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Simulation allows to predict the impact of changes before they are implemented

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Shaping the Future of Industrial Automation

In today’s fast-paced industrial landscape, simulation plays a crucial role in smart decision-making. By digitally replicating real-world systems and production processes, businesses can forecast the results of any change — be it a new automation line, equipment upgrade, or layout modification — without disrupting actual operations. Simulation provides a risk-free environment where ideas are tested, validated, and refined.

At Digivance, we harness the power of advanced simulation tools to help you visualize and optimize your industrial workflows. Whether you’re implementing factory automation, process control, or robotics integration, our simulations offer detailed insights into timing, resource utilization, and efficiency. This minimizes costly downtime and allows confident planning before deployment.

Beyond prediction, simulation supports proactive problem-solving. With real-time modeling, you can detect bottlenecks, prevent equipment overloads, and streamline operations before they even begin. It’s not just about forecasting — it’s about refining the future of your production process with precision and foresight.

By incorporating simulation into your automation strategy, Digivance empowers you to make smarter investments, increase ROI, and stay ahead of the competition. We don’t just automate; we simulate, analyze, and elevate your business to new levels of operational excellence.

Visualize how your new production line will operate to ensure an efficient process

We understand that designing a high-performing production line is complex and time-consuming. Therefore, using a design tool can help you to simplify tasks, avoid bottlenecks through simulation and reduce investment risks.

Simulation is a way to understand the impact of changes before they are implemented in real life. That means it is possible to get feedback on how a system behaves without having the real system in place.

 

Test future production volumes and new product mixes

For a simulation to be accurate, the system’s logic must be built according to a detailed specification defined by three different categories: system description, input data, and scenarios.

  1. System description: defines the behavior of the system. It states how all processes interact and under what conditions they run.
  2. Input data: E.g., order data, the delivery schedule for raw material, or cycle times. Input data and system description are enough to build a simulation model representing reality.
  3. Scenarios: the simulation model is often built to test different scenarios and new product mixes. You can also do a wide variety of tests to see the effect on your production, e.g., what happens if a machine stops more frequently than expected or what happens if an operator can’t reach a certain throughput.

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