AI Supply Chain Digital Twin System

AI-powered digital twin of the supply chain

A supply chain digital twin is a simulation and optimization system that reflects the state of the global network of suppliers, production facilities, warehouses, and transportation in real time. The value lies in scenario analysis: what would happen to a customer's delivery if the Shanghai port were closed for a week today? The answer is available in seconds, not a meeting.

Model Components

Physical network nodes:

• Suppliers (tier-1, tier-2, tier-3) with performance and reliability characteristics
• Production sites: capacity, changeover time, current workload
• Warehouses and DC (Distribution Center): capacity, pick & pack speed, current stock
• Transport hubs: ports, airports, customs with expected delays

Streams:

• Material: physical cargo flows with tracking (AIS for maritime, API for freight carriers)
• Information: orders, confirmations, forecasts in EDI/API
• Financial: payment terms, credit risk of suppliers

Technical architecture

Event-driven Digital Twin:

class SupplyChainTwin:
    def __init__(self):
        self.nodes = {}  # suppliers, plants, DCs, customers
        self.links = {}  # transportation lanes
        self.inventory = {}  # текущие запасы на каждом узле
        self.orders = []  # активные заказы в пути

    def process_event(self, event):
        """
        События: shipment_departed, shipment_delayed, supplier_disruption,
                 demand_change, port_closure
        """
        if event.type == 'shipment_delayed':
            affected_order = self.orders[event.order_id]
            affected_order.eta = event.new_eta
            # Пересчёт всех downstream зависимостей
            self._propagate_delay(affected_order)

        elif event.type == 'supplier_disruption':
            supplier = self.nodes[event.supplier_id]
            supplier.capacity = event.reduced_capacity
            # Запуск альтернативных поставщиков / перепланирование
            self._replan_sourcing(supplier, event.duration_days)

Graph Database for the chain: Neo4j or TigerGraph: suppliers, components, and products—as a graph. Algorithms: shortest path (alternative route), PageRank (key risk nodes), community detection (clusters of interdependent suppliers).

Scenario analysis and simulation

Monte Carlo simulation:

def simulate_disruption_impact(network, disruption_scenario, n_simulations=10000):
    """
    Для каждого сценария: случайная выборка параметров (время простоя, мощность)
    из распределений → симуляция цепочки → outcome metrics
    """
    outcomes = []
    for _ in range(n_simulations):
        # Случайная реализация сценария
        disruption_duration = np.random.lognormal(
            disruption_scenario['mean_log'],
            disruption_scenario['std_log']
        )
        # Симуляция с данным сценарием
        sim_result = network.simulate(disruption_duration)
        outcomes.append({
            'service_level': sim_result.service_level,
            'revenue_at_risk': sim_result.lost_revenue,
            'recovery_time': sim_result.time_to_normal
        })

    return pd.DataFrame(outcomes)

Typical scenarios:

• Closure of a major port (Shanghai, Rotterdam) for N days
• Qualification of a new supplier to replace the problematic one
• Doubling the demand for a particular product
• Container delay at customs (worst case 30 days)
• Natural disaster in the supplier's tier-2 region

Real-time inventory optimization

Multi-echelon Inventory Optimization: Inventory is optimized not at each warehouse independently, but jointly:

from scipy.optimize import minimize

def optimize_safety_stocks(network, service_level_target=0.95):
    """
    Оптимизация safety stocks с учётом correlated demands и leadtime variability
    Цель: минимизация общего капитала в запасах при заданном сервисном уровне
    """
    def objective(safety_stocks_vector):
        return sum(ss * holding_cost[node]
                  for node, ss in zip(network.nodes, safety_stocks_vector))

    def service_constraint(safety_stocks_vector):
        simulated_sl = simulate_service_level(network, safety_stocks_vector)
        return simulated_sl - service_level_target

    result = minimize(objective, x0=current_safety_stocks,
                      constraints={'type': 'ineq', 'fun': service_constraint})
    return result.x

Dynamic Repositioning: When demand changes, recommendations are made for moving inventory between warehouses (lateral transshipment) instead of re-ordering from the supplier.

Visibility and tracking

Real-time Shipment Visibility:

• Maritime transport: AIS (Marine Traffic API, Vessel Finder) - real-time vessel coordinates
• Авиа: FlightAware, ADS-B Exchange
• Auto delivery: GPS trackers, APIs of transport companies (GTD, Business Lines)
• Multimodal: project44, FourKites, Shippeo — visibility platforms

ETA prediction:

def predict_shipment_eta(shipment, current_position, weather_forecast):
    """
    Учёт: текущее расстояние, исторические скорости по данному маршруту,
    прогноз погоды (шторм → замедление), загрузка портов назначения
    """
    base_transit_days = distance_km / avg_speed_kmh / 24
    weather_delay = weather_model.predict_delay(route, weather_forecast)
    port_congestion = port_model.predict_dwell_time(destination_port)
    return current_eta + weather_delay + port_congestion

Supplier Risk Management

Supplier Risk Scoring:

supplier_risk_features = {
    'financial_health': altman_z_score(financial_data),
    'delivery_performance_l12m': on_time_delivery_rate,
    'quality_score': defect_rate,
    'geographic_risk': country_risk_index[supplier_country],
    'single_sourcing_concentration': revenue_from_this_buyer / supplier_revenue,
    'tier2_exposure': assess_tier2_risks(supplier.tier2_suppliers),
    'esg_score': esg_rating_provider.get(supplier_id)
}

risk_score = xgboost_model.predict(supplier_risk_features)

Second supplier (dual-sourcing): For high-risk components, the cost of dual vs. single sourcing is quantified (premium x volume vs. expected loss due to disruption). The system provides an economic justification.

Integration

ERP/WMS/TMS:

• SAP S/4HANA, Oracle SCM: two-way API
• Events from Digital Twin → automatic actions in ERP (changes in delivery dates, additional purchase orders)
• Digital Twin Analytics → Screens in SAP Fiori

Supply Chain Control Tower: A unified interface for the team: a map with cargo tracking, risk alerts, a KPI panel, and drill-down to individual orders.

Timeframe: TMS/WMS/ERP connection, network map, basic tracking + ETA — 6-8 weeks. Multi-tier inventory optimization, Monte Carlo scenarios, supplier risk scoring, control tower UI — 5-6 months.