supply-chain-simulation-engine

Development

Supply chain discrete-event simulation for scenario testing and optimization

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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
      parameters: object

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

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