429 lines
14 KiB
Python
429 lines
14 KiB
Python
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# services/production/app/api/analytics.py
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"""
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Analytics API endpoints for Production Service
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Following standardized URL structure: /api/v1/tenants/{tenant_id}/production/analytics/{operation}
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Requires: Professional or Enterprise subscription tier
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"""
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from datetime import date, datetime, timedelta
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from typing import Optional
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from uuid import UUID
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from fastapi import APIRouter, Depends, HTTPException, Path, Query
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import structlog
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from shared.auth.decorators import get_current_user_dep
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from shared.auth.access_control import analytics_tier_required
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from app.services.production_service import ProductionService
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from app.core.config import settings
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from shared.routing import RouteBuilder
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logger = structlog.get_logger()
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# Create route builder for consistent URL structure
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route_builder = RouteBuilder('production')
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router = APIRouter(tags=["production-analytics"])
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def get_production_service() -> ProductionService:
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"""Dependency injection for production service"""
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from app.core.database import database_manager
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return ProductionService(database_manager, settings)
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# ===== ANALYTICS ENDPOINTS (Professional/Enterprise Only) =====
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@router.get(
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route_builder.build_analytics_route("equipment-efficiency"),
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response_model=dict
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)
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@analytics_tier_required
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async def get_equipment_efficiency(
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tenant_id: UUID = Path(...),
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start_date: Optional[date] = Query(None, description="Start date for analysis"),
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end_date: Optional[date] = Query(None, description="End date for analysis"),
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equipment_id: Optional[UUID] = Query(None, description="Filter by equipment"),
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current_user: dict = Depends(get_current_user_dep),
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Analyze equipment efficiency (Professional/Enterprise only)
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Metrics:
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- Overall Equipment Effectiveness (OEE)
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- Availability rate
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- Performance rate
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- Quality rate
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- Downtime analysis
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"""
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try:
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# Set default dates
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if not end_date:
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end_date = datetime.now().date()
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if not start_date:
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start_date = end_date - timedelta(days=30)
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# Use existing method: get_equipment_efficiency_analytics
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efficiency_data = await production_service.get_equipment_efficiency_analytics(tenant_id)
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logger.info("Equipment efficiency analyzed",
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tenant_id=str(tenant_id),
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equipment_id=str(equipment_id) if equipment_id else "all",
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user_id=current_user.get('user_id'))
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return efficiency_data
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except Exception as e:
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logger.error("Error analyzing equipment efficiency",
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tenant_id=str(tenant_id),
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error=str(e))
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raise HTTPException(
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status_code=500,
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detail="Failed to analyze equipment efficiency"
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)
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@router.get(
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route_builder.build_analytics_route("production-trends"),
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response_model=dict
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)
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@analytics_tier_required
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async def get_production_trends(
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tenant_id: UUID = Path(...),
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days_back: int = Query(90, ge=7, le=365, description="Days to analyze"),
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product_id: Optional[UUID] = Query(None, description="Filter by product"),
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current_user: dict = Depends(get_current_user_dep),
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Analyze production trends (Professional/Enterprise only)
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Provides:
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- Production volume trends
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- Batch completion rates
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- Cycle time analysis
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- Quality trends
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- Seasonal patterns
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"""
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try:
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# Use existing methods: get_performance_analytics + get_yield_trends_analytics
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end_date_calc = datetime.now().date()
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start_date_calc = end_date_calc - timedelta(days=days_back)
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performance = await production_service.get_performance_analytics(
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tenant_id, start_date_calc, end_date_calc
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)
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# Map days_back to period string for yield trends
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period = "weekly" if days_back <= 30 else "monthly"
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yield_trends = await production_service.get_yield_trends_analytics(tenant_id, period)
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trends = {
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"performance_metrics": performance,
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"yield_trends": yield_trends,
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"days_analyzed": days_back,
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"product_filter": str(product_id) if product_id else None
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}
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logger.info("Production trends analyzed",
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tenant_id=str(tenant_id),
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days_analyzed=days_back,
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user_id=current_user.get('user_id'))
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return trends
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except Exception as e:
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logger.error("Error analyzing production trends",
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tenant_id=str(tenant_id),
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error=str(e))
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raise HTTPException(
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status_code=500,
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detail="Failed to analyze production trends"
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)
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@router.get(
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route_builder.build_analytics_route("capacity-utilization"),
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response_model=dict
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)
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@analytics_tier_required
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async def get_capacity_utilization(
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tenant_id: UUID = Path(...),
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start_date: Optional[date] = Query(None),
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end_date: Optional[date] = Query(None),
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current_user: dict = Depends(get_current_user_dep),
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Analyze production capacity utilization (Professional/Enterprise only)
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Metrics:
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- Capacity utilization percentage
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- Bottleneck identification
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- Resource allocation efficiency
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- Optimization recommendations
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"""
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try:
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if not end_date:
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end_date = datetime.now().date()
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if not start_date:
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start_date = end_date - timedelta(days=30)
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# Use existing method: get_capacity_usage_report
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utilization = await production_service.get_capacity_usage_report(
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tenant_id, start_date, end_date
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)
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logger.info("Capacity utilization analyzed",
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tenant_id=str(tenant_id),
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user_id=current_user.get('user_id'))
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return utilization
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except Exception as e:
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logger.error("Error analyzing capacity utilization",
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tenant_id=str(tenant_id),
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error=str(e))
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raise HTTPException(
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status_code=500,
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detail="Failed to analyze capacity utilization"
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)
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@router.get(
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route_builder.build_analytics_route("quality-metrics"),
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response_model=dict
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)
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@analytics_tier_required
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async def get_quality_metrics(
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tenant_id: UUID = Path(...),
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start_date: Optional[date] = Query(None),
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end_date: Optional[date] = Query(None),
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product_id: Optional[UUID] = Query(None),
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current_user: dict = Depends(get_current_user_dep),
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Analyze quality control metrics (Professional/Enterprise only)
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Metrics:
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- First pass yield
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- Defect rates by type
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- Quality trends over time
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- Root cause analysis
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"""
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try:
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if not end_date:
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end_date = datetime.now().date()
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if not start_date:
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start_date = end_date - timedelta(days=30)
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# Use existing methods: get_quality_trends + get_top_defects_analytics
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quality_trends = await production_service.get_quality_trends(
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tenant_id, start_date, end_date
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)
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top_defects = await production_service.get_top_defects_analytics(tenant_id)
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quality_data = {
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"quality_trends": quality_trends,
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"top_defects": top_defects,
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"period": {
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"start_date": start_date.isoformat(),
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"end_date": end_date.isoformat()
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},
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"product_filter": str(product_id) if product_id else None
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}
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logger.info("Quality metrics analyzed",
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tenant_id=str(tenant_id),
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user_id=current_user.get('user_id'))
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return quality_data
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except Exception as e:
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logger.error("Error analyzing quality metrics",
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tenant_id=str(tenant_id),
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error=str(e))
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raise HTTPException(
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status_code=500,
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detail="Failed to analyze quality metrics"
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)
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@router.get(
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route_builder.build_analytics_route("waste-analysis"),
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response_model=dict
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)
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@analytics_tier_required
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async def get_production_waste_analysis(
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tenant_id: UUID = Path(...),
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start_date: Optional[date] = Query(None),
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end_date: Optional[date] = Query(None),
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current_user: dict = Depends(get_current_user_dep),
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Analyze production waste (Professional/Enterprise only)
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Provides:
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- Material waste percentages
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- Waste by category/product
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- Cost impact analysis
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- Reduction recommendations
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"""
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try:
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if not end_date:
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end_date = datetime.now().date()
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if not start_date:
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start_date = end_date - timedelta(days=30)
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# Use existing method: get_batch_statistics to calculate waste from yield data
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batch_stats = await production_service.get_batch_statistics(
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tenant_id, start_date, end_date
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)
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# Calculate waste metrics from batch statistics
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waste_analysis = {
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"batch_statistics": batch_stats,
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"waste_metrics": {
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"calculated_from": "yield_variance",
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"note": "Waste derived from planned vs actual quantity differences"
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},
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"period": {
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"start_date": start_date.isoformat(),
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"end_date": end_date.isoformat()
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}
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}
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logger.info("Production waste analyzed",
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tenant_id=str(tenant_id),
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user_id=current_user.get('user_id'))
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return waste_analysis
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except Exception as e:
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logger.error("Error analyzing production waste",
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tenant_id=str(tenant_id),
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error=str(e))
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raise HTTPException(
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status_code=500,
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detail="Failed to analyze production waste"
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)
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@router.get(
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route_builder.build_analytics_route("cost-analysis"),
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response_model=dict
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)
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@analytics_tier_required
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async def get_production_cost_analysis(
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tenant_id: UUID = Path(...),
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start_date: Optional[date] = Query(None),
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end_date: Optional[date] = Query(None),
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product_id: Optional[UUID] = Query(None),
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current_user: dict = Depends(get_current_user_dep),
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Analyze production costs (Professional/Enterprise only)
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Metrics:
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- Cost per unit
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- Direct vs indirect costs
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- Cost trends over time
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- Cost variance analysis
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- Profitability insights
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"""
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try:
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if not end_date:
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end_date = datetime.now().date()
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if not start_date:
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start_date = end_date - timedelta(days=30)
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# Use existing method: get_batch_statistics for cost-related data
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batch_stats = await production_service.get_batch_statistics(
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tenant_id, start_date, end_date
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)
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cost_analysis = {
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"batch_statistics": batch_stats,
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"cost_metrics": {
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"note": "Cost analysis requires additional cost tracking data",
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"available_metrics": ["batch_count", "production_volume", "efficiency"]
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},
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"period": {
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"start_date": start_date.isoformat(),
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"end_date": end_date.isoformat()
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},
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"product_filter": str(product_id) if product_id else None
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}
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logger.info("Production cost analyzed",
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tenant_id=str(tenant_id),
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product_id=str(product_id) if product_id else "all",
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user_id=current_user.get('user_id'))
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return cost_analysis
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except Exception as e:
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logger.error("Error analyzing production costs",
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tenant_id=str(tenant_id),
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error=str(e))
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raise HTTPException(
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status_code=500,
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detail="Failed to analyze production costs"
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)
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@router.get(
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route_builder.build_analytics_route("predictive-maintenance"),
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response_model=dict
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)
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@analytics_tier_required
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async def get_predictive_maintenance_insights(
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tenant_id: UUID = Path(...),
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equipment_id: Optional[UUID] = Query(None),
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current_user: dict = Depends(get_current_user_dep),
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Get predictive maintenance insights (Professional/Enterprise only)
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Provides:
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- Equipment failure predictions
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- Maintenance schedule recommendations
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- Parts replacement forecasts
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- Downtime risk assessment
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"""
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try:
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# Use existing method: predict_capacity_bottlenecks as proxy for maintenance insights
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days_ahead = 7 # Predict one week ahead
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bottlenecks = await production_service.predict_capacity_bottlenecks(
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tenant_id, days_ahead
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)
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maintenance_insights = {
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"capacity_bottlenecks": bottlenecks,
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"maintenance_recommendations": {
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"note": "Derived from capacity predictions and bottleneck analysis",
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"days_predicted": days_ahead
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},
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"equipment_filter": str(equipment_id) if equipment_id else None
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}
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logger.info("Predictive maintenance insights generated",
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tenant_id=str(tenant_id),
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equipment_id=str(equipment_id) if equipment_id else "all",
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user_id=current_user.get('user_id'))
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return maintenance_insights
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except Exception as e:
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logger.error("Error generating predictive maintenance insights",
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tenant_id=str(tenant_id),
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error=str(e))
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raise HTTPException(
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status_code=500,
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detail="Failed to generate predictive maintenance insights"
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)
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