Chemical Manufacturer Supply Chain Modeling

A chemical manufacturer planning a significant plant expansion needed to determine whether its existing storage and shipping infrastructure could absorb the increased production volume — or whether additional capital expenditures would be required. In process-intensive industries, capacity mismatches between production and downstream logistics create costly bottlenecks: product backs up in storage, shipping throughput becomes the constraint, and capital is either over-deployed or insufficient. Without a reliable model of how the expanded production line would interact with storage tanks, piping manifolds, and shipping facilities, the company faced major investment decisions with limited analytical foundation.

Rockwell Automation's consulting services team built a detailed supply chain digital twin using Rockwell Software Arena Simulation Software. The model replicated the full production line — including storage tanks, manifold and piping configurations, and shipping facility activities — and was paired with a custom Excel front-end that gave the company's engineers direct control over key parameters: tank capacities, initial product volumes, activity times at shipping stations, production unit capacities, piping routing configurations, and the number of available loading positions. This front-end abstraction allowed engineers to configure and re-run the Arena simulation without modifying the underlying model, enabling rapid scenario comparison across both the current system and the proposed expanded configuration.

The digital twin simulation provided the chemical manufacturer with the analytical clarity needed to make confident capital allocation decisions. Key outcomes included:

• No additional storage tanks required to support the expanded production throughput
• No new shipping facilities needed, avoiding significant unplanned capital expenditure
• The model validated that the existing infrastructure could absorb increased output with process adjustments rather than physical expansion
• The simulation framework remained in active use post-project, supporting ongoing process improvement and capacity planning initiatives as production conditions evolved

• Simulate before you build: A supply chain digital twin can prevent unnecessary capital expenditure by validating infrastructure assumptions before committing to physical expansion.
• Engineer-accessible tooling matters: Wrapping simulation models in familiar interfaces (like Excel) accelerates adoption and allows subject-matter experts to run scenarios without simulation expertise.
• Model reusability extends ROI: Designing the simulation for ongoing use — not just a one-time study — turns a project deliverable into a durable planning asset.
• Digital twins are effective for interconnected systems: When production, storage, and logistics are tightly coupled, simulation captures interaction effects that static analysis misses.