How-To

What Is System Dynamics?

System dynamics helps analyze and improve complex systems by modeling feedback, behavior, and change—turning complexity into actionable insight.

System dynamics is a powerful approach for understanding how complex systems behave, evolve, and interact over time. It provides a structured way to study feedback loops, time delays, and cause-and-effect relationships that shape the behavior of systems—whether in business, engineering, or society.

At its core, system dynamics helps us answer a fundamental question: Why do systems behave the way they do—and how can we influence their outcomes?

Understanding Complex Systems

Modern organizations and industries operate within intricate networks of interdependent elements—people, processes, resources, and technologies—all influencing one another. System dynamics provides a lens for analyzing these interactions.

By modeling how these components interact, we can visualize how changes in one area ripple through the entire system. This allows decision-makers to better anticipate the consequences of their choices, test “what-if” scenarios, and identify leverage points for improvement.

Applications Across Industries

System dynamics is widely used in diverse fields such as:

Economics and Business: To study market behavior, resource allocation, and organizational performance.
Manufacturing and Engineering: To improve production flow, supply chains, and energy systems.
Transportation and Infrastructure: To model network congestion and optimize system performance.
Environmental and Social Systems: To explore sustainability challenges and policy impacts.

In business, for example, system dynamics can reveal how decisions about pricing, investment, or staffing influence performance over time. It can also show how feedback—both positive (reinforcing growth) and negative (stabilizing change)—shapes a company’s long-term trajectory.

From Insight to Action

The strength of system dynamics lies not just in understanding systems but in improving them. By developing simulation models, analysts can experiment safely—testing strategies before implementing them in the real world.

Through this process, organizations can:

Predict how systems respond to internal or external changes.
Identify bottlenecks and inefficiencies.
Design strategies to enhance adaptability and resilience.

In essence, system dynamics transforms complexity into clarity. It helps leaders see beyond immediate symptoms to uncover the deeper structures driving performance—enabling smarter decisions and more sustainable outcomes.

Where should you allocate resources?

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

Building a Process Flow in FlexSim

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

Which job should you schedule first? In what sequence?

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

Given the failure of a resource, how does this this affect your delivery time?

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