ReddRadar
Agent skill
process-simulation-modeler
Discrete event simulation skill for process modeling, scenario testing, and optimization
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/operations/skills/process-simulation-modeler
Metadata
Additional technical details for this skill
- domain
- business
- category
- operational-analytics
- specialization
- operations
SKILL.md
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
| Distribution | Use Case | Parameters |
|---|---|---|
| Exponential | Arrival times | Mean |
| Normal | Processing times | Mean, Std Dev |
| Triangular | Limited data | Min, Mode, Max |
| Uniform | Equal probability | Min, Max |
| Lognormal | Repair times | Mean, Std Dev |
| Weibull | Equipment life | Shape, Scale |
Performance Metrics
| Metric | Definition | Target |
|---|---|---|
| Throughput | Units per time period | Maximize |
| Cycle Time | Time through system | Minimize |
| WIP | Work in process | Minimize |
| Utilization | Resource busy % | 70-85% |
| Queue Length | Entities waiting | Minimize |
| Wait Time | Time in queue | Minimize |
Scenario Analysis Process
- Build baseline model
- Validate against actual data
- Define scenarios to test
- Run simulations
- Analyze results
- 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
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