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Types of Simulations - Coggle Diagram
Types of Simulations
Monte Carlo Simulation
Monte Carlo simulation uses random sampling and statistical modeling to estimate mathematical functions and mimic the operations of complex systems.
Each sample will require one or more random numbers, and may require converting a uniform random number into a sample from another distribution using the inversion or acceptance-rejection methods.
Follow 4 steps:
- Establish the math model
- Determine the input value
- Create a Sample Dataset
- Set up the software
- Analyze the results
Process Simulation
Provides a mechanism for robust validation under realistic conditions and if the simulator’s outcomes are not in sync with the expected results
Simulators also provide insights into alternate process flows, suggest course corrections, help reduce defects and increase customer satisfaction.
The simulation results provide insights that support decisions in process design or resource provision with the goal to improve factors such as process performance, process and product quality, customer satisfaction and resource utilization.
Steps
- Suggest process changes
- Examine workforce and devices consumption patterns
- Identify and optimize business specific bottlenceks
- Analyze workload and resource sufficiency
- Estimate the efficiency of business process improvements
- Conduct risk assessment, minimization and control