Babysitter process-simulation-modeler

Discrete event simulation skill for process modeling, scenario testing, and optimization

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/business/operations/skills/process-simulation-modeler" ~/.claude/skills/a5c-ai-babysitter-process-simulation-modeler && rm -rf "$T"

manifest: library/specializations/domains/business/operations/skills/process-simulation-modeler/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