Automated Container Washing Line Optimization with Simulation | Case Study

The client faced the task of designing a complex technological line consisting of:

• multiple washing and drying sections,
• robotic loading and unloading systems,
• conveyors,
• buffers,
• turntables,
• and a scalable structure (base variant: 4 washing sections; future variant: 6 sections).

While the initial concept defined cycle times and target throughput, the client needed to verify real feasibility before starting mechanical design. Due to the system’s complexity, key risks included:

• bottlenecks and congestion,
• lack of synchronization between sections,
• failure to meet performance targets,
• costly modifications during commissioning (estimated risk: over €100,000).

Key questions:

• Will the line achieve the required throughput?
• Will the expected technical availability be met?
• What changes are needed to ensure process stability?
• How will the system behave after expansion?

Simulation objectives:

• Validate cycle time depending on the number of sections
• Analyze alternative line configurations
• Provide 3D visualization

Solution: Advanced Process Simulation in FlexSim

Nextomation carried out a full simulation of the washing line using FlexSim simulation, replicating:

• 2D layout and topology,
• all washing modules, turntables, and robots,
• cycle times (variant 1: 90 s; variant 2: 60 s),
• buffer logic,
• roller transport,
• robot utilization,
• real-time material flow.

Watch the simulation we have designed:

Key steps:

1. Mapping the initial concept
A complete model based on the client’s assumptions enabled early identification of issues not visible in static 2D layouts.

2. Bottleneck analysis
The simulation revealed:

• linear tray buffers as a key bottleneck,
• upstream filling logic causing congestion at the end of the line,
• full throughput reached only after several hours,
• robots periodically idle, reducing technical availability.

3. Buffer redesign

• A new buffering strategy was developed:filling buffers from the end,
• adjusting tower capacities (10/14/18 positions depending on variant),
• modifying transport speed (0.3 m/s),
• dynamic batch filling,
• full synchronization with robot cycles.
• This significantly improved process stability.

4. WHAT-IF analysis
Tested scenarios included:

• 4-section base variant,
• 6-section future expansion,
• different buffering logics,
• varying numbers of robots.

All without building a physical prototype.

5. Comprehensive deliverables
The client received:

• 3D animations of the process,
• simulation videos of buffer behavior,
• detailed datasets (~50,000 events),
• performance and robot utilization charts,
• updated layout with recommended changes.

• Full line performance achieved in 33 minutes (instead of several hours)
• Stable buffer operation without robot waiting time
• Elimination of congestion at the end of the line
• Confirmed cycle time feasibility (90 s → 40 units/hour; 60 s → 60 units/hour)
• Enabled safe expansion to 6 sections
• Balanced robot utilization (e.g. 83% for one robot in a scenario)
• Reduced risk of design errors

Business benefits:

• Strong support in tender processes (data, charts, visualizations)
• Avoided commissioning modification costs (> €100,000)
• Faster concept validation without physical prototypes
• Increased project predictability for the end customer
• Proven scalability of the solution

Simulation allowed the client to:

• verify feasibility before physical implementation,
• analyze buffer behavior in detail,
• eliminate conceptual errors,
• evaluate alternative scenarios,
• shorten design and implementation time,
• minimize technological risk,
• define robot workloads and parallel operation potential,
• prepare clear visual materials understandable also for non-technical stakeholders.

Thanks to advanced simulation by Nextomation, the client not only confirmed that the line would work but also received a set of actionable recommendations that improved stability, ensured scalability, and enabled confident discussions with the end customer.

Simulation became:

• an engineering tool,
• a sales support asset,
• a foundation for business decisions,
• and a key factor in reducing costs and risks.

This project is a strong example of the value delivered by combining analytical, technological, and consulting expertise in complex production systems.