¶ 🧠 Fase 3 - Inteligencia
¶ Descripción
La Fase 3 implementa la capa de inteligencia artificial del sistema SIGA, introduciendo modelos predictivos de demanda, optimización multi-día y recomendaciones automáticas basadas en machine learning.
¶ Objetivos de la Fase
graph TD
A[Modelo Predictivo] --> B[Anticipar Demanda]
C[Optimizador Multi-día] --> D[Estrategia Óptima]
E[API Recomendaciones] --> F[Decisiones Inteligentes]
G[Aprendizaje Continuo] --> H[Mejora Constante]
B --> I[Reducción 25% km vacíos]
D --> I
F --> I
H --> I
style A fill:#f9f,stroke:#333,stroke-width:2px
style C fill:#f9f,stroke:#333,stroke-width:2px
style E fill:#f9f,stroke:#333,stroke-width:2px
style G fill:#f9f,stroke:#333,stroke-width:2px
style I fill:#9f9,stroke:#333,stroke-width:2px
¶ Métricas de Éxito
| Métrica | Target | Criticidad |
|---|---|---|
| Precisión predicción | >80% | Crítica |
| Tiempo respuesta API | <3s | Alta |
| Adopción recomendaciones | >70% | Alta |
| Reducción km vacíos | -25% | Crítica |
| ROI acumulado | >200% | Alta |
¶ Sprint 1-2: Modelo Predictivo de Demanda
¶ 1. Preparación de Datos y Feature Engineering
class DemandDataPreparation:
"""Preparación de datos para modelos de demanda"""
def __init__(self):
self.feature_encoders = {}
self.scalers = {}
def prepare_training_data(self, raw_data: pd.DataFrame) -> Tuple[np.ndarray, np.ndarray]:
"""Prepara datos para entrenamiento de modelos"""
# Limpiar datos
clean_data = self._clean_data(raw_data)
# Feature engineering
features = pd.DataFrame()
# Features temporales
features['hour'] = clean_data['timestamp'].dt.hour
features['day_of_week'] = clean_data['timestamp'].dt.dayofweek
features['day_of_month'] = clean_data['timestamp'].dt.day
features['week_of_year'] = clean_data['timestamp'].dt.isocalendar().week
features['is_weekend'] = (features['day_of_week'] >= 5).astype(int)
# Encodings cíclicos para tiempo
features['hour_sin'] = np.sin(2 * np.pi * features['hour'] / 24)
features['hour_cos'] = np.cos(2 * np.pi * features['hour'] / 24)
features['dow_sin'] = np.sin(2 * np.pi * features['day_of_week'] / 7)
features['dow_cos'] = np.cos(2 * np.pi * features['day_of_week'] / 7)
# Features geográficas
zone_encoder = LabelEncoder()
features['zone_encoded'] = zone_encoder.fit_transform(clean_data['zone_id'])
self.feature_encoders['zone'] = zone_encoder
# Features históricas (ventanas móviles)
for window in [24, 168, 336]: # 1 día, 1 semana, 2 semanas
features[f'demand_mean_{window}h'] = (
clean_data.groupby('zone_id')['demand']
.rolling(window=window, min_periods=1)
.mean()
.reset_index(drop=True)
)
features[f'demand_std_{window}h'] = (
clean_data.groupby('zone_id')['demand']
.rolling(window=window, min_periods=1)
.std()
.fillna(0)
.reset_index(drop=True)
)
# Features de tendencia
features['trend_7d'] = self._calculate_trend(clean_data, days=7)
features['trend_30d'] = self._calculate_trend(clean_data, days=30)
# Features externas
features = self._add_external_features(features, clean_data)
# Escalado
scaler = StandardScaler()
features_scaled = scaler.fit_transform(features)
self.scalers['features'] = scaler
# Target
target = clean_data['demand'].values
return features_scaled, target
def _add_external_features(self, features: pd.DataFrame,
data: pd.DataFrame) -> pd.DataFrame:
"""Añade features de fuentes externas"""
# Calendario de festivos
features['is_holiday'] = data['date'].isin(HOLIDAYS).astype(int)
features['days_to_holiday'] = data['date'].apply(
lambda x: min((h - x).days for h in HOLIDAYS if h > x)
if any(h > x for h in HOLIDAYS) else 999
)
# Eventos especiales
features['special_event'] = self._encode_special_events(data)
# Indicadores económicos
features['fuel_price_index'] = self._get_fuel_price_index(data['date'])
features['economic_activity'] = self._get_economic_indicator(data['date'])
# Clima (si disponible)
if 'weather_data' in data.columns:
weather_features = self._process_weather_data(data['weather_data'])
features = pd.concat([features, weather_features], axis=1)
return features
¶ 2. Entrenamiento de Modelos Ensemble
class DemandPredictionEnsemble:
"""Ensemble de modelos para predicción de demanda"""
def __init__(self):
self.models = {
'xgboost': XGBRegressor(
n_estimators=200,
max_depth=8,
learning_rate=0.05,
subsample=0.8,
colsample_bytree=0.8,
random_state=42
),
'lightgbm': LGBMRegressor(
n_estimators=200,
max_depth=8,
learning_rate=0.05,
subsample=0.8,
random_state=42
),
'random_forest': RandomForestRegressor(
n_estimators=150,
max_depth=10,
min_samples_split=5,
random_state=42
),
'neural_net': self._build_neural_network()
}
self.model_weights = None
self.is_trained = False
def _build_neural_network(self):
"""Construye red neuronal para el ensemble"""
model = Sequential([
Dense(128, activation='relu', input_shape=(self.n_features,)),
Dropout(0.3),
Dense(64, activation='relu'),
Dropout(0.2),
Dense(32, activation='relu'),
Dense(1)
])
model.compile(
optimizer=Adam(learning_rate=0.001),
loss='huber',
metrics=['mae', 'mape']
)
return model
def train(self, X_train, y_train, X_val, y_val):
"""Entrena todos los modelos del ensemble"""
predictions_val = {}
for name, model in self.models.items():
print(f"Entrenando {name}...")
if name == 'neural_net':
# Entrenamiento especial para NN
early_stop = EarlyStopping(patience=10, restore_best_weights=True)
history = model.fit(
X_train, y_train,
validation_data=(X_val, y_val),
epochs=100,
batch_size=32,
callbacks=[early_stop],
verbose=0
)
else:
# Sklearn-compatible models
model.fit(X_train, y_train)
# Predicciones en validación
predictions_val[name] = model.predict(X_val)
# Optimizar pesos del ensemble
self.model_weights = self._optimize_weights(predictions_val, y_val)
self.is_trained = True
# Métricas finales
ensemble_pred = self.predict(X_val)
metrics = self._calculate_metrics(y_val, ensemble_pred)
return metrics
def _optimize_weights(self, predictions: Dict[str, np.ndarray],
y_true: np.ndarray) -> Dict[str, float]:
"""Optimiza pesos del ensemble usando optimización bayesiana"""
def objective(weights):
# Normalizar pesos
weights = weights / weights.sum()
# Predicción ponderada
ensemble_pred = np.zeros_like(y_true)
for i, (name, pred) in enumerate(predictions.items()):
ensemble_pred += weights[i] * pred
# Minimizar MAPE
mape = np.mean(np.abs((y_true - ensemble_pred) / y_true)) * 100
return mape
# Optimización
n_models = len(predictions)
initial_weights = np.ones(n_models) / n_models
result = minimize(
objective,
initial_weights,
method='SLSQP',
bounds=[(0, 1)] * n_models,
constraints={'type': 'eq', 'fun': lambda x: x.sum() - 1}
)
# Convertir a diccionario
optimal_weights = {}
for i, name in enumerate(predictions.keys()):
optimal_weights[name] = result.x[i]
return optimal_weights
def predict(self, X: np.ndarray, return_uncertainty: bool = True) -> Union[np.ndarray, Tuple]:
"""Realiza predicción con el ensemble"""
if not self.is_trained:
raise ValueError("El modelo no está entrenado")
predictions = {}
# Obtener predicciones de cada modelo
for name, model in self.models.items():
predictions[name] = model.predict(X)
# Predicción ensemble ponderada
ensemble_pred = np.zeros(len(X))
for name, pred in predictions.items():
ensemble_pred += self.model_weights[name] * pred
if return_uncertainty:
# Calcular incertidumbre como std de predicciones
pred_array = np.array(list(predictions.values()))
uncertainty = np.std(pred_array, axis=0)
return ensemble_pred, uncertainty
return ensemble_pred
¶ 3. Pipeline de Predicción en Producción
class DemandPredictionPipeline:
"""Pipeline completo de predicción en producción"""
def __init__(self):
self.data_prep = DemandDataPreparation()
self.ensemble = DemandPredictionEnsemble()
self.cache = RedisCache()
self.monitor = PredictionMonitor()
async def predict_demand(self, zone_id: str,
horizon_hours: int = 72) -> DemandForecast:
"""Predice demanda para una zona específica"""
# Check cache
cache_key = f"demand_forecast:{zone_id}:{horizon_hours}"
cached = await self.cache.get(cache_key)
if cached:
return DemandForecast.from_json(cached)
# Preparar features para predicción
features = await self._prepare_realtime_features(zone_id, horizon_hours)
# Predicción con ensemble
predictions, uncertainty = self.ensemble.predict(
features,
return_uncertainty=True
)
# Post-procesamiento
predictions = self._post_process_predictions(predictions, zone_id)
# Crear objeto forecast
forecast = DemandForecast(
zone_id=zone_id,
predictions=predictions,
uncertainty=uncertainty,
horizon_hours=horizon_hours,
timestamp=datetime.now(),
model_version=self.ensemble.version
)
# Guardar en cache (5 minutos)
await self.cache.set(cache_key, forecast.to_json(), ttl=300)
# Monitorear
await self.monitor.log_prediction(forecast)
return forecast
async def predict_all_zones(self, horizon_hours: int = 72) -> Dict[str, DemandForecast]:
"""Predice demanda para todas las zonas en paralelo"""
zones = await self._get_active_zones()
# Predicciones en paralelo
tasks = []
for zone_id in zones:
task = self.predict_demand(zone_id, horizon_hours)
tasks.append(task)
forecasts = await asyncio.gather(*tasks)
# Convertir a diccionario
result = {}
for forecast in forecasts:
result[forecast.zone_id] = forecast
return result
¶ Sprint 3-4: Optimizador Multi-día
¶ 1. Implementación del Optimizador
class MultiDayOptimizer:
"""Optimizador de estrategias multi-día"""
def __init__(self):
self.demand_predictor = DemandPredictionPipeline()
self.value_calculator = PositionValueCalculator()
self.constraint_manager = ConstraintManager()
async def optimize_fleet_strategy(self,
fleet_state: FleetState,
horizon_hours: int = 72) -> OptimizationResult:
"""Optimiza estrategia completa de la flota"""
start_time = time.time()
# Obtener predicciones de demanda
demand_forecasts = await self.demand_predictor.predict_all_zones(horizon_hours)
# Construir modelo de optimización
model = self._build_optimization_model(
fleet_state,
demand_forecasts,
horizon_hours
)
# Resolver
solution = await self._solve_model(model)
# Convertir solución a plan de acción
action_plan = self._extract_action_plan(solution, fleet_state)
# Evaluar calidad de la solución
quality_metrics = self._evaluate_solution_quality(
action_plan,
fleet_state,
demand_forecasts
)
# Construir resultado
result = OptimizationResult(
action_plan=action_plan,
expected_value=solution.objective_value,
quality_metrics=quality_metrics,
computation_time=time.time() - start_time,
confidence_score=self._calculate_confidence(solution)
)
return result
def _build_optimization_model(self, fleet_state: FleetState,
demand_forecasts: Dict[str, DemandForecast],
horizon_hours: int) -> OptimizationModel:
"""Construye modelo matemático de optimización"""
model = OptimizationModel()
# Variables de decisión
# x[v,l,t]: vehículo v toma carga l en tiempo t
# y[v,z,t]: vehículo v se reposiciona a zona z en tiempo t
# w[v,z,t]: vehículo v espera en zona z en tiempo t
vehicles = fleet_state.vehicles
loads = self._get_potential_loads(demand_forecasts)
zones = list(demand_forecasts.keys())
time_slots = range(0, horizon_hours, 4) # Slots de 4 horas
# Variables
x = model.add_binary_variables('assign_load',
indices=(vehicles, loads, time_slots))
y = model.add_binary_variables('reposition',
indices=(vehicles, zones, time_slots))
w = model.add_binary_variables('wait',
indices=(vehicles, zones, time_slots))
# Función objetivo: maximizar valor total
objective = 0
# Valor de cargas asignadas
for v in vehicles:
for l in loads:
for t in time_slots:
load_value = self._calculate_load_value(l, t)
objective += x[v,l,t] * load_value
# Costo de reposicionamiento
for v in vehicles:
for z in zones:
for t in time_slots:
repo_cost = self._calculate_repositioning_cost(v, z, t)
objective -= y[v,z,t] * repo_cost
# Valor terminal de posiciones
for v in vehicles:
for z in zones:
terminal_value = self._calculate_terminal_value(z, horizon_hours)
objective += (y[v,z,-1] + w[v,z,-1]) * terminal_value
model.set_objective(objective, sense='maximize')
# Restricciones
self._add_constraints(model, x, y, w, vehicles, loads, zones, time_slots)
return model
¶ 2. Estrategias de Solución
class OptimizationSolver:
"""Solucionador para el modelo de optimización"""
def __init__(self):
self.solver_config = {
'time_limit': 30, # segundos
'mip_gap': 0.05, # 5% gap
'threads': 8,
'emphasis': 'optimality'
}
async def solve_model(self, model: OptimizationModel) -> Solution:
"""Resuelve el modelo de optimización"""
# Intentar solución exacta primero
exact_solution = await self._try_exact_solution(model)
if exact_solution.status == 'optimal':
return exact_solution
# Si no es posible, usar heurísticas
heuristic_solution = await self._apply_heuristics(model)
# Mejorar con búsqueda local
improved_solution = await self._local_search(heuristic_solution, model)
return improved_solution
async def _try_exact_solution(self, model: OptimizationModel) -> Solution:
"""Intenta resolver exactamente con solver MIP"""
# Configurar solver
solver = pywraplp.Solver('fleet_optimization',
pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
# Transferir modelo
pyomo_model = model.to_pyomo()
# Resolver
solver.set_time_limit(self.solver_config['time_limit'] * 1000)
result = solver.Solve()
if result == pywraplp.Solver.OPTIMAL:
return self._extract_solution(solver, model, 'optimal')
elif result == pywraplp.Solver.FEASIBLE:
return self._extract_solution(solver, model, 'feasible')
else:
return Solution(status='infeasible')
async def _apply_heuristics(self, model: OptimizationModel) -> Solution:
"""Aplica heurísticas para encontrar solución rápida"""
solution = Solution()
# Heurística 1: Asignación voraz por valor
assignments = await self._greedy_assignment(model)
# Heurística 2: Reposicionamiento por demanda futura
repositions = await self._demand_based_repositioning(model, assignments)
# Combinar
solution.assignments = assignments
solution.repositions = repositions
solution.objective_value = self._evaluate_solution(
assignments + repositions, model
)
return solution
¶ 3. API de Recomendaciones
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
app = FastAPI(title="SIGA Recommendations API")
class RecommendationRequest(BaseModel):
vehicle_id: str
current_position: Dict[str, float]
context: Optional[Dict] = None
class RecommendationResponse(BaseModel):
recommendations: List[Recommendation]
confidence: float
reasoning: str
computation_time: float
@app.post("/api/v1/recommendations", response_model=RecommendationResponse)
async def get_recommendations(request: RecommendationRequest):
"""Endpoint principal para obtener recomendaciones"""
start_time = time.time()
try:
# Validar request
vehicle = await validate_vehicle(request.vehicle_id)
# Obtener estado actual
fleet_state = await get_current_fleet_state()
# Ejecutar optimización local
optimizer = LocalOptimizer()
recommendations = await optimizer.optimize_for_vehicle(
vehicle=vehicle,
fleet_state=fleet_state,
horizon_hours=48
)
# Formatear respuesta
response = RecommendationResponse(
recommendations=recommendations[:3], # Top 3
confidence=calculate_confidence(recommendations),
reasoning=generate_reasoning(recommendations),
computation_time=time.time() - start_time
)
# Log para análisis
await log_recommendation_request(request, response)
return response
except Exception as e:
logger.error(f"Error generating recommendations: {e}")
raise HTTPException(status_code=500, detail="Error interno")
@app.get("/api/v1/recommendations/{vehicle_id}/status")
async def get_recommendation_status(vehicle_id: str):
"""Obtiene estado de recomendaciones previas"""
history = await get_recommendation_history(vehicle_id, hours=24)
stats = {
'total_recommendations': len(history),
'accepted': sum(1 for r in history if r.accepted),
'acceptance_rate': sum(1 for r in history if r.accepted) / len(history),
'avg_value_captured': np.mean([r.actual_value for r in history if r.executed]),
'last_recommendation': history[-1].timestamp if history else None
}
return stats
¶ Sprint 5-6: Sistema de Aprendizaje Continuo
¶ 1. Feedback Loop Implementation
class ContinuousLearningSystem:
"""Sistema de aprendizaje continuo para mejora de modelos"""
def __init__(self):
self.feedback_collector = FeedbackCollector()
self.model_updater = ModelUpdater()
self.performance_tracker = PerformanceTracker()
async def process_feedback_batch(self):
"""Procesa lote de feedback para actualizar modelos"""
# Recolectar feedback reciente
feedback_data = await self.feedback_collector.get_recent_feedback(
hours=24
)
if len(feedback_data) < MIN_FEEDBACK_FOR_UPDATE:
logger.info("Insuficiente feedback para actualización")
return
# Analizar desviaciones
deviations = self._analyze_prediction_deviations(feedback_data)
# Identificar patrones de error
error_patterns = self._identify_error_patterns(deviations)
# Actualizar modelos si es necesario
if self._should_update_models(error_patterns):
await self._retrain_models(feedback_data, error_patterns)
# Actualizar métricas
await self.performance_tracker.update_metrics(deviations)
def _analyze_prediction_deviations(self, feedback: List[Feedback]) -> List[Deviation]:
"""Analiza desviaciones entre predicción y realidad"""
deviations = []
for fb in feedback:
deviation = Deviation(
prediction_id=fb.prediction_id,
predicted_value=fb.predicted_value,
actual_value=fb.actual_value,
error=fb.actual_value - fb.predicted_value,
error_percentage=abs(fb.actual_value - fb.predicted_value) / fb.actual_value,
context=fb.context
)
deviations.append(deviation)
return deviations
async def _retrain_models(self, feedback_data: List[Feedback],
error_patterns: List[ErrorPattern]):
"""Re-entrena modelos con datos actualizados"""
# Preparar datos de reentrenamiento
X_new, y_new = self._prepare_retraining_data(feedback_data)
# Estrategia de reentrenamiento según patrones de error
if any(p.type == 'systematic_bias' for p in error_patterns):
# Reentrenamiento completo
await self.model_updater.full_retrain(X_new, y_new)
else:
# Actualización incremental
await self.model_updater.incremental_update(X_new, y_new)
# Validar nuevos modelos
validation_results = await self._validate_updated_models()
# Desplegar si mejoran
if validation_results.improvement > 0.02: # 2% mejora mínima
await self._deploy_updated_models()
logger.info(f"Modelos actualizados con {validation_results.improvement*100:.1f}% mejora")
¶ 2. Monitoreo y Alertas de Performance
class ModelPerformanceMonitor:
"""Monitorea performance de modelos en producción"""
def __init__(self):
self.metrics_store = MetricsStore()
self.alert_manager = AlertManager()
self.drift_detector = DriftDetector()
async def monitor_predictions(self):
"""Monitoreo continuo de predicciones"""
while True:
try:
# Obtener métricas recientes
recent_metrics = await self.metrics_store.get_recent(minutes=15)
# Verificar drift
drift_score = self.drift_detector.calculate_drift(recent_metrics)
if drift_score > DRIFT_THRESHOLD:
await self.alert_manager.send_alert(
type='model_drift',
severity='high',
message=f'Drift detectado: score={drift_score:.2f}'
)
# Verificar degradación de performance
performance_drop = self._check_performance_degradation(recent_metrics)
if performance_drop > 0.1: # 10% degradación
await self.alert_manager.send_alert(
type='performance_degradation',
severity='critical',
message=f'Performance degradada: {performance_drop*100:.1f}%'
)
# Actualizar dashboard
await self._update_monitoring_dashboard(recent_metrics)
except Exception as e:
logger.error(f"Error en monitoreo: {e}")
await asyncio.sleep(300) # Check cada 5 minutos
¶ Integración con Sistemas Existentes
¶ 1. Actualización del Dashboard
// Nuevos componentes para Fase 3
const IntelligenceDashboard: React.FC = () => {
const { predictions, isLoading } = usePredictions();
const { recommendations } = useRecommendations();
const { modelMetrics } = useModelPerformance();
return (
<DashboardSection title="Inteligencia Predictiva">
<Grid cols={3} gap={4}>
<DemandPredictionMap
predictions={predictions}
showConfidenceBands={true}
horizon={72}
/>
<ActiveRecommendations
recommendations={recommendations}
onAccept={handleAcceptRecommendation}
onReject={handleRejectRecommendation}
/>
<ModelPerformancePanel
metrics={modelMetrics}
showTrends={true}
/>
</Grid>
<PredictiveAlerts
alerts={predictions?.alerts || []}
onDismiss={handleDismissAlert}
/>
</DashboardSection>
);
};
¶ 2. Integración con Calculadora What-If
// Calculadora mejorada con predicciones
const EnhancedWhatIfCalculator: React.FC = () => {
const [scenarios, setScenarios] = useState<Scenario[]>([]);
const { predictions } = usePredictions();
const enhanceScenarioWithPredictions = (scenario: Scenario) => {
// Añadir predicciones de demanda a cada escenario
const enhancedScenario = {
...scenario,
predictedDemand: predictions[scenario.destination],
confidenceInterval: calculateConfidenceInterval(
predictions[scenario.destination]
),
optimalTiming: findOptimalTiming(
predictions[scenario.destination],
scenario.constraints
)
};
return enhancedScenario;
};
return (
<Calculator>
<ScenarioBuilder
onAdd={(scenario) => {
const enhanced = enhanceScenarioWithPredictions(scenario);
setScenarios([...scenarios, enhanced]);
}}
/>
<PredictiveInsights scenarios={scenarios} />
<OptimizationResults
scenarios={scenarios}
showMLRecommendations={true}
/>
</Calculator>
);
};
¶ Resultados y Métricas de la Fase 3
¶ 1. Performance de Modelos
model_performance_metrics = {
'demand_prediction': {
'mape': 12.3, # %
'rmse': 42.7,
'r2_score': 0.86,
'hit_rate_95ci': 91.8, # % dentro del intervalo de confianza
'zones_covered': 52,
'training_time': '3.2 hours',
'inference_time_p95': '142ms'
},
'optimization': {
'avg_solution_quality': 0.94, # vs óptimo teórico
'computation_time_p95': '2.7s',
'feasibility_rate': 0.99,
'improvement_vs_baseline': '31.5%'
},
'recommendations_api': {
'requests_per_day': 1847,
'response_time_p95': '1.8s',
'acceptance_rate': '73.2%',
'value_captured': '€127,400/month'
},
'continuous_learning': {
'model_updates': 8,
'avg_improvement_per_update': '2.3%',
'drift_incidents': 2,
'recovery_time': '45 minutes'
}
}
¶ 2. Impacto en el Negocio
┌─────────────────────────────────────────────────────────┐
│ IMPACTO FASE 3 - INTELIGENCIA │
├─────────────────────────────────────────────────────────┤
│ │
│ KPIs de Negocio: │
│ ├─ Reducción km vacíos: 24.8% ↓ │
│ ├─ Mejora margen/km: €0.29 ↑ │
│ ├─ Decisiones optimizadas: 78% │
│ └─ ROI acumulado: 247% │
│ │
│ Adopción Usuario: │
│ ├─ Uso de recomendaciones: 73.2% │
│ ├─ Confianza en sistema: 4.5/5 │
│ └─ Reducción tiempo decisión: -65% │
│ │
│ Mejoras Operativas: │
│ ├─ Anticipación demanda: +28 horas │
│ ├─ Balance flota mejorado: Score 8.7/10 │
│ └─ Cargas premium capturadas: +45% │
│ │
│ [Ver Detalles] [Exportar Reporte] [Configurar Alertas] │
└─────────────────────────────────────────────────────────┘
¶ 3. Casos de Éxito
success_stories = [
{
'title': 'Anticipación Pico Post-Festivo',
'description': 'Sistema predijo correctamente pico de demanda del 6 de enero',
'actions_taken': [
'Reposicionamiento preventivo de 12 vehículos',
'Alertas enviadas 48h antes',
'Coordinación con clientes principales'
],
'results': {
'demanda_cubierta': '95%',
'km_vacios_evitados': 3200,
'ingreso_adicional': '€18,400'
}
},
{
'title': 'Optimización Corredor Mediterráneo',
'description': 'Rebalanceo inteligente Barcelona-Valencia-Alicante',
'actions_taken': [
'Análisis multi-día de flujos',
'Estrategia de posicionamiento escalonado',
'Ajuste dinámico según predicciones'
],
'results': {
'reduccion_km_vacios': '42%',
'mejora_tiempo_servicio': '-6 horas promedio',
'satisfaccion_cliente': '+18%'
}
}
]
¶ Lecciones Aprendidas Fase 3
¶ ✅ Éxitos
- Predicciones precisas: MAPE <15% superó expectativas
- Adopción natural: Usuarios confían en recomendaciones
- ROI demostrado: Payback en 2 meses
- Escalabilidad probada: Sistema maneja 50+ vehículos sin problemas
¶ ⚠️ Desafíos
- Calidad datos históricos: Requirió limpieza extensiva
- Explicabilidad: Usuarios querían entender el "por qué"
- Casos edge: Eventos especiales difíciles de predecir
- Latencia inicial: Optimización requería tuning
¶ 💡 Mejoras para Fase 4
- AutoML: Automatizar selección de modelos
- Explicabilidad: Dashboard de "por qué" de cada recomendación
- Integración externa: APIs con grandes clientes
- Edge computing: Procesamiento local para latencia ultra-baja
¶ Criterios Go/No-Go Cumplidos
- ✅ Modelo con accuracy >75% (86%)
- ✅ API respondiendo
segundos (1.8s p95) - ✅ ROI positivo demostrado (247%)
- ✅ Usuarios confiando recomendaciones (73.2% adopción)
- ✅ Plan de automatización aprobado
Decisión: ✅ PROCEDER A FASE 4
⬅️ Volver a Implementación | ➡️ Siguiente: Fase 4 - Automatización