Simulation Modeling and Analysis 1. Scenario Testing * Create multiple scenarios in the simulation model. For instance, add or remove a machine or operator at different stations and observe the outcomes. 2. Key Metrics * Track lead time, throughput, and resource utilization for each scenario. * Compare scenarios to see which resource allocation
Creating a clear, structured process flow is the foundation of any effective simulation model. Whether you’re analyzing a manufacturing line, an airport operation, or a service workflow, the goal is the same: define the activities, connect them logically, allocate the right resources, and control the flow of events. This
Simulation Modeling and Analysis 1. Dispatching Rules * Program or set different scheduling heuristics (e.g., First-In-First-Out (FIFO), Earliest Due Date (EDD), Shortest Processing Time (SPT), etc.) in your FlexSim model. 2. Batch or Sequence Comparison * Experiment with various sequences in simulation (e.g., schedule high-priority orders first vs. regular jobs
Simulation Modeling and Analysis 1. Downtime Modeling * Build probability distributions or schedules for resource downtime in your FlexSim model (e.g., Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR)). 2. Scenario Analysis * Run scenarios where a critical machine fails for a certain duration. Observe queue build-up, lead
Operational problems rarely announce themselves. Instead, queues silently expand, delays accumulate, and throughput declines mysteriously.
Understand the difference between operations and technical systems, and when to use FlexSim Manufacturing vs. Factory. Includes a comparison table, case study, and downloadable diagram.
Simulation empowers Industry 4.0 by enabling virtual testing, optimization, and innovation—bridging the gap between digital design and real-world production.
Agent-Based Modeling simulates how individual agents interact to reveal complex system behaviors, helping researchers test, predict, and understand change.
System dynamics helps analyze and improve complex systems by modeling feedback, behavior, and change—turning complexity into actionable insight.
Discover how Discrete Event Simulation (DES) helps analyze complex systems, identify bottlenecks, and optimize performance for smarter business decisions.
Simulation modeling helps scientists, engineers, and businesses analyze, predict, and optimize complex systems — from weather and biology to manufacturing and markets. Discover how simulation turns data into insight and ideas into action.
Learn how to design efficient manufacturing work cells using FlexSim simulation. Explore benefits like waste reduction, cross-training, and improved flow through virtual prototyping and data-driven optimization.