Anonymous Food Manufacturer

A long-established American food manufacturer faced mounting pressure as consumer demand outpaced the capacity of its aging packaging infrastructure. Operating three primary packaging lines — each incorporating up to nine pieces of equipment — the facility struggled with chronic reliability issues that prevented meaningful throughput increases. In the food and beverage industry, where thin margins and strict production schedules leave little room for inefficiency, unplanned downtime and suboptimal line performance translate directly to lost revenue. The manufacturer needed a 10 percent increase in production capacity but could not afford to interrupt ongoing operations to test potential equipment upgrades through trial and error.

Burns & McDonnell, an engineering and construction firm, engaged Rockwell Automation's Arena simulation software to build a high-fidelity digital twin of the manufacturer's plant floor. The team gathered comprehensive operational data — including equipment reliability records, production schedules, and throughput rates — and constructed a virtual model of all three packaging lines. To validate accuracy, 150 days of simulation output were benchmarked against six months of historical plant data, achieving agreement within 0.25 percent. With a validated model in place, the team evaluated 42 distinct upgrade scenarios, testing individual equipment replacements (bundler, capper, tray former, palletizer) and full line overhauls across low, mid, and high performance ranges over simulated 120-day production windows. This approach allowed the manufacturer to stress-test an $18 million capital decision entirely within the digital environment before committing to physical changes.

The simulation-informed upgrade strategy delivered measurable improvements across both throughput and reliability:

• 10%+ increase in production capacity achieved with minimal disruption to ongoing operations — meeting the manufacturer's original target
• 80% less downtime than competitor projections following implementation of the recommended equipment replacements
• $18 million packaging-line investment guided by simulation data, reducing the risk of misallocated capital

Because the digital twin identified exactly which pieces of equipment were underperforming across each line, the manufacturer avoided blanket replacements and concentrated spending where it would generate the greatest throughput gains.

• Validate the model before acting on it: Burns & McDonnell compared simulation output against six months of real plant data before making any recommendations — a step that ensured the digital twin reflected actual operating conditions rather than idealized assumptions.
• Simulation justifies capital at scale: A digital twin environment allowed the team to evaluate 42 scenarios without a single hour of production downtime, de-risking an $18 million decision.
• Target equipment-level analysis, not line-level: Blanket upgrades waste capital; isolating underperforming units within each line concentrates investment where reliability gains are largest.
• Build in performance range testing: Running low, mid, and high simulations over 120-day windows captures natural production variability and produces more defensible capacity projections.