making in complex environments.
No. Artificial Intelligence does not replace simulation engineers, process engineers, analysts, or decision-makers. It can automate certain analytical tasks and suggest alternatives, but problem formulation, model validation, interpretation of results, and final decisions remain human responsibilities.
Decision Intelligence is an approach that integrates data, models, and algorithms to improve the decision-making process. It enables more informed, contextualized, and verifiable decisions, especially in complex environments.
AI and simulation create the most value in complex and dynamic environments, such as supply chains, logistics, and manufacturing. In these contexts, they enable faster decisions and the management of multiple constraints and alternative scenarios.
How the integration of AI and simulation is evolving toward decision-making models.
(Article by AnyLogic)
How AI, interoperability, and industrial simulation are converging to enable intelligent workflows, Digital Twins, and decision-support systems.
(Article by Flexcon)
Contact Flexcon to discover how simulation can transform decision processes in your organization.
