1. Method Name

Workflow Simulation

2. Alternative Names

Process Simulation, Discrete Event Simulation

3. Brief Description

Workflow simulation is a technique that uses a software model to mimic a real-world workflow or process. It allows you to analyze process behavior over time, test various scenarios (“what-if”), and predict the impact of changes without having to interfere with the real world.

4. Purpose / When to Use

It is used in business process reengineering (BPR), capacity planning, bottleneck analysis, and the design of new production lines and service centers. It is ideal when changes are too costly or risky to test in a live environment.

5. Procedure / How to Apply It

1. Map the process: Create a detailed map of the process you want to simulate.
2. Collect data: Gather real-world data about the process: the durations of individual steps (including variability), arrival rates (e.g., customers per hour), and resource availability (people, machines).
3. Build a model: Use specialized simulation software (e.g., Simul8, AnyLogic, FlexSim) to build a model of the process.
4. Test and validate the model: Ensure that the simulation model behaves the same way as the real-world process.
5. Experiment: Run the simulation and test various scenarios: What happens if we add one worker? What if the machine breaks down 10% more often? What will be the impact of new technology?
6. Analyze the results and make a decision.

6. A Real-World Example

The airport wants to change its security screening process. Using a simulation, they will create a model of the current situation. They will then test various options: adding another scanner, introducing separate lines for families with children, and changing the layout. The simulation will show them which change will most effectively reduce the average wait time for passengers without incurring unreasonable costs.

7. Benefits

- It allows you to experiment without risk or cost
.- It helps you make better, data-driven investment decisions
.- It reveals the unexpected consequences of changes and the dynamics of the system
.- It quantifies the benefits of proposed improvements.

8. Risks / Limits

- High cost and complexity: Requires expensive software and specialized knowledge
.- "Garbage in, garbage out": If the input data is inaccurate, the simulation results will be worthless.
- Over-simplification: A model is always just a simplification of reality. If it omits important factors, it can be misleading
.- Time-consuming: Creating and validating a good model can take a long time.

9. Practical Tips

- Start with a simple model and gradually add details
.- Don't forget about variability: The greatest benefit of simulation lies in its ability to model the randomness and variability present in the real world.
- Visual animation: Modern tools provide 2D or 3D animations of the process, which greatly aid in communicating results and understanding dynamics.