It’s no doubt that most engineers may have faced the disappointment of a seemingly perfect spreadsheet falling apart when deployed in a live factory. What appeared to work initially on paper simply doesn’t hold up to the unpredictability of production.
That’s not a failure of analysis but a limitation of the tool.
Today’s manufacturers have more options that even improve efficiency including new equipment, workflow changes, automation, and even AI. The real challenge falls in choosing the right combination of strategies to use. Traditionally, these decisions have been made using spreadsheets but static models fail to capture the complex dynamics of modern production environments.
Static tools fall short in three critical ways: they can’t handle variability, as they rely on averages and overlook fluctuations in demand, cycle times, and machine availability; they also ignore system interdependencies, failing to account for how changes one area can cause a ripple through the entire operation; and they are blind to system dynamics over time, offering only static snapshots rather than capturing how processes evolve, queues form and dissolve, or resources shift in real-world conditions.
Meanwhile, physical trials are often disruptive or expensive to conduct. Manufacturers need a low-risk, high-fidelity way to test changes before going live.
This is where Discrete Event Simulation (DES) provides a path forward.
DES models rely on four key elements to replicate factory systems accurately:
These simple yet powerful building blocks closely mirror how real-world operations function, which, as we all know, are far from predictable. DES captures this complexity by using probability distributions rather than static averages to simulate variability in cycle times, arrival rates, and equipment downtime. This approach results in more accurate and realistic models.
Further, by applying Kingman’s Equation - a formula that estimates the average wait time in a queue based on variability in arrival and service times - DES is able to show how even minor variability can drastically increase queue lengths and wait times in high-utilization systems. This insight is essential to solving real bottlenecks.
For simulation projects to drive measurable improvements, it’s essential to track key performance indicators (KPIs). In this case, the most useful KPIs include:
Together, these metrics help to define and measure success in process optimization.
Successful simulation projects follow a clear, repeatable process - from identifying the problem to implementing improvements. This methodology ensures models are accurate, results are trusted, and insights drive real operational value.
That said, here’s a guide on executing simulation projects using FlexSim.
Start with a clear objective; whether it’s eliminating a bottleneck, testing a new layout, or evaluating a staffing plan. After that, map out the process steps and decision logic, define resource assignments, and gather the essential data such as processing times, equipment availability and routing paths. This foundational step forms the conceptual model for your simulation.
FlexSim’s drag-and-drop interface, CAD layout imports, and code-free logic makes it easier to construct 3D models that accurately reflect current operating conditions. With these tools, you can visually test real process flows, clearly communicate findings to stakeholders, and build a digital twin of your facility for deeper analysis and experimentation.
Compare simulation outputs against historical data. Until a model behaves like the real system, it’s not useful. Having a validation step builds trust and ensures that results are credible.
With a validated model, you gain a virtual lab that allows you to test a wide range of improvement scenarios. You can modify staffing levels and scheduling strategies, add or relocate machines to optimize layout, integrate automated guided vehicles (AGVs) using features like the Dynamic AGV Load Types in FlexSim 25.1, and experiment with just-in-time workflows using the Timed Travel feature - all without having to disrupt real-world operations.
As manufacturers move towards smarter and more connected operations, simulation tools must keep pace. FlexSim offers advanced capabilities that support seamless integration with digital factory ecosystems while remaining accessible to users at all skill levels.
FlexSim integrates with the NVIDIA Omniverse Connector that enables simulations to be exported in USD format for use in digital twins and other collaborative platforms. This supports real-time data exchange, enhances compatibility with AI and VR tools, and connects with enterprise systems through robust database integration - ensuring that the simulation environment is able to work in harmony with the broader digital landscape.
Users are able to gain value quickly with FlexSim without needing extensive coding experience. In fact, 46% of users spend under five hours per week on modeling while there are firms that are able to build their first model in just 1.5 days.
With FlexSim’s drag-and-drop logic, prebuilt templates, and guided wizards, FlexSim shortens the learning curve and accelerates time to insight.
FlexSim empowers manufacturers to test, validate, and optimize their decisions with confidence. Whether you’re reconfiguring a layout, diagnosing a bottleneck, or investing in new equipment; DES provides you with a safe, accurate way to simulate outcomes before committing to resources.
It replaces guesswork with clarity and static spreadsheets with a living, dynamic model of your factory.
Do you have a complex bottleneck or workflow challenge you can’t solve with a spreadsheet? Get in touch with Hagerman & Company and let our experts show you how FlexSim’s intuitive, powerful tools can model your unique environment and help you find the right solution.