Arena Analyzes Large Scale Brewery Distribution

A large-scale brewery distribution facility needed to optimize pallet throughput across a complex narrow-aisle crane storage system. The operation relied on paired pallet movement through a multi-level conveyor-elevator network, but the facility lacked visibility into where bottlenecks were forming across different times of day. Without a structured analytical approach, identifying inefficiencies in the crane scheduling, elevator sequencing, and pick-up/drop-off station allocation was impractical through observation alone. Undetected bottlenecks risked throughput shortfalls during peak distribution windows, directly impacting order fulfillment reliability.

Rockwell Automation's Arena simulation platform was used to build a detailed discrete-event digital twin of the entire warehouse distribution system. The model was decomposed into submodels representing each functional subsystem: the core conveyor-elevator network, scanner stations, multi-level pallet routing, and individual narrow-aisle crane aisles. Pallets move in pairs from the main scanner level via two elevators to two upper levels, feeding four crane pick-up points. Each aisle was modeled with two pickup and two dropdown stations positioned asymmetrically across levels, with each aisle divided into two sections each served by a dedicated crane. Arena's Input and Output Analyzers were used to validate input distributions and analyze simulation results systematically across the full operational cycle.

The simulation study produced findings that were unexpected by both the end user and the main contractor. While the system's aggregate design capacity was sufficient to handle the required pallet throughput, the model revealed that bottlenecks emerged in unanticipated locations at specific times of day — not in the cranes themselves, but in the supporting conveyor and elevator sequencing. Key outcomes included:

• Identification of time-dependent congestion points not visible through static capacity analysis
• Confirmation that overall throughput targets could be met without a full system redesign
• Actionable data for targeted operational adjustments rather than capital-intensive infrastructure changes

The digital twin allowed complex interdependencies across all system levels to be evaluated simultaneously.

• Discrete-event simulation can surface non-obvious bottlenecks that aggregate capacity calculations miss, particularly in multi-level, time-varying systems.
• Decomposing a complex system into validated submodels improves model accuracy and makes debugging significantly more tractable.
• Results should not be assumed — even well-designed systems can exhibit congestion in unexpected subsystems under real operational load profiles.
• Engaging both the end user and the main contractor in reviewing simulation outputs increases confidence in findings and accelerates implementation of recommendations.
• Digital twin studies can justify avoiding costly physical changes by pinpointing the specific interventions actually needed.