La Fase 4 representa la culminación del proyecto SIGA, implementando automatización inteligente que permite al sistema tomar decisiones autónomas con alta confianza, integraciones bidireccionales con sistemas externos, y estableciendo las bases para el crecimiento y mejora continua del sistema.
| Métrica | Target | Criticidad |
|---|---|---|
| Decisiones automáticas | >30% | Crítica |
| Confianza en automatización | >95% | Crítica |
| Tiempo respuesta | <1s | Alta |
| Integraciones activas | >5 | Media |
| Reducción km vacíos total | -30% | Crítica |
| ROI proyecto | >300% | Alta |
class AutonomousDecisionEngine:
"""Motor principal para decisiones automáticas"""
def __init__(self):
self.confidence_threshold = 0.95
self.risk_manager = RiskManager()
self.decision_logger = DecisionLogger()
self.override_manager = OverrideManager()
async def process_decision_request(self,
context: DecisionContext) -> DecisionResult:
"""Procesa una solicitud de decisión"""
# Evaluar si es candidata para automatización
automation_score = await self._evaluate_automation_potential(context)
if automation_score < 0.7:
# No apta para automatización
return DecisionResult(
action='manual_required',
reason='Complejidad alta o contexto inusual',
automation_score=automation_score
)
# Generar recomendaciones
recommendations = await self._generate_recommendations(context)
# Evaluar confianza
best_recommendation = recommendations[0]
confidence = best_recommendation.confidence
if confidence >= self.confidence_threshold:
# Verificar restricciones y riesgos
risk_assessment = await self.risk_manager.assess(
best_recommendation,
context
)
if risk_assessment.is_acceptable:
# Ejecutar decisión automática
result = await self._execute_automatic_decision(
best_recommendation,
context
)
# Log completo para auditoría
await self.decision_logger.log_automatic_decision(
context=context,
recommendation=best_recommendation,
result=result,
risk_assessment=risk_assessment
)
return result
else:
# Riesgo muy alto, escalar a humano
return DecisionResult(
action='manual_review',
reason=f'Riesgo alto: {risk_assessment.main_risk}',
suggested_action=best_recommendation,
risk_assessment=risk_assessment
)
else:
# Confianza insuficiente
return DecisionResult(
action='manual_review',
reason=f'Confianza insuficiente: {confidence:.1%}',
recommendations=recommendations[:3]
)
async def _execute_automatic_decision(self,
recommendation: Recommendation,
context: DecisionContext) -> DecisionResult:
"""Ejecuta una decisión de forma automática"""
# Verificar override manual
if self.override_manager.has_override(context.vehicle_id):
return DecisionResult(
action='manual_override',
reason='Override manual activo'
)
# Preparar ejecución
execution_plan = ExecutionPlan(
vehicle_id=context.vehicle_id,
action=recommendation.action,
parameters=recommendation.parameters,
expected_outcome=recommendation.expected_outcome
)
# Ejecutar
try:
execution_result = await self._dispatch_execution(execution_plan)
# Monitorear resultado
monitoring_task = asyncio.create_task(
self._monitor_execution(execution_result.execution_id)
)
return DecisionResult(
action='automated_execution',
execution_id=execution_result.execution_id,
expected_value=recommendation.expected_value,
confidence=recommendation.confidence,
monitoring_task_id=id(monitoring_task)
)
except ExecutionException as e:
# Rollback si es necesario
await self._handle_execution_failure(e, execution_plan)
return DecisionResult(
action='execution_failed',
reason=str(e),
fallback='manual_intervention_required'
)
class ConfidenceValidationSystem:
"""Sistema para validar y mejorar confianza en decisiones"""
def __init__(self):
self.validation_history = ValidationHistory()
self.confidence_calculator = ConfidenceCalculator()
async def validate_recommendation(self,
recommendation: Recommendation,
context: DecisionContext) -> ValidationResult:
"""Valida una recomendación antes de automatizar"""
validation_checks = []
# Check 1: Consistencia histórica
historical_check = await self._check_historical_consistency(
recommendation,
context
)
validation_checks.append(historical_check)
# Check 2: Validación de constraints
constraint_check = await self._validate_constraints(
recommendation,
context
)
validation_checks.append(constraint_check)
# Check 3: Simulación rápida
simulation_check = await self._quick_simulation(
recommendation,
context
)
validation_checks.append(simulation_check)
# Check 4: Detección de anomalías
anomaly_check = await self._detect_anomalies(
recommendation,
context
)
validation_checks.append(anomaly_check)
# Calcular confianza final
final_confidence = self.confidence_calculator.calculate(
base_confidence=recommendation.confidence,
validation_checks=validation_checks
)
# Determinar si es válida para automatización
is_valid = (
final_confidence >= 0.95 and
all(check.passed for check in validation_checks)
)
result = ValidationResult(
is_valid=is_valid,
confidence=final_confidence,
checks=validation_checks,
recommendation=recommendation
)
# Guardar para aprendizaje
await self.validation_history.store(result)
return result
def _check_historical_consistency(self,
recommendation: Recommendation,
context: DecisionContext) -> ValidationCheck:
"""Verifica consistencia con decisiones históricas similares"""
# Buscar situaciones similares
similar_situations = self.validation_history.find_similar(
context=context,
limit=20,
similarity_threshold=0.85
)
if len(similar_situations) < 5:
# No hay suficiente historia
return ValidationCheck(
name='historical_consistency',
passed=True,
confidence=0.7,
message='Historia limitada, validación parcial'
)
# Analizar outcomes de situaciones similares
positive_outcomes = sum(
1 for s in similar_situations
if s.outcome.success and s.action == recommendation.action
)
consistency_ratio = positive_outcomes / len(similar_situations)
return ValidationCheck(
name='historical_consistency',
passed=consistency_ratio > 0.8,
confidence=consistency_ratio,
message=f'{positive_outcomes}/{len(similar_situations)} éxitos históricos'
)
AUTOMATION_MATRIX = {
'high_confidence_high_value': {
'confidence_range': (0.95, 1.0),
'value_range': (1000, float('inf')),
'action': 'full_automation',
'human_notification': 'post_execution',
'override_window': 0 # Sin ventana de override
},
'high_confidence_medium_value': {
'confidence_range': (0.95, 1.0),
'value_range': (500, 1000),
'action': 'full_automation',
'human_notification': 'immediate',
'override_window': 300 # 5 minutos para override
},
'high_confidence_low_value': {
'confidence_range': (0.95, 1.0),
'value_range': (0, 500),
'action': 'full_automation',
'human_notification': 'batch_summary',
'override_window': 600 # 10 minutos
},
'medium_confidence_high_value': {
'confidence_range': (0.85, 0.95),
'value_range': (1000, float('inf')),
'action': 'suggest_strongly',
'human_notification': 'immediate_approval_required',
'override_window': None # Requiere aprobación
},
'medium_confidence_medium_value': {
'confidence_range': (0.85, 0.95),
'value_range': (500, 1000),
'action': 'suggest',
'human_notification': 'normal_priority',
'override_window': None
},
'low_confidence_any_value': {
'confidence_range': (0, 0.85),
'value_range': (0, float('inf')),
'action': 'manual_only',
'human_notification': 'information_only',
'override_window': None
}
}
def determine_automation_level(confidence: float,
expected_value: float) -> AutomationDecision:
"""Determina nivel de automatización según matriz"""
for scenario, config in AUTOMATION_MATRIX.items():
if (config['confidence_range'][0] <= confidence <= config['confidence_range'][1] and
config['value_range'][0] <= expected_value <= config['value_range'][1]):
return AutomationDecision(
scenario=scenario,
action=config['action'],
notification_type=config['human_notification'],
override_window=config['override_window']
)
# Default: manual
return AutomationDecision(
scenario='default',
action='manual_only',
notification_type='information_only',
override_window=None
)
from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
app = FastAPI(title="SIGA Integration Gateway")
security = HTTPBearer()
class IntegrationGateway:
"""Gateway principal para integraciones externas"""
def __init__(self):
self.integrations = {}
self.rate_limiter = RateLimiter()
self.auth_manager = AuthManager()
def register_integration(self,
integration_id: str,
config: IntegrationConfig):
"""Registra una nueva integración"""
integration = Integration(
id=integration_id,
config=config,
status='active',
rate_limit=config.rate_limit,
auth_method=config.auth_method
)
self.integrations[integration_id] = integration
# Configurar webhooks si aplica
if config.webhooks_enabled:
self._setup_webhooks(integration)
return integration
@app.post("/api/v1/integrations/{client_id}/push-demand")
async def receive_demand_forecast(
client_id: str,
demand_data: DemandForecastData,
credentials: HTTPAuthorizationCredentials = Depends(security)
):
"""Recibe predicciones de demanda de clientes"""
# Autenticar
if not await auth_manager.validate_token(credentials.credentials, client_id):
raise HTTPException(status_code=401, detail="No autorizado")
# Rate limiting
if not rate_limiter.check_limit(client_id, 'demand_push'):
raise HTTPException(status_code=429, detail="Límite de rate excedido")
# Validar datos
validation_result = validate_demand_data(demand_data)
if not validation_result.is_valid:
raise HTTPException(status_code=400, detail=validation_result.errors)
# Procesar y fusionar con predicciones internas
try:
fusion_result = await demand_fusion_engine.merge_external_forecast(
client_id=client_id,
external_forecast=demand_data,
confidence_weight=0.3 # 30% peso a predicción externa
)
# Actualizar modelos con nueva información
await ml_pipeline.update_with_external_data(fusion_result)
# Notificar cambios relevantes
await notification_service.notify_forecast_update(
client_id=client_id,
impact_summary=fusion_result.impact_summary
)
return {
"status": "accepted",
"forecast_id": fusion_result.id,
"impact": fusion_result.impact_summary,
"next_update": fusion_result.next_expected_update
}
except Exception as e:
logger.error(f"Error procesando forecast de {client_id}: {e}")
raise HTTPException(status_code=500, detail="Error interno")
@app.get("/api/v1/integrations/{client_id}/fleet-status")
async def provide_fleet_status(
client_id: str,
zone_filter: Optional[List[str]] = None,
credentials: HTTPAuthorizationCredentials = Depends(security)
):
"""Proporciona estado de flota a clientes integrados"""
# Autenticar y autorizar
auth_result = await auth_manager.validate_and_authorize(
token=credentials.credentials,
client_id=client_id,
required_scope='fleet_status_read'
)
if not auth_result.authorized:
raise HTTPException(status_code=403, detail="No autorizado para este recurso")
# Obtener estado filtrado según permisos del cliente
fleet_status = await fleet_manager.get_status_for_client(
client_id=client_id,
zone_filter=zone_filter,
include_sensitive=auth_result.has_sensitive_access
)
# Formatear respuesta según preferencias del cliente
integration_config = integrations[client_id].config
if integration_config.response_format == 'simplified':
response = format_simplified_fleet_status(fleet_status)
else:
response = fleet_status.to_dict()
return response
class ClientConnector(ABC):
"""Clase base para conectores específicos de clientes"""
@abstractmethod
async def push_updates(self, updates: List[Update]):
"""Envía actualizaciones al sistema del cliente"""
pass
@abstractmethod
async def pull_data(self) -> ClientData:
"""Obtiene datos del sistema del cliente"""
pass
class LIDLConnector(ClientConnector):
"""Conector específico para LIDL"""
def __init__(self, config: LIDLConfig):
self.config = config
self.sap_client = SAPClient(config.sap_credentials)
self.edi_handler = EDIHandler(config.edi_settings)
async def push_updates(self, updates: List[Update]):
"""Envía actualizaciones a sistemas LIDL"""
# Formatear para SAP
sap_updates = []
for update in updates:
sap_update = {
'VEHICLE_ID': update.vehicle_id,
'STATUS': self._map_status_to_sap(update.status),
'ETA': update.eta.isoformat(),
'LOCATION': {
'LAT': update.position.lat,
'LON': update.position.lng
}
}
sap_updates.append(sap_update)
# Enviar a SAP
try:
response = await self.sap_client.update_shipment_status(sap_updates)
if response.status != 'OK':
logger.error(f"Error actualizando SAP: {response.error}")
raise IntegrationError("SAP update failed")
except Exception as e:
logger.error(f"Error en conector LIDL: {e}")
raise
async def pull_data(self) -> ClientData:
"""Obtiene órdenes y forecasts de LIDL"""
# Obtener órdenes pendientes via EDI
edi_orders = await self.edi_handler.fetch_pending_orders()
# Obtener forecast de SAP
sap_forecast = await self.sap_client.get_demand_forecast(
horizon_days=7
)
# Combinar y normalizar
client_data = ClientData(
orders=[self._normalize_order(o) for o in edi_orders],
forecast=self._normalize_forecast(sap_forecast),
metadata={
'source': 'LIDL',
'timestamp': datetime.now(),
'data_quality_score': self._assess_data_quality(edi_orders, sap_forecast)
}
)
return client_data
class WebhookManager:
"""Gestiona webhooks para notificaciones push"""
def __init__(self):
self.webhooks = {}
self.retry_policy = RetryPolicy(max_attempts=3, backoff_factor=2)
async def register_webhook(self,
client_id: str,
webhook_config: WebhookConfig) -> WebhookRegistration:
"""Registra un nuevo webhook"""
# Validar URL
if not await self._validate_webhook_url(webhook_config.url):
raise ValueError("URL de webhook inválida o inaccesible")
# Generar secreto para firma
webhook_secret = secrets.token_urlsafe(32)
registration = WebhookRegistration(
id=str(uuid.uuid4()),
client_id=client_id,
url=webhook_config.url,
events=webhook_config.events,
secret=webhook_secret,
status='active',
created_at=datetime.now()
)
self.webhooks[registration.id] = registration
# Test inicial
await self._send_test_event(registration)
return registration
async def trigger_event(self, event: WebhookEvent):
"""Dispara un evento a todos los webhooks suscritos"""
# Encontrar webhooks relevantes
relevant_webhooks = [
w for w in self.webhooks.values()
if event.type in w.events and w.status == 'active'
]
# Enviar en paralelo
tasks = []
for webhook in relevant_webhooks:
task = self._send_webhook_event(webhook, event)
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
# Procesar resultados
success_count = sum(1 for r in results if not isinstance(r, Exception))
logger.info(
f"Evento {event.type} enviado a {success_count}/{len(relevant_webhooks)} webhooks"
)
async def _send_webhook_event(self,
webhook: WebhookRegistration,
event: WebhookEvent) -> WebhookResult:
"""Envía un evento específico a un webhook"""
payload = {
'event_id': str(event.id),
'event_type': event.type,
'timestamp': event.timestamp.isoformat(),
'data': event.data
}
# Firmar payload
signature = self._sign_payload(payload, webhook.secret)
headers = {
'Content-Type': 'application/json',
'X-SIGA-Signature': signature,
'X-SIGA-Event-Type': event.type
}
# Intentar envío con reintentos
for attempt in range(self.retry_policy.max_attempts):
try:
async with aiohttp.ClientSession() as session:
async with session.post(
webhook.url,
json=payload,
headers=headers,
timeout=aiohttp.ClientTimeout(total=30)
) as response:
if response.status == 200:
return WebhookResult(
webhook_id=webhook.id,
event_id=event.id,
status='success',
attempts=attempt + 1
)
elif response.status == 410:
# Webhook ya no existe
await self._deactivate_webhook(webhook.id)
return WebhookResult(
webhook_id=webhook.id,
event_id=event.id,
status='deactivated',
attempts=attempt + 1
)
else:
# Error temporal, reintentar
if attempt < self.retry_policy.max_attempts - 1:
await asyncio.sleep(
self.retry_policy.backoff_factor ** attempt
)
continue
except Exception as e:
logger.error(f"Error enviando webhook: {e}")
if attempt < self.retry_policy.max_attempts - 1:
await asyncio.sleep(self.retry_policy.backoff_factor ** attempt)
continue
# Fallaron todos los intentos
return WebhookResult(
webhook_id=webhook.id,
event_id=event.id,
status='failed',
attempts=self.retry_policy.max_attempts,
error='Max retries exceeded'
)
class SelfImprovingMLSystem:
"""Sistema de ML que mejora automáticamente"""
def __init__(self):
self.model_registry = ModelRegistry()
self.experiment_tracker = ExperimentTracker()
self.auto_tuner = AutoHyperparameterTuner()
self.performance_monitor = PerformanceMonitor()
async def continuous_improvement_loop(self):
"""Loop principal de mejora continua"""
while True:
try:
# Evaluar performance actual
current_metrics = await self.performance_monitor.get_current_metrics()
# Determinar si necesita mejora
if self._needs_improvement(current_metrics):
# Ejecutar experimento de mejora
experiment = await self._design_experiment(current_metrics)
# Entrenar nuevos modelos
new_models = await self._train_experimental_models(experiment)
# Evaluar en shadow mode
shadow_results = await self._shadow_evaluation(
new_models,
duration_hours=24
)
# Decidir si promover a producción
if self._should_promote(shadow_results, current_metrics):
await self._promote_to_production(
new_models,
shadow_results
)
logger.info(
f"Modelos mejorados promovidos. "
f"Mejora: {shadow_results.improvement:.1%}"
)
# Limpiar experimentos antiguos
await self._cleanup_old_experiments()
except Exception as e:
logger.error(f"Error en loop de mejora: {e}")
# Esperar antes de siguiente ciclo
await asyncio.sleep(3600) # 1 hora
async def _design_experiment(self,
current_metrics: ModelMetrics) -> Experiment:
"""Diseña experimento de mejora basado en métricas actuales"""
experiment = Experiment()
# Identificar áreas de mejora
weak_areas = self._identify_weak_areas(current_metrics)
for area in weak_areas:
if area == 'prediction_accuracy':
# Probar nuevas features
experiment.add_variation(
'new_features',
self._generate_feature_experiments()
)
elif area == 'computational_efficiency':
# Optimizar arquitectura
experiment.add_variation(
'model_optimization',
self._generate_optimization_experiments()
)
elif area == 'edge_case_handling':
# Mejorar casos especiales
experiment.add_variation(
'edge_case_training',
self._generate_edge_case_experiments()
)
# Hyperparameter tuning
experiment.hyperparameter_space = self.auto_tuner.suggest_search_space(
current_metrics
)
return experiment
async def _shadow_evaluation(self,
new_models: List[Model],
duration_hours: int) -> ShadowResults:
"""Evalúa nuevos modelos en paralelo con producción"""
shadow_evaluator = ShadowEvaluator()
# Configurar evaluación shadow
await shadow_evaluator.setup(
production_models=self.model_registry.get_production_models(),
candidate_models=new_models
)
# Ejecutar en paralelo
start_time = datetime.now()
results = []
while (datetime.now() - start_time).total_seconds() < duration_hours * 3600:
# Obtener siguiente request
request = await self._get_next_production_request()
# Ejecutar en ambos conjuntos
prod_result = await shadow_evaluator.run_production(request)
shadow_result = await shadow_evaluator.run_shadow(request)
# Comparar
comparison = shadow_evaluator.compare_results(
prod_result,
shadow_result
)
results.append(comparison)
# Sleep para no saturar
await asyncio.sleep(0.1)
# Analizar resultados
shadow_results = ShadowResults(
comparisons=results,
improvement=self._calculate_improvement(results),
edge_case_performance=self._analyze_edge_cases(results),
computational_metrics=self._analyze_computation(results)
)
return shadow_results
class AutomaticRollbackSystem:
"""Sistema de rollback automático ante degradación"""
def __init__(self):
self.health_checker = ModelHealthChecker()
self.rollback_manager = RollbackManager()
self.alert_system = AlertSystem()
async def monitor_production_health(self):
"""Monitorea salud de modelos en producción"""
baseline_metrics = await self._establish_baseline()
degradation_threshold = 0.05 # 5% degradación
while True:
try:
# Check salud actual
current_health = await self.health_checker.check_all_models()
# Detectar degradación
for model_id, health in current_health.items():
baseline = baseline_metrics.get(model_id)
if self._is_degraded(health, baseline, degradation_threshold):
# Alerta inmediata
await self.alert_system.send_critical_alert(
f"Modelo {model_id} degradado: "
f"{health.primary_metric:.2%} vs {baseline.primary_metric:.2%}"
)
# Intentar rollback automático
rollback_result = await self.rollback_manager.rollback(
model_id=model_id,
reason='automatic_degradation_detected'
)
if rollback_result.success:
logger.info(f"Rollback exitoso para {model_id}")
# Actualizar baseline con modelo anterior
baseline_metrics[model_id] = await self._get_previous_baseline(
model_id
)
else:
# Escalar a intervención manual
await self.alert_system.escalate_to_oncall(
f"Rollback fallido para {model_id}: {rollback_result.error}"
)
# Actualizar métricas adaptativas
baseline_metrics = self._adaptive_baseline_update(
baseline_metrics,
current_health
)
except Exception as e:
logger.error(f"Error en monitoreo de salud: {e}")
await self.alert_system.send_error_alert(str(e))
await asyncio.sleep(60) # Check cada minuto
// Dashboard completo con todas las fases integradas
const MasterControlDashboard: React.FC = () => {
const { user, permissions } = useAuth();
const { systemStatus } = useSystemStatus();
return (
<DashboardLayout>
<Header>
<Title>SIGA - Control Maestro</Title>
<SystemHealthIndicator status={systemStatus} />
<AutomationToggle enabled={permissions.canToggleAutomation} />
</Header>
<TabContainer>
<Tab label="Operaciones" icon={<TruckIcon />}>
<OperationsView />
</Tab>
<Tab label="Inteligencia" icon={<BrainIcon />}>
<IntelligenceView />
</Tab>
<Tab label="Automatización" icon={<AutomationIcon />}>
<AutomationView />
</Tab>
<Tab label="Integraciones" icon={<IntegrationIcon />}>
<IntegrationsView />
</Tab>
<Tab label="Analytics" icon={<ChartIcon />}>
<AnalyticsView />
</Tab>
</TabContainer>
</DashboardLayout>
);
};
// Vista de automatización
const AutomationView: React.FC = () => {
const { automationStats } = useAutomationStats();
const { recentDecisions } = useRecentAutomatedDecisions();
return (
<div className="automation-view">
<AutomationMetrics stats={automationStats} />
<div className="grid grid-cols-2 gap-6">
<AutomatedDecisionsFeed decisions={recentDecisions} />
<AutomationConfiguration
onUpdate={handleConfigUpdate}
currentConfig={automationStats.config}
/>
</div>
<MLPerformanceMonitor />
<IntegrationStatus />
</div>
);
};
// App móvil React Native
const DriverApp: React.FC = () => {
const { currentRoute } = useCurrentRoute();
const { notifications } = useNotifications();
const { vehicleStatus } = useVehicleStatus();
return (
<SafeAreaView style={styles.container}>
<Header>
<VehicleInfo vehicle={vehicleStatus} />
<NotificationBell count={notifications.unread} />
</Header>
<MapView
style={styles.map}
initialRegion={currentRoute?.region}
>
<RouteOverlay route={currentRoute} />
<VehicleMarker position={vehicleStatus.position} />
</MapView>
<BottomSheet>
<CurrentLoadInfo load={currentRoute?.currentLoad} />
<NextActionCard
action={currentRoute?.nextAction}
isAutomated={currentRoute?.isAutomatedDecision}
/>
<QuickActions>
<Button onPress={reportIssue}>Reportar Problema</Button>
<Button onPress={requestAssistance}>Solicitar Ayuda</Button>
</QuickActions>
</BottomSheet>
</SafeAreaView>
);
};
automation_metrics = {
'decision_automation': {
'total_decisions': 15847,
'automated_decisions': 5254,
'automation_rate': '33.2%',
'automation_by_type': {
'repositioning': '42%',
'wait_decisions': '38%',
'load_assignment': '28%',
'route_optimization': '24%'
},
'confidence_distribution': {
'>95%': 5254,
'90-95%': 2847,
'85-90%': 3126,
'<85%': 4620
},
'override_rate': '2.3%',
'automation_accuracy': '97.8%'
},
'integration_metrics': {
'active_integrations': 7,
'clients_connected': ['LIDL', 'REWE', 'DHL', 'Coca-Cola', 'Amazon', 'Mercadona', 'Carrefour'],
'api_calls_daily': 24500,
'webhook_deliveries': 18900,
'data_sync_frequency': 'real-time',
'integration_uptime': '99.94%'
},
'ml_evolution': {
'model_updates': 47,
'automatic_improvements': 12,
'average_improvement': '3.2%',
'rollbacks_triggered': 2,
'edge_cases_learned': 156,
'computation_optimization': '28% faster'
},
'business_impact': {
'empty_km_reduction': '31.7%',
'margin_improvement': '€0.41/km',
'decisions_time_saved': '847 hours/month',
'customer_satisfaction': '+24%',
'roi_achieved': '337%'
}
}
┌─────────────────────────────────────────────────────────────┐
│ SIGA - IMPACTO TOTAL DEL PROYECTO │
├─────────────────────────────────────────────────────────────┤
│ │
│ Evolución KPIs Principales: │
│ │
│ KM Vacíos: 32% ──▶ 23.5% ──▶ 15.7% ──▶ 8.3% 🎯 │
│ Base Fase 1 Fase 2 Fase 3 Fase 4 │
│ │
│ Margen/km: €0.95 ──▶ €1.05 ──▶ €1.24 ──▶ €1.36 💰 │
│ │
│ Decisiones: 100% ──▶ 96% ──▶ 73% ──▶ 33% 🤖 │
│ Manuales Manual Manual Manual Manual Automáticas │
│ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Ahorro Mensual Total: €127,400 │ │
│ │ ROI del Proyecto: 337% │ │
│ │ Payback Period: 3.2 meses │ │
│ └─────────────────────────────────────────────────┘ │
│ │
│ Beneficios Adicionales: │
│ ✓ Tiempo de respuesta a clientes: -45% │
│ ✓ Satisfacción conductores: +38% │
│ ✓ Predictibilidad operaciones: +67% │
│ ✓ Reducción estrés dispatchers: -72% │
│ │
│ [Exportar Informe] [Compartir] [Siguiente Fase] │
└─────────────────────────────────────────────────────────────┘
automation_success_stories = [
{
'title': 'Gestión Automática Crisis Navidad',
'context': 'Pico demanda 23-24 diciembre 2024',
'challenge': 'Incremento 300% demanda en 48 horas',
'solution': [
'Sistema predijo pico con 5 días anticipación',
'Reposicionamiento automático preventivo',
'127 decisiones automáticas en período crítico',
'Coordinación automática con 5 clientes principales'
],
'results': {
'demanda_servida': '98.5%',
'km_vacios': '12.3%', # vs 35% año anterior
'decisiones_manuales_requeridas': 'Solo 18',
'ingreso_periodo': '€485,000 (+145% YoY)'
}
},
{
'title': 'Integración Tiempo Real con Amazon',
'context': 'Black Friday 2024',
'challenge': 'Sincronizar con sistema dinámico Amazon',
'solution': [
'API bidireccional con actualizaciones cada 30s',
'Predicciones compartidas y fusionadas',
'Priorización automática de cargas',
'Webhooks para eventos críticos'
],
'results': {
'entregas_a_tiempo': '99.7%',
'sincronizacion_perfecta': '48 horas continuas',
'valor_generado': '€127,000 en 4 días'
}
}
]
El Proyecto SIGA ha transformado completamente la operación logística:
Resultado Final: Una reducción del 74% en kilómetros vacíos (de 32% a 8.3%), un ROI del 337%, y lo más importante, un sistema que seguirá mejorando automáticamente en el futuro.
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