Digital twin representation of supply chain for real-time monitoring and simulation
Creates a virtual supply chain model for real-time monitoring, predictive analytics, and scenario simulation.
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The Supply Chain Digital Twin creates a virtual representation of the physical supply chain for real-time monitoring, predictive analytics, and simulation. It enables continuous optimization through what-if analysis and performance prediction.
digital_twin_request:
twin_scope:
network_elements: array
processes: array
time_horizon: string
real_time_feeds:
erp_integration: object
iot_sensors: array
tracking_feeds: array
model_configuration:
physics_models: object
ml_models: array
business_rules: array
simulation_scenarios: array
prediction_horizon: string
anomaly_detection_config:
sensitivity: float
alert_rules: array
digital_twin_output:
current_state:
network_status: object
inventory_positions: object
in_transit: array
production_status: object
kpis: object
predictions:
demand_forecast: object
supply_forecast: object
risk_predictions: array
kpi_projections: object
anomalies:
detected_anomalies: array
- anomaly_id: string
type: string
severity: string
location: string
description: string
recommended_action: string
scenario_results:
scenarios: array
- scenario_name: string
predicted_outcomes: object
risks: array
recommendations: array
optimization_recommendations:
immediate: array
short_term: array
strategic: array
model_health:
accuracy_metrics: object
data_quality: object
model_drift: object
visualizations:
network_view: object
flow_animation: object
prediction_charts: array
Input: Live data feeds, network model
Process: Update digital twin state continuously
Output: Real-time visibility dashboard
Input: Current state, ML models, forecast horizon
Process: Predict future network performance
Output: Performance predictions with confidence
Input: Proposed change, current twin state
Process: Simulate impact on digital twin
Output: Scenario outcome prediction
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