2026 Simio Sync

Welcome to Simio Sync 2026 - the ninth year of bringing together leaders who use simulation, digital twins, and decision intelligence to shape the future of operations.

In this opening video, Eric Howard sets the stage from Pittsburgh, a city defined by reinvention, resilience, and industrial diversity. From advanced manufacturing and healthcare to energy, aviation, sports venues, and hospitality, this introduction explores how complex systems across industries are being transformed through simulation and real-time digital twins.

The video also highlights the powerful partnership between Simio and Aegis Software, combining advanced planning, simulation, and manufacturing execution to help organizations plan intelligently, validate virtually, and execute with confidence.

Simio Sync is more than a conference - it’s a community and a movement focused on better decisions in a world of constant change.

Keynote on the vision behind bringing these capabilities together and what it means for the industry moving forward.

Every movement begins with a question.

This video tells the story of Simio Sync - from its beginnings in 2017 alongside the IISE Annual Conference in Pittsburgh to its evolution into a global forum for leaders shaping the future of simulation and decision intelligence.

What started as a belief that the most valuable insights live inside people - not software - became a place where industries learn from one another. Over the years, Simio Sync has explored leadership, digital twins, resilience, enterprise-scale decision intelligence, and real-time adaptive systems powering organizations every day.

Through moments of growth, disruption, and acceleration, Simio Sync has remained focused on one idea: bringing people together to model, test, and shape a better future.

This is the story so far - and the story continues.

Production Scheduling at scale is hard. For a global CPG snack foods manufacturer operating multiple continuous productions lines across a diverse product portfolio, it meant hours of manual Excel manipulation, tribal knowledge baked into copy-paste workflows, and a single scenario run before the day was over.

This session tells the story of how Accenture partnered with a leading snack foods manufacturer to build a simulation-driven scheduling platform — starting with a Python ETL embedded in a local Simio model, and evolving into a fully cloud-native deployment on Simio Portal backed by Azure Blob Storage, event-driven automation, and Power BI reporting.

Attendees will learn how the team designed a modular, AI-extensible ETL architecture supporting both tactical and operational planning modes; how the local CSV-based prototype was deliberately used to prove the logic before lifting it to the cloud; and how an Azure Function triggered at run completion bridges the gap between simulation output and actionable business dashboards — all without middleware or manual handoffs.

Whether you're a simulation engineer looking for a replicable architecture or an operations leader asking "could we do this at enterprise scale?", this session delivers a concrete blueprint — from first prototype to production platform.

We build simulation models to answer hard questions about capacity, scheduling, and resource allocation. But the models themselves can't answer questions — they produce data, and then someone has to interpret it. What if that changed?

This session presents a vision for AI-augmented simulation where models become systems that explain their own results, catch their own data errors, and extend their own integration pipelines. We'll walk through real examples: asking a scheduling model "why are these orders late?" and getting a structured bottleneck analysis back in seconds; feeding a model's documentation to an LLM and having it identify logic bugs a human reviewer missed; building ETL pipelines in pure Python so that AI tools can maintain and extend them without vendor involvement.

Along the way, we'll address the questions manufacturing customers ask first: where does my data go, what happens when the network is down, and how do I trust something I can't see inside? The answers are architectural, not aspirational — and they're working today.

At McDonald’s, innovation happens at full speed—and Speedee Labs is where teams can learn, iterate, and scale bold ideas in a fully functioning restaurant testing environment. For years, operational testing relied on printed tickets and manual scans through the many customer journeys, leading to data gaps, inconsistency, and missed insights. In this session, I’ll share how we partnered with Simio to rethink our approach and create McTaps, a smarter, simulation-driven way to capture operational data.

Demand for an established paint facility is expected to change significantly due to legacy product phasing out overtime and the introduction of a new product. The new product is physically larger, changing the rate parts can flow through the paint facility. Two primary questions were asked: 1) Can existing paint facilities meet future demand, and if not, when will demand exceed capacity? 2) If new paint booth technology were deployed, how much paint facility will be required? Two discrete event simulation models were developed to answer each question. The current state model played a primary role in identifying that demand would exceed capacity before a new system could be installed and then quantified the impact of implementing a provisional booth. The future state model quantified the resources required in various demand profiles and equipment configurations to ensure proper throughput and process lead-times.

Simio Sync would not be possible without the people who bring it to life.

This video is a heartfelt thank you to the presenters, moderators, partners, and behind-the-scenes contributors who made Day 1 of Simio Sync 2026 a success. Your insights, honesty, preparation, and willingness to share real-world experiences create the meaningful conversations that define this community.

To everyone who contributed their time, expertise, and energy - thank you for helping make Simio Sync a place where ideas turn into action and the future of decision-making is shaped together.

Every industry faces the challenge of managing complex operations while making confident decisions.

This introduction video shares the story of Simio, founded by simulation pioneer Dennis Pegden, and the vision to make advanced digital twin technology accessible to organizations of all sizes. Simio combines powerful simulation with Python analytics, AI, and advanced visualization to help teams explore scenarios, validate decisions, and improve performance before changes are made in the real world.

From manufacturing and logistics to healthcare and business processes, Simio Intelligent Digital Twins help organizations model complexity, reduce risk, and unlock operational potential across every industry.

Dynamic work movement is a transformative approach that allows tasks not fully completed at their designated position to transition seamlessly to subsequent positions within the workflow. This method ensures that delays are minimized and the entire process remains fluid and dynamic. It is particularly useful for tasks not entirely bound by their positional location, providing remarkable flexibility and adaptability in the workflow process. Facilitating the movement of jobs that are behind schedule to follow-on positions, dynamic work movement ensures that the production process remains on schedule, thereby enhancing overall efficiency and productivity. The implementation of dynamic work movement significantly improves the flexibility and adaptability of the production system. The streamlined process ensures continuous progress and minimizes delays, ultimately leading to a more efficient and effective production system. This approach also enhances the ability to handle variability and disruptions, ensuring that the production system can adapt to changing conditions and requirements.

Mitchel Lincoln manufactures corrugated cardboard sheets and converts them into printed, cut, folded, and bundled packaging products for industrial and commercial customers. To maximize utilization of a new corrugator with 2 billion square feet of annual capacity, Mitchel Lincoln partnered with SimWell to build a full-plant Simio model and uncover downstream constraints across the plant. The analysis produced a roadmap toward the two-billion target by prioritizing bottlenecks and investment opportunities across conversion, outbound flow, and internal material handling. Among the identified initiatives, train movement decision logic and production planning were recognized as complementary performance levers to further stabilize flow and support capacity growth. The next phase will leverage the model to analyze these interactions and enable the potential integration of a smart train dispatch algorithm that determines the optimal routing of each WIP pack to maximize flow.

Successful simulation projects are defined long before the first model is built. This talk focuses on how to effectively perform a Functional Requirements Specification (FRS) and why it is critical to project success.

Attendees will learn a structured approach to planning and facilitating FRS workshops, including how to engage stakeholders, define clear scope boundaries, assess data readiness, and make informed fidelity decisions. The session highlights four guiding principles—leading with data, right-sizing fidelity, maintaining clear scope, and delivering value in phases—and shows how they shape better project outcomes.

By grounding simulation work in a well-defined FRS, practitioners can reduce rework, avoid misalignment, and build models that answer the right questions. Participants will leave with practical techniques and a repeatable process for improving the success rate of their simulation projects. of their simulation projects.

This presentation showcases how Chevron applies Simio discrete‑event simulation to support decision‑making across construction operations and engineering management workflows. In remote construction environments, simulation is used to evaluate haul truck routing, equipment utilization, traffic interactions, and stormwater management under variable operating conditions. These models enable rapid scenario testing and reveal system behaviors that are often non‑intuitive, leading to safer operations and measurable productivity improvements.

The session also examines the use of simulation to analyze an engineering document production process, where traditional capacity calculations failed to reflect the true impact of variability. By explicitly modeling task time variation, resource utilization, and work‑in‑process limits, the analysis identified the actual system bottleneck and evaluated alternative improvement strategies. Scenarios explored include variability reduction, resource adjustments, and WIP control, demonstrating how targeted changes can significantly increase throughput while reducing cycle time.

Together, these examples illustrate how Chevron leverages simulation to move beyond intuition, uncover hidden constraints, and make data‑driven decisions in complex engineering and construction systems.

This presentation demonstrates how discrete event simulation in Simio was used to optimize resource allocation in a standalone Emergency Department facing a projected 10-30% increase in annual patient volume. By combining data analysis, interviews, and process mapping, a simulation model was developed to replicate patient flow and test various scenarios. The model revealed that the ED could manage a 20% volume surge with minor staffing adjustments, identifying critical bottlenecks such as an 87% nighttime nurse utilization rate beyond this threshold. This framework provides actionable insights for proactive and precise resource management and process optimization that other emergency departments can apply when anticipating similar challenges.

Day 2 of Simio Sync 2026 continued the conversation with deep insights, practical applications, and real-world perspectives on simulation, digital twins, and decision intelligence.

This video is a sincere thank you to the speakers, panelists, moderators, partners, and the Simio team whose preparation, expertise, and engagement made Day 2 meaningful and impactful. Your willingness to share experiences, challenges, and lessons learned is what makes Simio Sync more than an event.

Thank you for contributing to a community focused on better decisions, stronger systems, and the future of intelligent operations.