At Simio Sync 2026, a common theme emerged across presentations from Boeing, Chevron, Northwell Health, SimWell, and other industry leaders: the quality of simulation outcomes is directly tied to how effectively organizations handle data integration and workflow automation. As Jason, a senior engineer at Simio’s Consulting and Services group, emphasized in his presentation on functional requirements, “The quality of your model is limited by the clarity of your requirements.” This principle extends beyond requirements to encompass the entire data ecosystem that feeds modern simulation environments.
The conference showcased a transformation taking place across industries - from aerospace manufacturing to healthcare to energy - where organizations are moving beyond manual data processes toward integrated, automated simulation workflows that deliver insights in minutes rather than weeks.
As presented at Sync, simulation projects often struggle not because of technical modeling issues, but because of fundamental data and requirements problems. Jason Ceresoli from Simio explained: “Most models are built correctly, follow good practices, and are technically sound. The real issue is that they answer the wrong questions. When that happens, scope begins to expand. Alignment starts to break down and stakeholders lose confidence.”
He continued: “What we typically see is a workflow like this for projects. You define the problem, you build the model and you deliver results. And honestly, this makes sense. Modeling is the fun part. It feels like progress. It’s where we naturally want to spend our time as simulationists and modelers.” “But when we skip a structured requirements phase, here’s what happens: Requirements emerge during the build. Stakeholders react to what they see rather than defining their needs upfront, and the scope starts to expand without control.”
This challenge is compounded by the reality that data preparation historically consumes 60-80% of simulation project time, leaving precious little bandwidth for the analysis and decision-making that actually drives business value.
SimWell’s presentation on scaling a corrugated plant, with capacity for 2 billion square feet of production annually, provided compelling insights into data-driven simulation. Joanie Robichaud and Akrem Dhahri, simulation consultants at SimWell, alongside Christian Roy, VP of Operations and Supply Chain for Mitchel Lincoln, demonstrated how they tackled a complex manufacturing challenge where bottlenecks were preventing the plant from reaching its theoretical capacity.
“The first [reason we chose Simio] is because it’s really data driven,” the SimWell team explained. “It’s easy to have a lot of data tables and to organize them nicely in the software in order to use them efficiently in the model. As mentioned, the orders are really customized. We have every order customized to the customer. So that already means a lot of data.”
Their simulation model structure exemplified modern data integration: Input data captured all historical production data - the number of files, order characteristics simplified into families according to distributions, fluids, colors, equipment parameters, production schedules, cycle times, setup times, and workforce availability. The simulation model contained all material flow, routing rules, operational logic, and equipment-specific parameters. Outputs focused on throughput measurement, equipment utilization, time state analysis, and work-in-progress queue tracking.
The SimWell team leveraged Simio’s experimentation capabilities extensively: “We used a lot the experimentation, the experiment tab of Simio. And we could do rapid parameter adjustments and really look at the response variables and their change. When we would change scenarios, we use that to do sensitivity analysis…to really identify the bottlenecks quickly.”
Key Data Integration Innovation: The SimWell team simplified a 65-lane garage system into 15 lanes while maintaining total capacity. When they lacked historical data for train loading and unloading times, they created equations based on pile depth and loading cycles: “Our system expert estimated that it was an equation that would depend on how deep the pile is in the garage…and based on the number of loading cycles that need to happen.”
Joanie from SimWell explained the equation development: “For example, if you have one big pile like that, the train has three conveyors. So he only needs to push this one pile and boom it’s loaded. That’s it. But if you have smaller piles you need to load each conveyor separately. So then you would have y equals three loading cycles. And then x would be the depth.”
Northwell Health’s presentation on optimizing emergency department patient flow demonstrated sophisticated data integration in healthcare settings. Their team combined qualitative and quantitative data to build a comprehensive simulation model responding to a projected 10-30% increase in patient volume following a nearby hospital closure.
The Northwell Health model incorporated nine distinct entity types based on triage level and arrival mode, with customized arrival rates, task process times defined by statistical distributions, routing probabilities, and precise weekly staffing schedules. Liam Coen from the Northwell team explained: “We trimmed the top 5% of patients in all of our distributions due to documentation outliers, and we did further validation to see why this was appropriate,” demonstrating rigorous data validation processes.
Their data integration approach: “We use an external software to fit the distributions based on our input data…the blue is the real data, and that red line is a fitted data line…a Pearson six distribution essentially a normal distribution that is skewed to the left.”
The Northwell Health presentation highlighted future capabilities, noting that Python integration features being developed by Simio could significantly simplify distribution fitting processes, particularly for large-scale healthcare datasets containing tens of thousands or hundreds of thousands of patient encounters.
Chevron’s presentation, “From Intuition to Insight: How Chevron Uses Simulation to Improve Engineering and Construction Performance,” showcased data integration challenges across multiple project types. Nick Wann, a project execution advisor at Chevron, presented several case studies demonstrating how simulation transforms decision-making in complex environments.
The first case study focused on producing 100 piping isometric drawings per week for offshore platform fabrication in Korea. “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,” Nick from Chevron explained. This high-stakes environment demanded accurate data integration to model the checking and rework cycles inherent in engineering workflows.
Chevron’s approach included tracking multiple process steps—drawing prep, checking, scrubbing, back-checking, quality control—each with associated rework probabilities and resource requirements. Their analysis revealed that traditional calculations failed to account for variability: “We said we actually think we need three and a third resources to achieve 100 ISOs per week. But we’ll round up to four…Well, the contractor proposed to the owner…four full time resources plus a part time resource.”
The simulation validated the contractor’s request by incorporating process variability that static calculations missed, demonstrating the value of data-driven simulation over simplified analytical approaches.
Another case study within the presentation focused on truck haul route optimization at a mine reclamation site. Nick from Chevron demonstrated rapid iteration from simulation insights to implementation: “What we were doing was making recommendations to the contractor. 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.”
This example illustrated the power of well-integrated data workflows: the simulation could test safety scenarios (reversing traffic to reduce vehicle interactions), quantify the impact (50% reduction in vehicle interactions, 6% increase in productivity), and enable near-immediate implementation.
Nick from Chevron summarized the core message: “The power obviously is in the experiments and running multiple simulations and summarizing all of that information. Leadership teams…always love the simulation. They love to see things moving around. It makes for good presentations. But the power is in the data.”
Boeing’s presentation on modeling for dynamic work movement touched on broader organizational aspects of data integration. The Boeing team emphasized: “Given these realities, spreadsheets and static analyses, well, they’re often insufficient. And that’s where simulation really comes in and shines by building a realistic model of the production system.”
The presentation also emphasized cultural transformation: “Boeing’s culture today is changing. It’s not there yet, but the culture is definitely on the rise.” This cultural element is critical for data integration success - organizations must foster environments where “the ones here on the ground floor, the ones building the product…want to do a good job and they have good ideas. They have great suggestions.” Effective data integration requires capturing knowledge from those closest to the processes being modeled.
Multiple presenters emphasized the importance of reusable components. The SimWell team noted: “It’s also modular object-oriented design. So all of the presses and the trains were developed as reusable [objects]. And then eventually we can just move them around or change the parameters in order to have a new press in the model. And that was really quick, really easy, really efficient.”
The experimentation capabilities within Simio were consistently highlighted. SimWell ran more than 100 experiments, enabling thorough sensitivity analysis to identify bottlenecks quickly. This rapid iteration capability transforms simulation from a one-time analysis tool into a dynamic decision-support system.
Gantt visualization was repeatedly mentioned as valuable for debugging models during development. The ability to see material flow, bottlenecks forming, and trucks moving creates stakeholder buy-in while the underlying data analytics drive actual decision-making.
The Boeing team discussed creating “internal SimBits” - modular pieces of logic that resonate with end users within the company. A Boeing representative explained: “You guys have a plethora of useful modular pieces of logic that are absolutely critical to really learning, but sometimes kind of lack the ability for it to resonate with some of the end users within the company.” This approach to building internal simulation capabilities accelerates adoption and improves data integration across the organization.
SimWell’s Mitchel Lincoln project targeted increasing production capacity from 1.4 billion square feet to 2 billion square feet - a 43% increase. The simulation identified that the train (material handling system) and presses were the primary bottlenecks preventing the facility from reaching its theoretical capacity after a corrugator upgrade.
The SimWell team’s approach to lot sizing analysis, applying lean manufacturing principles (“the famous every product every interval from lean”), demonstrated how data-driven simulation enables testing of operational strategies before implementation.
Northwell Health’s simulation revealed precise impacts of volume increases: at 20% increased patient volume, door-to-provider time increased to an average of 18 minutes, treat-and-release time increased about 12% to just over 200 minutes, and nighttime nurse utilization reached 87% - identifying it as a potential bottleneck.
The Northwell Health team explained their decision-making: “The site adopted our simulation recommendation to add an eight hour shift on Sunday nighttime hours, as volume was gradually increasing around 7 to 10% at the time of implementation.” This represents the shift from reactive to proactive healthcare capacity management.
Chevron’s ISO drawing production simulation prevented resource allocation disputes by quantifying the impact of process variability. Rather than relying on simplified calculations that suggested four full-time checkers were sufficient, the simulation validated the need for additional part-time resources to account for real-world variability.
Their truck route optimization case study achieved a 50% reduction in vehicle interactions (a proxy for safety risk) while simultaneously delivering a 6% increase in productivity. The rapid implementation timeline—from simulation results to full implementation in three days—demonstrates the value of having data integration infrastructure in place.
Simio’s presentation on functional requirements provided a framework for ensuring simulation projects deliver business value from the start. Jason from Simio explained the four-phase approach - define requirements, build model, validate, deliver - ensures alignment before significant modeling work begins.
Key workshop questions Jason from Simio presented:
“If you could have anything from this tool, what would it be?” - gets stakeholders thinking about key questions
This framework directly addresses the data integration challenge by clarifying what data is needed and why before modeling begins.
Multiple presenters emphasized the importance of validation with domain experts. SimWell’s team worked closely with Mitchel Lincoln’s operations team to validate assumptions about the 65-lane garage system, train operations, and order characteristics.
Northwell Health’s team conducted comprehensive swim lane process mapping with ED staff: “This captured all of the different steps, decision points and handoffs between the various staff…culminated in 50 process steps, 15 decision points, and ten different roles.” This qualitative data collection paired with quantitative data analysis created models that staff trusted and leadership adopted.
The Northwell Health team was asked about their decision to segment patients into nine distinct entity types. Their response highlighted the tradeoff: “Those discrete, those distinct rather turnaround time differences between them…not only for the overall patient stay, but also for the different process steps that we saw.”
This decision exemplifies data integration strategy - enough granularity to capture meaningful differences, but not so much that the model becomes unwieldy or data requirements become impossible to meet.
The Boeing team touched on a fundamental challenge in complex manufacturing: “Given these realities, spreadsheets and static analyses, well, they’re often insufficient. And that’s where simulation really comes in and shines by building a realistic model of the production system.”
This observation was echoed across presentations. Nick from Chevron’s experience with analytical tools versus simulation was instructive: “When we were first introduced to the concept of viewing our projects as production systems…we started with some analytical tools…The problem that we found with analytical tools was it appeared to be a black box, and nobody really understood what was going on.”
He continued: “We couldn’t see it, we couldn’t feel it. We couldn’t, you know, we couldn’t observe the behavior. And so 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 transparency of simulation models - the ability to observe behavior, see bottlenecks forming, and understand cause-and-effect relationships - builds stakeholder confidence that static calculations cannot match.
SimWell’s experience highlighted the importance of planning for data gaps. When historical data for train loading/unloading was unavailable, they developed estimation equations with system experts rather than abandoning that aspect of the model.
Northwell Health’s approach to handling data outliers - trimming the top 5% after validation that these represented documentation errors rather than actual patient experiences - demonstrated rigorous data quality management.
The presentations showcased integration of diverse data types:
Historical operational data (SimWell’s production records)
This multi-source integration requires organizational capabilities beyond just simulation software - it demands cross-functional collaboration, data governance, and systematic validation processes.
SimWell demonstrated sophisticated Python integration for data processing. Akram from SimWell explained: “Initially we collected all of the raw data that was in Excel files. And some of them were expert estimations that we turned into formulas and different things that we processed using a Python script. Then we cleaned the data and all of the we did the exploratory data analysis.”
The team continued: “We extracted the important parameters and fit the distribution using the Python script still. And then everything was ready. We created the input tables directly in Simio…And then we once we downloaded those CSV, we were able to process them using again a Python script, because sometimes we would run some scenarios on one computer on Akram’s computer, and then I would run some others, and then we would put everything together like aggregates.”
This workflow demonstrates the evolution from manual data handling to automated, script-based data processing that enables scalable simulation projects.
Boeing’s emphasis on cultural transformation resonates across all data integration efforts: “Listen to your employees, the ones here on the ground floor, the ones building the product, the ones that are neck deep in…what goes in on these airplanes, they want to do a good job and they have good ideas.”
Effective data integration cannot be purely technical - it requires organizational cultures that value:
Data-driven decision making
The emphasis on creating internal SIMbits and reusable components points to a maturity model for simulation adoption. Organizations move from:
Ad hoc simulation projects with external consultants
This progression requires deliberate capability building and knowledge transfer, as Boeing’s team emphasized.
The most impactful presentations at Sync showcased simulation integrated into business processes:
Chevron’s three-day cycle from simulation results to full traffic reversal implementation
These examples demonstrate simulation as a continuous decision-support tool rather than an occasional analysis exercise.
While specific advanced Python integration details were discussed by SimWell’s team in their data processing workflow, Northwell Health’s reference to upcoming capabilities points to ongoing evolution in simulation technology.
The focus remains on enabling faster, more accurate data integration so analysts can spend time on insight generation rather than data wrangling.
The presentations at Simio Sync 2026 reinforced a fundamental truth: modern simulation succeeds or fails based on data integration effectiveness. Organizations from aerospace to energy healthcare to demonstrated that breaking data barriers requires:
Technical capabilities:
Flexible data structures supporting complex operational rules
Organizational capabilities:
Cross-functional collaboration between modelers and operators
Process integration:
Clear requirements definition before modeling begins
As the SimWell team demonstrated with their 100+ experiments, as Northwell Health showed with their proactive capacity planning, and as Chevron illustrated with their three-day implementation cycle, the organizations that master data integration gain significant competitive advantages.
The future belongs to organizations that recognize data integration as a strategic capability rather than a technical necessity. Those investing in automated, well-integrated simulation architectures today will find themselves better positioned to leverage increasing data volumes and growing complexity in modern operational environments.
The message from Simio Sync 2026 was clear: breaking data barriers isn’t about having more data - it’s about integrating it more effectively, validating it more rigorously, and acting on insights more rapidly. The companies that master this integration will lead their industries in operational excellence.