Solving Complex Problems Across Industries: The Universal Language of Simulation

The power of simulation transcends industry boundaries. At Simio Sync 2026, a remarkable pattern emerged across presentations from aerospace, healthcare, energy, manufacturing, and food service sectors - while these industries operate in dramatically different contexts, they face fundamentally identical operational challenges: capacity planning, process optimization, resource allocation, and managing complexity that exceeds traditional analytical tools.

This wasn’t isolated to a few keynote examples. Throughout the conference, presenters from diverse sectors repeatedly demonstrated how simulation serves as a universal problem-solving language, enabling organizations to model variability and complexity that spreadsheets simply cannot handle. What emerged was a comprehensive picture of how modern enterprises across all sectors are turning to discrete event simulation to make critical decisions about capital investments, operational efficiency, and strategic planning.

Aerospace Manufacturing: Boeing’s Dynamic Work Movement Challenge

Boeing’s paint facility expansion exemplifies how aerospace complexity demands sophisticated simulation capabilities. The company faced a critical greenfield expansion decision requiring them to determine equipment and space requirements for future demand - a challenge involving batching strategies, buffer sizing, and equipment counts with significant duration variability.

“This drove us to innovate a different approach to model something that incorporated work movement between positions, which we call the hybrid approach,” explained Chris Tonn, a Boeing simulation engineer. The traditional approach proved insufficient because “it lacks the flexibility to handle constraints of late parts, certifications, whatever constraints are driving a delay from the nature of our production system.”

The aerospace industry's reality presents unique challenges. Production schedules cannot accommodate delays for minor components when aircraft involve thousands of jobs and millions of parts. The system also needs to handle overflow parking scenarios and manage constraints around parts availability and certification requirements for completing last-minute jobs.

Through simulation, Boeing successfully identified when their current system would fail to meet demand, properly sized their building expansion, and optimized both space utilization - critical given premium real estate costs - and equipment investments. The analysis revealed bottlenecks and constraints that would have been impossible to identify through traditional methods, enabling data-driven decisions about capital investments worth millions of dollars.

Packaging Manufacturing: Mitchel Lincoln’s Path to 2 Billion Square Feet

The packaging industry presented a strikingly similar challenge at the opposite end of the complexity spectrum. Mitchel Lincoln, a corrugated box manufacturer, had recently purchased new equipment with capacity for 2 billion square feet of production annually. Yet despite this investment, their actual throughput remained stuck at 1.4 billion square feet.

“The suspected bottlenecks are either the train at the presses, in the garage or the packaging lines. So, this was really the ultimate goal of the project, to really identify the bottlenecks and even find solutions or test different investment solutions,” explained Christian Roy, Vice President of Operations and Supply Chain at Mitchel Lincoln.

The simulation team chose their platform because “it’s really data driven” and could handle the complexity: “The orders are really like customized. We have every, every, every order is customized to the customer. So that already means a lot of data.”

Their analysis revealed multiple bottleneck layers: “When we did bottleneck the train, we were able to get a little bit more, a little bit higher than this first test…But there was a second bottleneck that was created at the presses.” The team performed over 100 experiments to identify the optimal combination of improvements and investments.

Like Boeing, Mitchel Lincoln discovered that their most complex operational challenges - involving custom routing, variable processing times, and intricate material handling - required simulation capabilities far beyond spreadsheet analysis.

Healthcare Operations: Northwell Health’s Emergency Department Optimization

Healthcare operations might seem worlds apart from aerospace and manufacturing, yet Northwell Health faced identical fundamental challenges when preparing their Manhattan emergency department for a projected 10-30% patient volume increase.

The site’s unique constraint added urgency: “It’s not attached to a partner facility for admissions…any admission of patients requires an ambulance journey at least two miles uptown, which can take, you know, ten minutes, sometimes 15 to 20 minutes.” When a nearby hospital announced closure, Northwell needed to “test the impact of this increased volume on their emergency department capacity, to highlight their staffing or resource constraints, and to identify potential optimizations and mitigation strategies.”

The simulation team created nine distinct patient entity types, customized arrival rates, and “various probabilities of where the patient might go if they get a certain test.” They modeled the precise weekly staffing schedule and created detailed routing logic: “Based on our nine different patient types, what is the probability that a certain patient type is going to go to a chair to a full exam room, to a hallway bed.”

The healthcare application demonstrated the same pattern seen in aerospace and manufacturing: complex routing decisions, variable processing times, resource constraints, and the need to model scenarios that would be prohibitively expensive or disruptive to test in reality.

Energy Sector: Chevron’s Engineering and Construction Challenges

Chevron’s simulation applications revealed how the universal patterns extend beyond traditional production environments into engineering workflows and construction logistics. Their challenges spanned two distinct domains - both requiring simulation to solve problems that traditional analytical tools couldn’t address.

Engineering Drawing Production

For an offshore platform project, Chevron’s engineering contractor needed to produce 100 piping isometric drawings per week. “The consequence of not doing so means that you have entire fabrication yards at a standstill waiting for engineering drawings. And so the consequences are actually quite high,” explained Nick Wann, a Project Execution Advisor at Chevron.

The engineering process involved multiple review stages with variability in recycling rates: “There’s a drawing prep step. Then those drawings are then checked. A certain amount of them are recycled have to be reworked. Then they’re going to go to scrubbing and back check again a certain amount of them will be will fail checking and will need to be reworked.”

Simple resource calculations suggested four full-time checkers would suffice, but simulation revealed the impact of process variability: “The check step was on average 75 minutes with a maximum of four hours. So that’s quite a bit of variability.” When they modeled scenarios to reduce maximum check time from four hours to two hours, system performance improved dramatically.

The simulation prevented a potentially costly mistake. Without accounting for variability, “what could very easily have happened here is the owner said, oh no, no, no, no, no contractor, you’re trying to pull one over on me. You don’t need that extra full part time resources.” The analysis revealed that variability-blind calculations would have left the project dangerously under-resourced.

Construction Site Safety and Logistics

Chevron also used simulation for truck haul route optimization on a mine reclamation project, focusing on both productivity and safety. The model tracked trucks moving material across the constantly changing site topography.

A critical safety issue emerged at "Stairway to Heaven"—an extremely steep section of the site. Trucks descending this slope carried full loads, creating dangerous conditions for vehicles they might encounter. Meanwhile, trucks returning uphill were empty but needed to maintain speed to make the climb. After a near-miss incident, the team proposed reversing traffic flow.

After a near-miss incident, the team proposed reversing traffic flow. The simulation results led to immediate action: “However they saw these results. And then the next day had their drivers practicing this new route, and by the third day had fully reversed traffic on site. And so, this is a good example of how we can use Simio to solve not only productivity problems, because that wasn’t actually the objective of this particular analysis is really more of a safety thing.”

Chevron’s experience reinforced a critical insight about simulation adoption. Years earlier, they had attempted similar analyses using analytical tools, but “the problem that we found with analytical tools was it appeared to be a black box, and nobody really understood what was going on in the black box…When we would get results that were counterintuitive or what we expected, the natural light is what I don’t understand. It’s a bit of a black box. I’m not going to trust it.”

The visual, observable nature of discrete event simulation solved this trust problem—what simulation professionals call "model credibility." Teams that are not used to simulation get a lot of value out of seeing the simulation in action. They get really engaged when you can show them whether it's trucks moving or bottlenecks forming.

The Universal Patterns: Five Industries, One Language

Pattern 1: Complexity Beyond Static Modeling Capabilities

Every organization encountered scenarios where traditional analytical tools failed. Boeing couldn’t model dynamic work movement. Mitchel Lincoln’s customized orders created data complexity spreadsheets couldn’t manage. Northwell needed to model nine patient types with probabilistic routing. Chevron’s engineering workflows involved recursive quality loops that simple calculations missed.

The common thread: all required modeling complex routing and variability that static modeling in Excel simply cannot handle.

Pattern 2: Speed to Insight Over Perfect Accuracy

All five organizations prioritized rapid scenario testing over absolute precision. Mitchel Lincoln ran over 100 experiments. Boeing developed reusable components for quick layout changes. Northwell created a flexible model to test multiple volume scenarios. Chevron could reverse traffic patterns within days of simulation results.

As one Nick Wann from Chevron noted about their 85% utilization ceiling discussions: “We don’t always get this right…Oftentimes it’s a bit of a negotiation. So, we say look 80 to 85% is what we advise. And then we let them make their own decision.” The focus remained on enabling timely decisions rather than achieving theoretical perfection.

Pattern 3: Integration Into Operational Workflows

Successful implementations embedded simulation directly into decision-making processes rather than treating it as separate analysis. Boeing developed it for capital investment decisions. Mitchel Lincoln used it for investment scenario planning. Northwell integrated it into capacity planning. Chevron’s contractor implemented traffic changes within three days of simulation results.

The Chevron traffic reversal exemplifies this integration: simulation didn’t just provide recommendations - it enabled immediate operational changes because stakeholders trusted the visualized results.

Pattern 4: Measuring Success by Decisions Made

All organizations evaluated simulation success based on actionable decisions rather than model sophistication. Boeing sized their building expansion. Mitchel Lincoln identified specific bottleneck combinations requiring investment. Northwell prepared resource plans for volume increases. Chevron validated resource requirements and changed traffic patterns.

As Chevron’s experience demonstrated, simulation’s value lies in preventing costly mistakes - like under-resourcing an engineering team - rather than in the elegance of the model itself.

Pattern 5: Visualization Builds Trust and Adoption

Multiple presenters emphasized how seeing the simulation in action-built stakeholder confidence and established model credibility. Chevron noted that earlier analytical tool attempts failed because results came from a “black box” nobody understood. When they switched to visual simulation, adoption accelerated: “Teams that are not used to this…get a lot of value out of the seeing the simulation…they always love the simulation. They love to see things moving around. It makes for good presentations. But the power is in the data.”

The Cross-Industry Learning Opportunity

The multi-industry advantage creates opportunities for cross-pollination of approaches. Consider these potential knowledge transfers:

From Healthcare to Manufacturing: Northwell’s sophisticated patient routing logic based on acuity levels could inform Mitchel Lincoln’s approach to prioritizing rush orders through their press system.

From Construction to Aerospace: Chevron’s rapid implementation of simulation results (traffic reversal in three days) could inspire Boeing’s approach to operational changes during facility transitions.

From Packaging to Healthcare: Mitchel Lincoln’s lot sizing experiments and lean manufacturing principles (every product every interval) could help Northwell optimize patient batching for similar procedures.

From Energy to All Sectors: Chevron’s lesson about visualization overcoming the “black box” problem provides a roadmap for any organization struggling with simulation adoption.

Universal Challenges, Universal Solutions

The Simio Sync 2026 presentations revealed a fundamental truth: despite dramatic differences in what they produce - aircraft, boxes, patient care, or engineering drawings - organizations face remarkably similar operational challenges. The universal language of simulation provides consistent value because the underlying problems remain constant:

• How do we plan capacity when demand and processing times vary?

• Where are our bottlenecks, and what investments will relieve them?
• How do we allocate limited resources across competing demands?
• What happens when we change our process or layout?

Boeing employee reflections on company culture - “it takes a real team to make progress, but it has to be balanced with a culture that’s respectful and with people that truly honor each other” - applied equally to simulation teams across all industries. Success required not just technical sophistication but organizational trust and collaborative problem-solving.

The evidence from six distinct organizations across five industries demonstrates that simulation has evolved from a specialized analytical technique into a universal operational language. Whether optimizing aircraft production, scaling box manufacturing, preparing emergency departments, managing engineering workflows, or ensuring construction safety, the fundamental patterns remain consistent: model complexity and variability, test scenarios rapidly, integrate insights into operations, measure success by decisions made, and build trust through visualization.

Organizations that recognize simulation as a universal problem-solving tool - rather than an industry-specific application - gain competitive advantages through enhanced analytical capabilities and improved decision-making processes. The path forward isn’t about finding the perfect model but about enabling better decisions faster.