Real-Time Supply Chain Optimization Platform

1from typing import List, Dict
2import asyncio
3from datetime import datetime
4
5class DataIntegrationPipeline:
6    def __init__(self):
7        self.carriers = self.load_carrier_integrations()
8        self.warehouse_systems = self.load_warehouse_systems()
9        self.kafka_producer = KafkaProducer()
10        
11    async def sync_shipment_data(self) -> None:
12        """Sync data from all carrier systems"""
13        
14        tasks = []
15        for carrier in self.carriers:
16            task = self.sync_carrier(carrier)
17            tasks.append(task)
18        
19        results = await asyncio.gather(*tasks, return_exceptions=True)
20        
21        # Process results
22        for carrier, result in zip(self.carriers, results):
23            if isinstance(result, Exception):
24                logger.error(f"Failed to sync {carrier.name}: {result}")
25                continue
26            
27            # Publish to Kafka
28            for shipment in result:
29                await self.kafka_producer.send(
30                    'shipments',
31                    key=shipment['tracking_number'],
32                    value=shipment
33                )
34    
35    async def sync_carrier(self, carrier: Carrier) -> List[Dict]:
36        """Sync data from specific carrier"""
37        
38        # Authenticate
39        token = await carrier.authenticate()
40        
41        # Fetch shipments updated since last sync
42        last_sync = await self.get_last_sync_time(carrier.id)
43        shipments = await carrier.fetch_shipments(
44            since=last_sync,
45            token=token
46        )
47        
48        # Normalize data format
49        normalized = [
50            self.normalize_shipment(s, carrier)
51            for s in shipments
52        ]
53        
54        # Update last sync time
55        await self.update_last_sync_time(carrier.id, datetime.now())
56        
57        return normalized
58    
59    def normalize_shipment(self, raw: Dict, carrier: Carrier) -> Dict:
60        """Convert carrier-specific format to standard format"""
61        
62        # Each carrier has different field names
63        mapping = carrier.field_mapping
64        
65        return {
66            'tracking_number': raw[mapping['tracking_number']],
67            'origin': self.normalize_location(raw[mapping['origin']]),
68            'destination': self.normalize_location(raw[mapping['destination']]),
69            'status': self.normalize_status(raw[mapping['status']]),
70            'current_location': self.parse_location(raw.get(mapping['location'])),
71            'estimated_delivery': self.parse_datetime(raw[mapping['eta']]),
72            'carrier': carrier.name,
73            'service_level': raw.get(mapping['service'], 'STANDARD'),
74            'weight': float(raw.get(mapping['weight'], 0)),
75            'dimensions': self.parse_dimensions(raw.get(mapping['dimensions'])),
76            'last_updated': datetime.now().isoformat()
77        }
78

1from ortools.constraint_solver import routing_enums_pb2
2from ortools.constraint_solver import pywrapcp
3import numpy as np
4
5class RouteOptimizer:
6    def __init__(self):
7        self.geocoder = Geocoder()
8        self.traffic_predictor = TrafficPredictor()
9        
10    async def optimize_routes(
11        self,
12        shipments: List[Shipment],
13        vehicles: List[Vehicle],
14        constraints: RouteConstraints
15    ) -> List[Route]:
16        """Optimize delivery routes using constraint programming"""
17        
18        # Build distance matrix
19        distance_matrix = await self.build_distance_matrix(
20            shipments,
21            vehicles
22        )
23        
24        # Create routing model
25        manager = pywrapcp.RoutingIndexManager(
26            len(distance_matrix),
27            len(vehicles),
28            [v.depot_index for v in vehicles]
29        )
30        
31        routing = pywrapcp.RoutingModel(manager)
32        
33        # Define cost function
34        def distance_callback(from_index, to_index):
35            from_node = manager.IndexToNode(from_index)
36            to_node = manager.IndexToNode(to_index)
37            return distance_matrix[from_node][to_node]
38        
39        transit_callback_index = routing.RegisterTransitCallback(
40            distance_callback
41        )
42        routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
43        
44        # Add capacity constraints
45        def demand_callback(from_index):
46            from_node = manager.IndexToNode(from_index)
47            return shipments[from_node].weight
48        
49        demand_callback_index = routing.RegisterUnaryTransitCallback(
50            demand_callback
51        )
52        
53        routing.AddDimensionWithVehicleCapacity(
54            demand_callback_index,
55            0,  # null capacity slack
56            [v.capacity for v in vehicles],
57            True,  # start cumul to zero
58            'Capacity'
59        )
60        
61        # Add time window constraints
62        self.add_time_windows(routing, manager, shipments)
63        
64        # Set search parameters
65        search_parameters = pywrapcp.DefaultRoutingSearchParameters()
66        search_parameters.first_solution_strategy = (
67            routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
68        )
69        search_parameters.local_search_metaheuristic = (
70            routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
71        )
72        search_parameters.time_limit.seconds = 30
73        
74        # Solve
75        solution = routing.SolveWithParameters(search_parameters)
76        
77        if solution:
78            return self.extract_routes(routing, manager, solution, vehicles)
79        else:
80            raise OptimizationError("No solution found")
81    
82    async def build_distance_matrix(
83        self,
84        shipments: List[Shipment],
85        vehicles: List[Vehicle]
86    ) -> np.ndarray:
87        """Build distance/time matrix with traffic predictions"""
88        
89        # Get all unique locations
90        locations = set()
91        for shipment in shipments:
92            locations.add(shipment.pickup_location)
93            locations.add(shipment.delivery_location)
94        for vehicle in vehicles:
95            locations.add(vehicle.depot_location)
96        
97        locations = list(locations)
98        n = len(locations)
99        
100        # Initialize matrix
101        matrix = np.zeros((n, n))
102        
103        # Calculate distances with traffic
104        for i, origin in enumerate(locations):
105            for j, destination in enumerate(locations):
106                if i == j:
107                    continue
108                
109                # Get predicted travel time
110                travel_time = await self.traffic_predictor.predict_travel_time(
111                    origin,
112                    destination,
113                    departure_time=datetime.now()
114                )
115                
116                matrix[i][j] = travel_time
117        
118        return matrix
119

1import tensorflow as tf
2from tensorflow import keras
3import pandas as pd
4
5class DemandForecaster:
6    def __init__(self):
7        self.model = self.build_model()
8        self.feature_engineer = FeatureEngineer()
9        
10    def build_model(self) -> keras.Model:
11        """Build LSTM model for demand forecasting"""
12        
13        model = keras.Sequential([
14            keras.layers.LSTM(128, return_sequences=True, input_shape=(30, 50)),
15            keras.layers.Dropout(0.2),
16            keras.layers.LSTM(64, return_sequences=True),
17            keras.layers.Dropout(0.2),
18            keras.layers.LSTM(32),
19            keras.layers.Dense(64, activation='relu'),
20            keras.layers.Dense(1)
21        ])
22        
23        model.compile(
24            optimizer='adam',
25            loss='mse',
26            metrics=['mae', 'mape']
27        )
28        
29        return model
30    
31    async def forecast_demand(
32        self,
33        product_id: str,
34        location_id: str,
35        horizon_days: int = 30
36    ) -> pd.DataFrame:
37        """Forecast demand for product at location"""
38        
39        # Get historical data
40        history = await self.get_historical_demand(
41            product_id,
42            location_id,
43            days=365
44        )
45        
46        # Engineer features
47        features = self.feature_engineer.create_features(history)
48        
49        # Prepare sequences
50        X = self.create_sequences(features, lookback=30)
51        
52        # Generate forecast
53        predictions = []
54        current_sequence = X[-1:]
55        
56        for day in range(horizon_days):
57            # Predict next day
58            pred = self.model.predict(current_sequence, verbose=0)
59            predictions.append(pred[0, 0])
60            
61            # Update sequence
62            new_row = np.append(current_sequence[0, 1:], pred[0])
63            current_sequence = new_row.reshape(1, 30, -1)
64        
65        # Create forecast dataframe
66        forecast_dates = pd.date_range(
67            start=history.index[-1] + pd.Timedelta(days=1),
68            periods=horizon_days
69        )
70        
71        forecast = pd.DataFrame({
72            'date': forecast_dates,
73            'predicted_demand': predictions,
74            'confidence_lower': [p * 0.8 for p in predictions],
75            'confidence_upper': [p * 1.2 for p in predictions]
76        })
77        
78        return forecast
79    
80    def create_features(self, data: pd.DataFrame) -> pd.DataFrame:
81        """Create time series features"""
82        
83        data = data.copy()
84        
85        # Temporal features
86        data['day_of_week'] = data.index.dayofweek
87        data['day_of_month'] = data.index.day
88        data['month'] = data.index.month
89        data['quarter'] = data.index.quarter
90        data['is_weekend'] = data['day_of_week'].isin([5, 6]).astype(int)
91        
92        # Lag features
93        for lag in [1, 7, 14, 30]:
94            data[f'demand_lag_{lag}'] = data['demand'].shift(lag)
95        
96        # Rolling statistics
97        for window in [7, 14, 30]:
98            data[f'demand_rolling_mean_{window}'] = (
99                data['demand'].rolling(window).mean()
100            )
101            data[f'demand_rolling_std_{window}'] = (
102                data['demand'].rolling(window).std()
103            )
104        
105        # Holiday indicator
106        data['is_holiday'] = data.index.isin(self.get_holidays())
107        
108        return data.dropna()
109

1import { ApolloServer, gql } from 'apollo-server';
2import DataLoader from 'dataloader';
3
4const typeDefs = gql`
5  type Shipment {
6    id: ID!
7    trackingNumber: String!
8    origin: Location!
9    destination: Location!
10    currentLocation: Location
11    status: ShipmentStatus!
12    estimatedDelivery: DateTime!
13    actualDelivery: DateTime
14    carrier: Carrier!
15    route: Route
16    events: [TrackingEvent!]!
17  }
18  
19  type Route {
20    id: ID!
21    stops: [Stop!]!
22    totalDistance: Float!
23    estimatedDuration: Int!
24    optimizationScore: Float!
25  }
26  
27  type Location {
28    latitude: Float!
29    longitude: Float!
30    address: String!
31    city: String!
32    country: String!
33  }
34  
35  enum ShipmentStatus {
36    PENDING
37    IN_TRANSIT
38    OUT_FOR_DELIVERY
39    DELIVERED
40    DELAYED
41    EXCEPTION
42  }
43  
44  type Query {
45    shipment(trackingNumber: String!): Shipment
46    shipments(
47      status: ShipmentStatus
48      carrier: String
49      limit: Int
50      offset: Int
51    ): [Shipment!]!
52    
53    routeOptimization(
54      shipmentIds: [ID!]!
55      vehicleIds: [ID!]!
56    ): [Route!]!
57    
58    demandForecast(
59      productId: ID!
60      locationId: ID!
61      days: Int!
62    ): [DemandPrediction!]!
63  }
64  
65  type Subscription {
66    shipmentUpdated(trackingNumber: String!): Shipment!
67    routeOptimized(routeId: ID!): Route!
68  }
69`;
70
71const resolvers = {
72  Query: {
73    shipment: async (_, { trackingNumber }, { dataSources }) => {
74      return dataSources.shipmentAPI.getByTrackingNumber(trackingNumber);
75    },
76    
77    shipments: async (_, args, { dataSources }) => {
78      return dataSources.shipmentAPI.getShipments(args);
79    },
80    
81    routeOptimization: async (_, args, { dataSources }) => {
82      const optimizer = new RouteOptimizer();
83      return optimizer.optimize(args);
84    },
85    
86    demandForecast: async (_, args, { dataSources }) => {
87      const forecaster = new DemandForecaster();
88      return forecaster.forecast(args);
89    }
90  },
91  
92  Subscription: {
93    shipmentUpdated: {
94      subscribe: (_, { trackingNumber }, { pubsub }) => {
95        return pubsub.asyncIterator(`SHIPMENT_${trackingNumber}`);
96      }
97    }
98  },
99  
100  Shipment: {
101    events: async (shipment, _, { eventLoader }) => {
102      return eventLoader.load(shipment.id);
103    }
104  }
105};
106

1┌─────────────────────────────────────┐
2│  Data Sources                       │
3│  - Carrier APIs (200+)              │
4│  - IoT sensors                      │
5│  - Weather data                     │
6│  - Traffic data                     │
7└──────────────┬──────────────────────┘
8               ↓
9┌─────────────────────────────────────┐
10│  Data Pipeline (Apache Kafka)       │
11│  - Real-time ingestion              │
12│  - Stream processing                │
13│  - Event sourcing                   │
14└──────────────┬──────────────────────┘
15               ↓
16┌─────────────────────────────────────┐
17│  Analytics Engine                   │
18│  - Route optimization               │
19│  - Demand forecasting               │
20│  - Anomaly detection                │
21└──────────────┬──────────────────────┘
22               ↓
23┌─────────────────────────────────────┐
24│  API Layer (GraphQL)                │
25│  - Real-time queries                │
26│  - Subscriptions                    │
27│  - Batch operations                 │
28└──────────────┬──────────────────────┘
29               ↓
30┌─────────────────────────────────────┐
31│  Frontend (React)                   │
32│  - Control center dashboard         │
33│  - Mobile apps                      │
34│  - Customer portal                  │
35└─────────────────────────────────────┘
36