supply-chain-simulation-engine

name	supply-chain-simulation-engine
description	Supply chain discrete-event simulation for scenario testing and optimization
allowed-tools	["Read","Write","Glob","Grep","Bash"]
metadata	{"specialization":"supply-chain","domain":"business","category":"cross-functional","priority":"future"}
graph	{"domains":["domain:supply-chain"],"specializations":["specialization:supply-chain-optimization"],"skillAreas":["skill-area:procurement-management","skill-area:quantitative-modeling","skill-area:statistical-analysis"],"workflows":["workflow:vendor-onboarding","workflow:vendor-evaluation"],"roles":["role:supply-chain-analyst","role:operations-analyst","role:data-scientist"]}

Supply Chain Simulation Engine

Overview

The Supply Chain Simulation Engine provides discrete-event simulation capabilities for testing supply chain scenarios, policies, and disruptions. It enables what-if analysis, Monte Carlo integration, and performance optimization through simulation-based experimentation.

Capabilities

• End-to-End Supply Chain Simulation: Full network modeling
• What-If Scenario Testing: Policy and configuration testing
• Disruption Impact Modeling: Shock and recovery simulation
• Policy Optimization Testing: Inventory, sourcing policy experiments
• Monte Carlo Integration: Stochastic variability modeling
• Sensitivity Analysis: Parameter impact assessment
• Animation and Visualization: Visual simulation playback
• Performance Metric Tracking: KPI measurement through simulation

Input Schema

simulation_request:
  network_model:
    nodes: array
      - node_id: string
        type: string              # supplier, plant, DC, customer
        capacity: float
        processing_time: object
        inventory_policy: object
    arcs: array
      - from_node: string
        to_node: string
        lead_time: object
        cost: float
  demand_model:
    patterns: array
    variability: object
    events: array                 # promotions, seasonality
  supply_model:
    reliability: object
    variability: object
  simulation_parameters:
    run_length: integer
    warm_up_period: integer
    replications: integer
    random_seed: integer
  scenarios: array
    - scenario_name: string
       

Output Schema

simulation_output:
  results_summary:
    scenarios: array
      - scenario_name: string
        kpis:
          fill_rate: object
          inventory_turns: object
          lead_time: object
          cost: object
        confidence_intervals: object
  detailed_results:
    time_series: array
    event_log: array
    bottleneck_analysis: object
  scenario_comparison:
    comparison_matrix: object
    statistical_tests: object
    best_scenario: string
  sensitivity_results:
    parameters_tested: array
    impact_analysis: object
    critical_parameters: array
  optimization_insights:
    recommendations: array
    trade_offs: object
  visualization_data:
    animation_data: object
    charts: array

Usage

Inventory Policy Simulation

Input: Network model, demand patterns, inventory policies
Process: Simulate multiple policy scenarios
Output: Policy comparison with fill rate and cost

Disruption Impact Analysis

Input: Current network, disruption scenario
Process: Simulate disruption and recovery
Output: Impact quantification and recovery timeline

Network Configuration Testing

Input: Alternative network configurations
Process: Simulate each configuration
Output: Configuration comparison and recommendation

Integration Points

• Simulation Platforms: AnyLogic, Simul8, SimPy
• Data Sources: ERP, planning system data
• Optimization Tools: Combine with optimization
• Visualization Tools: Animation and dashboards
• Tools/Libraries: AnyLogic, Simul8, SimPy, discrete-event simulation

Process Dependencies

• Supply Chain Network Design
• Business Continuity and Contingency Planning
• Capacity Planning and Constraint Management

Best Practices

1. Validate model against historical data
2. Use adequate replications for statistical validity
3. Include warm-up period for steady-state analysis
4. Document model assumptions
5. Involve operations in model validation
6. Use sensitivity analysis to identify key drivers