Simulation & Digital Twins
Explore scenarios, predict outcomes, and support decisions before implementing changes in practice.
Test decisions before you implement them
Many organizations operate in environments where processes influence one another, uncertainty plays a major role, and small changes can have unexpected consequences.
What happens to lead times when demand increases? How does a new warehouse layout affect productivity? How does a change in a production process affect capacity, quality, or delivery reliability?
Questions like these cannot always be answered with a spreadsheet, dashboard, or traditional analysis. When interdependencies, dynamics, and uncertainty play an important role, simulation and digital twins can provide valuable insights.
With simulation and digital twins, we explore how systems behave under different conditions. This allows us to understand the consequences of decisions before changes are implemented in practice.
The right question determines the approach
Not every challenge requires simulation.
Sometimes a statistical analysis is sufficient. Sometimes an optimization model provides a direct answer. And sometimes it is necessary to examine the behavior of a process, system, or supply chain as a whole.
In these situations, we use simulation to understand how different parts of a system interact. Not only to understand what is happening, but more importantly, to explore what happens when conditions change.
The goal is not to build a model. The goal is to make better decisions.
Simulation provides insight into complex systems
In many organizations, processes are closely interconnected. Changes in one area can have consequences elsewhere in the system.
Simulation allows us to make these interdependencies explicit.
We can use it to:
- assess the impact of changes;
- identify bottlenecks;
- analyze capacity challenges;
- compare scenarios;
- support investment decisions;
- identify operational risks.
Because situations are replicated virtually, organizations can experiment without disrupting day-to-day operations.
This makes simulation a powerful tool for decision-making in complex environments.
Digital Twins: a digital representation of reality
A digital twin combines data, models, and simulations in a single digital environment.
This creates a virtual representation of a product, process, or system that can be used not only to analyze the current situation, but also to explore future developments.
Depending on the challenge, a digital twin can be used for:
- scenario planning;
- impact analysis;
- performance improvement;
- monitoring;
- incident analysis;
- inspection and maintenance.
Testing changes digitally first provides insight into the consequences of decisions before they are implemented in practice.
This reduces risks and improves the quality of decision-making.
From product development to supply chains
Simulation and digital twins are used across a wide range of domains.
In product development, they support design decisions and help evaluate performance in advance. In production environments, they provide insight into capacity, process behavior, and improvement potential. In inspection and maintenance, they help organizations better understand risks and support maintenance strategies.
Simulation and digital twins are also increasingly used in logistics, warehousing, and supply chains. By replicating processes digitally, organizations can test new ways of working, evaluate investments, and substantiate operational improvements before implementation.
This approach is particularly valuable in environments where even small improvements can have significant financial or operational impact.
Simulation, predictive simulation, and AI
Simulation is increasingly being combined with data science and AI.
While data science helps identify patterns and predict future developments, simulation provides insight into the potential consequences of different decisions.
Combining historical data, real-time information, predictive models, and simulations creates a powerful tool for decision-making.
It provides insight not only into what is likely to happen, but also into the available courses of action and their potential consequences.
From insight to well-informed decisions
A simulation model or digital twin only creates value when its insights lead to better decisions in practice.
That is why we always develop simulations in close collaboration with our clients. We combine domain expertise, data, modeling, and practical experience to examine challenges from different perspectives and provide a stronger foundation for decision-making.
For more than 45 years, we have helped organizations understand complex processes, reduce risks, and implement changes with greater confidence.
With simulation and digital twins, we not only provide insight into complex systems, but also help organizations prepare more effectively for the future.
Simulation and Digital Twins in practice
Curious how simulation and digital twins are used to address challenges in logistics, warehousing, production, and innovation? See how organizations use these methods to reduce risks, support investment decisions, and make better decisions.