Process Simulation Modeler

Process Simulation Modeler

Overview

The Process Simulation Modeler skill provides comprehensive capabilities for discrete event simulation. It supports process flow modeling, resource allocation analysis, scenario comparison, and capacity optimization.

Capabilities

• Process flow modeling
• Resource allocation simulation
• Queue behavior analysis
• Scenario comparison
• What-if analysis
• Capacity optimization
• Layout simulation
• Monte Carlo simulation

Used By Processes

• LEAN-004: Kanban System Design
• CAP-001: Capacity Requirements Planning
• TOC-002: Drum-Buffer-Rope Scheduling

Tools and Libraries

• AnyLogic
• FlexSim
• Simio
• SimPy

Usage

skill: process-simulation-modeler
inputs:
  model_type: "discrete_event"  # discrete_event | continuous | agent_based
  process_flow:
    - step: "Arrival"
      distribution: "exponential"
      rate: 10  # per hour
    - step: "Processing"
      distribution: "normal"
      mean: 5
      std_dev: 1
    - step: "Inspection"
      distribution: "uniform"
      min: 2
      max: 4
  resources:
    - name: "Operator"
      quantity: 2
    - name: "Inspector"
      quantity: 1
  simulation_parameters:
    run_length: 480  # minutes
    replications: 30
    warm_up: 60  # minutes
outputs:
  - simulation_model
  - performance_metrics
  - utilization_statistics
  - queue_analysis
  - scenario_comparison
  - recommendations

Simulation Components

Entities

• Items flowing through the system
• Examples: products, customers, orders

Resources

• Required for processing
• Examples: machines, operators, tools

Queues

• Waiting areas
• FIFO, priority, or custom rules

Processes

• Work performed on entities
• Service time distributions

Statistical Distributions

Performance Metrics

Scenario Analysis Process

1. Build baseline model
2. Validate against actual data
3. Define scenarios to test
4. Run simulations
5. Analyze results
6. Make recommendations

Monte Carlo Simulation

For uncertainty analysis:

1. Define input distributions
2. Run many iterations
3. Collect output distributions
4. Calculate confidence intervals
5. Identify risk factors

Model Validation

• Compare to historical data
• Face validity with experts
• Sensitivity analysis
• Stress testing

Integration Points

• CAD/layout systems
• ERP data sources
• Real-time data feeds
• Optimization solvers