Manufacturing Capacity Planning with Crane Simulation

Introduction: capacity planning in aluminum production

Novelis is a global leader in flat-rolled aluminum products and the world’s largest aluminum recycler. The company works in a complex production environment, where equipment reliability, production flow, and manufacturing capacity planning are essential.

At one of its remelt facilities, Novelis produces large aluminum ingots. These ingots need to be moved between several process steps:

• Casting
• Laydown
• Storage
• Sawing
• Outbound shipping

A large overhead crane moves the ingots through these stages and plays a critical role in the plant’s operations.

Problem: evaluating crane capacity before investing in new equipment

Novelis needed to determine whether its existing crane could meet future production demand or whether the company would need to invest in a second crane.

This was not an easy decision. The crane was used across several key areas of the process. If it became a bottleneck, it could:

• Slow down production.
• Delay delivery schedules.
• Limit the plant’s ability to meet demand.

Buying a second crane would require a major capital investment. Before making that decision, Novelis wanted to check whether the existing crane was truly a limitation.

The team needed a reliable way to test the crane under different operating conditions. They wanted to look at crane movement, process times, downtime, throughput, storage use, and other key performance indicators. A crane simulation would help them evaluate these factors before making changes in the real plant.

Solution: using a crane simulation to test production scenarios

Novelis built a crane simulation model in AnyLogic to study crane capacity and test different scenarios. The model recreated the main process flow at the plant: casting, laydown, storage, sawing, and outbound truck loading.

The model allowed the team to stress-test the crane and understand how it performed under target demand. Users could change inputs such as:

• Handling time
• Maintenance intervals
• Downtime
• Casting time
• Production schedules

The model presented the results in output tables and dashboards, allowing the team to compare how different scenarios affected crane performance and production flow.

By using a crane simulation for manufacturing capacity planning, Novelis could compare different operating conditions and see how they affected production flow.

Why AnyLogic was used

AnyLogic was selected because it gave the team the tools they needed to model crane and storage operations in detail. The Material Handling Library helped represent the movement of ingots and crane activity, which was important for accurate crane simulation.

Another important benefit was flexibility. The model included adjustable parameters, so non-developers could run different scenarios without changing the model itself. Novelis also deployed the simulation through AnyLogic Cloud, allowing the operations team to use it without extra software or licenses.

Using Omniverse for clear communication

The team also connected the AnyLogic model with NVIDIA Omniverse. AnyLogic handled the simulation logic and analysis, while Omniverse provided realistic 3D visuals of the plant environment, including the overhead crane, aluminum ingots, trucks, and other plant elements.

The realistic 3D visuals made the model easier to understand, especially for leadership. Instead of looking only at data and charts, stakeholders could clearly see how the process worked and where the crane was spending time, allowing teams to discuss manufacturing capacity planning more effectively.

The Omniverse visualization was not only a static 3D render. It could also update while the simulation was running. For example, the team showed how an ingot could change its visual state after completing an operation, such as switching from silver to gold.

Results: better manufacturing capacity planning

The crane simulation showed that the existing crane could meet the target production demand. By analyzing crane states, throughput, and storage use, the team identified bottlenecks and gained a clearer understanding of how the system performed under different operating conditions.

The findings showed that minor improvements in personnel planning and crane reliability would be enough to support the required throughput. Therefore, Novelis did not need to purchase a second crane, avoiding a multi-million-dollar capital expense. This demonstrated how manufacturing capacity planning can support better investment decisions.

The final model was delivered through AnyLogic Cloud, so the operations team could continue using it as business needs change. With this tool, they can:

• Update model inputs.
• Test new scenarios.
• Evaluate future demand.
• Check how process changes may affect crane performance.

The Omniverse visualization also improved communication across teams by turning simulation findings into clear, visual insights. According to the presenters, the project improved collaboration and sparked interest in new simulation projects within Novelis.

The case study was presented by Zach Buran from Novelis at the AnyLogic Conference 2025.

The slides are available as a PDF.