# ================================================================
# services/suppliers/app/services/dashboard_service.py
# ================================================================
"""
Supplier Dashboard and Analytics Service
Comprehensive supplier performance dashboards and business intelligence
"""

from datetime import datetime, timedelta, timezone
from typing import List, Optional, Dict, Any
from uuid import UUID
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select, func, and_, or_, desc, asc, text
from decimal import Decimal
import structlog

from app.models.suppliers import (
    Supplier, PurchaseOrder, Delivery, SupplierQualityReview,
    SupplierStatus, SupplierType, PurchaseOrderStatus, DeliveryStatus
)
from app.models.performance import (
    SupplierPerformanceMetric, SupplierScorecard, SupplierAlert,
    PerformanceMetricType, PerformancePeriod, AlertSeverity, AlertStatus
)
from app.schemas.performance import (
    PerformanceDashboardSummary, SupplierPerformanceInsights,
    PerformanceAnalytics, BusinessModelInsights, AlertSummary
)
from app.core.config import settings

logger = structlog.get_logger()


class DashboardService:
    """Service for supplier performance dashboards and analytics"""
    
    def __init__(self):
        self.logger = logger.bind(service="dashboard_service")
    
    async def get_performance_dashboard_summary(
        self,
        db: AsyncSession,
        tenant_id: UUID,
        date_from: Optional[datetime] = None,
        date_to: Optional[datetime] = None
    ) -> PerformanceDashboardSummary:
        """Get comprehensive performance dashboard summary"""
        try:
            # Default date range - last 30 days
            if not date_to:
                date_to = datetime.now(timezone.utc)
            if not date_from:
                date_from = date_to - timedelta(days=30)
            
            self.logger.info("Generating dashboard summary",
                           tenant_id=str(tenant_id),
                           date_from=date_from.isoformat(),
                           date_to=date_to.isoformat())
            
            # Get supplier statistics
            supplier_stats = await self._get_supplier_statistics(db, tenant_id)
            
            # Get performance statistics
            performance_stats = await self._get_performance_statistics(db, tenant_id, date_from, date_to)
            
            # Get alert statistics
            alert_stats = await self._get_alert_statistics(db, tenant_id, date_from, date_to)
            
            # Get financial statistics
            financial_stats = await self._get_financial_statistics(db, tenant_id, date_from, date_to)
            
            # Get business model insights
            business_model = await self._detect_business_model(db, tenant_id)
            
            # Calculate trends
            trends = await self._calculate_performance_trends(db, tenant_id, date_from, date_to)
            
            return PerformanceDashboardSummary(
                total_suppliers=supplier_stats['total_suppliers'],
                active_suppliers=supplier_stats['active_suppliers'],
                suppliers_above_threshold=performance_stats['above_threshold'],
                suppliers_below_threshold=performance_stats['below_threshold'],
                average_overall_score=performance_stats['avg_overall_score'],
                average_delivery_rate=performance_stats['avg_delivery_rate'],
                average_quality_rate=performance_stats['avg_quality_rate'],
                total_active_alerts=alert_stats['total_active'],
                critical_alerts=alert_stats['critical_alerts'],
                high_priority_alerts=alert_stats['high_priority'],
                recent_scorecards_generated=performance_stats['recent_scorecards'],
                cost_savings_this_month=financial_stats['cost_savings'],
                performance_trend=trends['performance_trend'],
                delivery_trend=trends['delivery_trend'],
                quality_trend=trends['quality_trend'],
                detected_business_model=business_model['model'],
                model_confidence=business_model['confidence'],
                business_model_metrics=business_model['metrics']
            )
            
        except Exception as e:
            self.logger.error("Error generating dashboard summary", error=str(e))
            raise
    
    async def get_supplier_performance_insights(
        self,
        db: AsyncSession,
        tenant_id: UUID,
        supplier_id: UUID,
        days_back: int = 30
    ) -> SupplierPerformanceInsights:
        """Get detailed performance insights for a specific supplier"""
        try:
            date_to = datetime.now(timezone.utc)
            date_from = date_to - timedelta(days=days_back)
            
            # Get supplier info
            supplier = await self._get_supplier_info(db, supplier_id, tenant_id)
            
            # Get current performance metrics
            current_metrics = await self._get_current_performance_metrics(db, supplier_id, tenant_id)
            
            # Get previous period metrics for comparison
            previous_metrics = await self._get_previous_performance_metrics(db, supplier_id, tenant_id, days_back)
            
            # Get recent activity statistics
            activity_stats = await self._get_supplier_activity_stats(db, supplier_id, tenant_id, date_from, date_to)
            
            # Get alert summary
            alert_summary = await self._get_supplier_alert_summary(db, supplier_id, tenant_id, date_from, date_to)
            
            # Calculate performance categorization
            performance_category = self._categorize_performance(current_metrics.get('overall_score', 0))
            risk_level = self._assess_risk_level(current_metrics, alert_summary)
            
            # Generate recommendations
            recommendations = await self._generate_supplier_recommendations(
                db, supplier_id, tenant_id, current_metrics, activity_stats, alert_summary
            )
            
            return SupplierPerformanceInsights(
                supplier_id=supplier_id,
                current_overall_score=current_metrics.get('overall_score', 0),
                previous_score=previous_metrics.get('overall_score'),
                score_change_percentage=self._calculate_change_percentage(
                    current_metrics.get('overall_score', 0),
                    previous_metrics.get('overall_score')
                ),
                performance_rank=current_metrics.get('rank'),
                delivery_performance=current_metrics.get('delivery_performance', 0),
                quality_performance=current_metrics.get('quality_performance', 0),
                cost_performance=current_metrics.get('cost_performance', 0),
                service_performance=current_metrics.get('service_performance', 0),
                orders_last_30_days=activity_stats['orders_count'],
                average_delivery_time=activity_stats['avg_delivery_time'],
                quality_issues_count=activity_stats['quality_issues'],
                cost_variance=activity_stats['cost_variance'],
                active_alerts=alert_summary['active_count'],
                resolved_alerts_last_30_days=alert_summary['resolved_count'],
                alert_trend=alert_summary['trend'],
                performance_category=performance_category,
                risk_level=risk_level,
                top_strengths=recommendations['strengths'],
                improvement_priorities=recommendations['improvements'],
                recommended_actions=recommendations['actions']
            )
            
        except Exception as e:
            self.logger.error("Error generating supplier insights",
                            supplier_id=str(supplier_id),
                            error=str(e))
            raise
    
    async def get_performance_analytics(
        self,
        db: AsyncSession,
        tenant_id: UUID,
        period_days: int = 90
    ) -> PerformanceAnalytics:
        """Get advanced performance analytics"""
        try:
            date_to = datetime.now(timezone.utc)
            date_from = date_to - timedelta(days=period_days)
            
            # Get performance distribution
            performance_distribution = await self._get_performance_distribution(db, tenant_id, date_from, date_to)
            
            # Get trend analysis
            trends = await self._get_detailed_trends(db, tenant_id, date_from, date_to)
            
            # Get comparative analysis
            comparative_analysis = await self._get_comparative_analysis(db, tenant_id, date_from, date_to)
            
            # Get risk analysis
            risk_analysis = await self._get_risk_analysis(db, tenant_id, date_from, date_to)
            
            # Get financial impact
            financial_impact = await self._get_financial_impact(db, tenant_id, date_from, date_to)
            
            return PerformanceAnalytics(
                period_start=date_from,
                period_end=date_to,
                total_suppliers_analyzed=performance_distribution['total_suppliers'],
                performance_distribution=performance_distribution['distribution'],
                score_ranges=performance_distribution['score_ranges'],
                overall_trend=trends['overall'],
                delivery_trends=trends['delivery'],
                quality_trends=trends['quality'],
                cost_trends=trends['cost'],
                top_performers=comparative_analysis['top_performers'],
                underperformers=comparative_analysis['underperformers'],
                most_improved=comparative_analysis['most_improved'],
                biggest_declines=comparative_analysis['biggest_declines'],
                high_risk_suppliers=risk_analysis['high_risk'],
                contract_renewals_due=risk_analysis['contract_renewals'],
                certification_expiries=risk_analysis['certification_expiries'],
                total_procurement_value=financial_impact['total_value'],
                cost_savings_achieved=financial_impact['cost_savings'],
                cost_avoidance=financial_impact['cost_avoidance'],
                financial_risk_exposure=financial_impact['risk_exposure']
            )
            
        except Exception as e:
            self.logger.error("Error generating performance analytics", error=str(e))
            raise
    
    async def get_business_model_insights(
        self,
        db: AsyncSession,
        tenant_id: UUID
    ) -> BusinessModelInsights:
        """Get business model detection and insights"""
        try:
            # Analyze supplier patterns
            supplier_patterns = await self._analyze_supplier_patterns(db, tenant_id)
            
            # Detect business model
            business_model = await self._detect_business_model_detailed(db, tenant_id)
            
            # Generate optimization recommendations
            optimization = await self._generate_optimization_recommendations(db, tenant_id, business_model)
            
            # Get benchmarking data
            benchmarking = await self._get_benchmarking_data(db, tenant_id, business_model['model'])
            
            return BusinessModelInsights(
                detected_model=business_model['model'],
                confidence_score=business_model['confidence'],
                model_characteristics=business_model['characteristics'],
                supplier_diversity_score=supplier_patterns['diversity_score'],
                procurement_volume_patterns=supplier_patterns['volume_patterns'],
                delivery_frequency_patterns=supplier_patterns['delivery_patterns'],
                order_size_patterns=supplier_patterns['order_size_patterns'],
                optimization_opportunities=optimization['opportunities'],
                recommended_supplier_mix=optimization['supplier_mix'],
                cost_optimization_potential=optimization['cost_potential'],
                risk_mitigation_suggestions=optimization['risk_mitigation'],
                industry_comparison=benchmarking['industry'],
                peer_comparison=benchmarking.get('peer')
            )
            
        except Exception as e:
            self.logger.error("Error generating business model insights", error=str(e))
            raise
    
    async def get_alert_summary(
        self,
        db: AsyncSession,
        tenant_id: UUID,
        date_from: Optional[datetime] = None,
        date_to: Optional[datetime] = None
    ) -> List[AlertSummary]:
        """Get alert summary by type and severity"""
        try:
            if not date_to:
                date_to = datetime.now(timezone.utc)
            if not date_from:
                date_from = date_to - timedelta(days=30)
            
            query = select(
                SupplierAlert.alert_type,
                SupplierAlert.severity,
                func.count(SupplierAlert.id).label('count'),
                func.avg(
                    func.extract('epoch', SupplierAlert.resolved_at - SupplierAlert.triggered_at) / 3600
                ).label('avg_resolution_hours'),
                func.max(
                    func.extract('epoch', func.current_timestamp() - SupplierAlert.triggered_at) / 3600
                ).label('oldest_age_hours')
            ).where(
                and_(
                    SupplierAlert.tenant_id == tenant_id,
                    SupplierAlert.triggered_at >= date_from,
                    SupplierAlert.triggered_at <= date_to
                )
            ).group_by(SupplierAlert.alert_type, SupplierAlert.severity)
            
            result = await db.execute(query)
            rows = result.all()
            
            alert_summaries = []
            for row in rows:
                alert_summaries.append(AlertSummary(
                    alert_type=row.alert_type,
                    severity=row.severity,
                    count=row.count,
                    avg_resolution_time_hours=row.avg_resolution_hours,
                    oldest_alert_age_hours=row.oldest_age_hours
                ))
            
            return alert_summaries
            
        except Exception as e:
            self.logger.error("Error getting alert summary", error=str(e))
            raise
    
    # === Private Helper Methods ===
    
    async def _get_supplier_statistics(self, db: AsyncSession, tenant_id: UUID) -> Dict[str, int]:
        """Get basic supplier statistics"""
        query = select(
            func.count(Supplier.id).label('total_suppliers'),
            func.count(Supplier.id.filter(Supplier.status == SupplierStatus.ACTIVE)).label('active_suppliers')
        ).where(Supplier.tenant_id == tenant_id)
        
        result = await db.execute(query)
        row = result.first()
        
        return {
            'total_suppliers': row.total_suppliers or 0,
            'active_suppliers': row.active_suppliers or 0
        }
    
    async def _get_performance_statistics(
        self, 
        db: AsyncSession, 
        tenant_id: UUID, 
        date_from: datetime, 
        date_to: datetime
    ) -> Dict[str, Any]:
        """Get performance statistics"""
        # Get recent performance metrics
        query = select(
            func.avg(SupplierPerformanceMetric.metric_value).label('avg_score'),
            func.count(
                SupplierPerformanceMetric.id.filter(
                    SupplierPerformanceMetric.metric_value >= settings.GOOD_DELIVERY_RATE
                )
            ).label('above_threshold'),
            func.count(
                SupplierPerformanceMetric.id.filter(
                    SupplierPerformanceMetric.metric_value < settings.GOOD_DELIVERY_RATE
                )
            ).label('below_threshold')
        ).where(
            and_(
                SupplierPerformanceMetric.tenant_id == tenant_id,
                SupplierPerformanceMetric.calculated_at >= date_from,
                SupplierPerformanceMetric.calculated_at <= date_to,
                SupplierPerformanceMetric.metric_type == PerformanceMetricType.DELIVERY_PERFORMANCE
            )
        )
        
        result = await db.execute(query)
        row = result.first()
        
        # Get quality statistics
        quality_query = select(
            func.avg(SupplierPerformanceMetric.metric_value).label('avg_quality')
        ).where(
            and_(
                SupplierPerformanceMetric.tenant_id == tenant_id,
                SupplierPerformanceMetric.calculated_at >= date_from,
                SupplierPerformanceMetric.calculated_at <= date_to,
                SupplierPerformanceMetric.metric_type == PerformanceMetricType.QUALITY_SCORE
            )
        )
        
        quality_result = await db.execute(quality_query)
        quality_row = quality_result.first()
        
        # Get scorecard count
        scorecard_query = select(func.count(SupplierScorecard.id)).where(
            and_(
                SupplierScorecard.tenant_id == tenant_id,
                SupplierScorecard.generated_at >= date_from,
                SupplierScorecard.generated_at <= date_to
            )
        )
        
        scorecard_result = await db.execute(scorecard_query)
        scorecard_count = scorecard_result.scalar() or 0
        
        return {
            'avg_overall_score': row.avg_score or 0,
            'above_threshold': row.above_threshold or 0,
            'below_threshold': row.below_threshold or 0,
            'avg_delivery_rate': row.avg_score or 0,
            'avg_quality_rate': quality_row.avg_quality or 0,
            'recent_scorecards': scorecard_count
        }
    
    async def _get_alert_statistics(
        self, 
        db: AsyncSession, 
        tenant_id: UUID, 
        date_from: datetime, 
        date_to: datetime
    ) -> Dict[str, int]:
        """Get alert statistics"""
        query = select(
            func.count(SupplierAlert.id.filter(SupplierAlert.status == AlertStatus.ACTIVE)).label('total_active'),
            func.count(SupplierAlert.id.filter(SupplierAlert.severity == AlertSeverity.CRITICAL)).label('critical'),
            func.count(SupplierAlert.id.filter(SupplierAlert.priority_score >= 70)).label('high_priority')
        ).where(
            and_(
                SupplierAlert.tenant_id == tenant_id,
                SupplierAlert.triggered_at >= date_from,
                SupplierAlert.triggered_at <= date_to
            )
        )
        
        result = await db.execute(query)
        row = result.first()
        
        return {
            'total_active': row.total_active or 0,
            'critical_alerts': row.critical or 0,
            'high_priority': row.high_priority or 0
        }
    
    async def _get_financial_statistics(
        self,
        db: AsyncSession,
        tenant_id: UUID,
        date_from: datetime,
        date_to: datetime
    ) -> Dict[str, Decimal]:
        """Get financial statistics"""
        # Calculate potential cost savings based on supplier performance
        # Cost savings estimated from quality issues avoided, on-time deliveries, etc.

        # Get purchase orders in period
        query = select(
            func.sum(PurchaseOrder.total_amount).label('total_spent')
        ).where(
            and_(
                PurchaseOrder.tenant_id == tenant_id,
                PurchaseOrder.created_at >= date_from,
                PurchaseOrder.created_at <= date_to,
                PurchaseOrder.status.in_([
                    PurchaseOrderStatus.RECEIVED,
                    PurchaseOrderStatus.PARTIALLY_RECEIVED,
                    PurchaseOrderStatus.COMPLETED
                ])
            )
        )

        result = await db.execute(query)
        row = result.first()
        total_spent = row.total_spent or Decimal('0')

        # Estimate cost savings as 2-5% of total spent based on:
        # - Better supplier selection
        # - Reduced waste from quality issues
        # - Better pricing through supplier comparison
        estimated_savings_percentage = Decimal('0.03')  # 3% conservative estimate
        cost_savings = total_spent * estimated_savings_percentage

        return {
            'cost_savings': cost_savings
        }
    
    async def _detect_business_model(self, db: AsyncSession, tenant_id: UUID) -> Dict[str, Any]:
        """Detect business model based on supplier patterns"""
        # Get supplier count by category
        query = select(
            func.count(Supplier.id).label('total_suppliers'),
            func.count(Supplier.id.filter(Supplier.supplier_type == SupplierType.INGREDIENTS)).label('ingredient_suppliers')
        ).where(
            and_(
                Supplier.tenant_id == tenant_id,
                Supplier.status == SupplierStatus.ACTIVE
            )
        )
        
        result = await db.execute(query)
        row = result.first()
        
        total_suppliers = row.total_suppliers or 0
        ingredient_suppliers = row.ingredient_suppliers or 0
        
        # Simple business model detection logic
        if total_suppliers >= settings.CENTRAL_BAKERY_THRESHOLD_SUPPLIERS:
            model = "central_bakery"
            confidence = 0.85
        elif total_suppliers >= settings.INDIVIDUAL_BAKERY_THRESHOLD_SUPPLIERS:
            model = "individual_bakery"
            confidence = 0.75
        else:
            model = "small_bakery"
            confidence = 0.60
        
        return {
            'model': model,
            'confidence': confidence,
            'metrics': {
                'total_suppliers': total_suppliers,
                'ingredient_suppliers': ingredient_suppliers,
                'supplier_diversity': ingredient_suppliers / max(total_suppliers, 1)
            }
        }
    
    async def _calculate_performance_trends(
        self,
        db: AsyncSession,
        tenant_id: UUID,
        date_from: datetime,
        date_to: datetime
    ) -> Dict[str, str]:
        """Calculate performance trends based on historical data"""

        # Calculate period length and compare with previous period
        period_length = (date_to - date_from).days
        previous_period_start = date_from - timedelta(days=period_length)
        previous_period_end = date_from

        # Get current period metrics
        current_query = select(
            func.avg(Supplier.delivery_rating).label('avg_delivery'),
            func.avg(Supplier.quality_rating).label('avg_quality'),
            func.count(PurchaseOrder.id).label('order_count')
        ).select_from(PurchaseOrder).join(
            Supplier, PurchaseOrder.supplier_id == Supplier.id
        ).where(
            and_(
                PurchaseOrder.tenant_id == tenant_id,
                PurchaseOrder.created_at >= date_from,
                PurchaseOrder.created_at <= date_to
            )
        )

        current_result = await db.execute(current_query)
        current = current_result.first()

        # Get previous period metrics
        previous_query = select(
            func.avg(Supplier.delivery_rating).label('avg_delivery'),
            func.avg(Supplier.quality_rating).label('avg_quality'),
            func.count(PurchaseOrder.id).label('order_count')
        ).select_from(PurchaseOrder).join(
            Supplier, PurchaseOrder.supplier_id == Supplier.id
        ).where(
            and_(
                PurchaseOrder.tenant_id == tenant_id,
                PurchaseOrder.created_at >= previous_period_start,
                PurchaseOrder.created_at < previous_period_end
            )
        )

        previous_result = await db.execute(previous_query)
        previous = previous_result.first()

        # Calculate trends
        def calculate_trend(current_value, previous_value, threshold=0.05):
            """Calculate trend direction based on percentage change"""
            if not current_value or not previous_value:
                return 'stable'
            change = (current_value - previous_value) / previous_value
            if change > threshold:
                return 'improving'
            elif change < -threshold:
                return 'declining'
            return 'stable'

        delivery_trend = calculate_trend(
            current.avg_delivery if current else None,
            previous.avg_delivery if previous else None
        )

        quality_trend = calculate_trend(
            current.avg_quality if current else None,
            previous.avg_quality if previous else None
        )

        # Overall performance based on both metrics
        if delivery_trend == 'improving' and quality_trend == 'improving':
            performance_trend = 'improving'
        elif delivery_trend == 'declining' or quality_trend == 'declining':
            performance_trend = 'declining'
        else:
            performance_trend = 'stable'

        return {
            'performance_trend': performance_trend,
            'delivery_trend': delivery_trend,
            'quality_trend': quality_trend
        }
    
    def _categorize_performance(self, score: float) -> str:
        """Categorize performance based on score"""
        if score >= settings.EXCELLENT_DELIVERY_RATE:
            return "excellent"
        elif score >= settings.GOOD_DELIVERY_RATE:
            return "good"
        elif score >= settings.ACCEPTABLE_DELIVERY_RATE:
            return "acceptable"
        elif score >= settings.POOR_DELIVERY_RATE:
            return "needs_improvement"
        else:
            return "poor"
    
    def _assess_risk_level(self, metrics: Dict[str, Any], alerts: Dict[str, Any]) -> str:
        """Assess risk level based on metrics and alerts"""
        if alerts.get('active_count', 0) > 3 or metrics.get('overall_score', 0) < 50:
            return "critical"
        elif alerts.get('active_count', 0) > 1 or metrics.get('overall_score', 0) < 70:
            return "high"
        elif alerts.get('active_count', 0) > 0 or metrics.get('overall_score', 0) < 85:
            return "medium"
        else:
            return "low"
    
    def _calculate_change_percentage(self, current: float, previous: Optional[float]) -> Optional[float]:
        """Calculate percentage change between current and previous values"""
        if previous is None or previous == 0:
            return None
        return ((current - previous) / previous) * 100
    
    # === Placeholder methods for complex analytics ===
    # These methods return placeholder data and should be implemented with actual business logic
    
    async def _get_supplier_info(self, db: AsyncSession, supplier_id: UUID, tenant_id: UUID) -> Dict[str, Any]:
        stmt = select(Supplier).where(and_(Supplier.id == supplier_id, Supplier.tenant_id == tenant_id))
        result = await db.execute(stmt)
        supplier = result.scalar_one_or_none()
        return {'name': supplier.name if supplier else 'Unknown Supplier'}
    
    async def _get_current_performance_metrics(self, db: AsyncSession, supplier_id: UUID, tenant_id: UUID) -> Dict[str, Any]:
        return {'overall_score': 75.0, 'delivery_performance': 80.0, 'quality_performance': 85.0, 'cost_performance': 70.0, 'service_performance': 75.0}
    
    async def _get_previous_performance_metrics(self, db: AsyncSession, supplier_id: UUID, tenant_id: UUID, days_back: int) -> Dict[str, Any]:
        return {'overall_score': 70.0}
    
    async def _get_supplier_activity_stats(self, db: AsyncSession, supplier_id: UUID, tenant_id: UUID, date_from: datetime, date_to: datetime) -> Dict[str, Any]:
        return {'orders_count': 15, 'avg_delivery_time': 3.2, 'quality_issues': 2, 'cost_variance': 5.5}
    
    async def _get_supplier_alert_summary(self, db: AsyncSession, supplier_id: UUID, tenant_id: UUID, date_from: datetime, date_to: datetime) -> Dict[str, Any]:
        return {'active_count': 1, 'resolved_count': 3, 'trend': 'improving'}
    
    async def _generate_supplier_recommendations(self, db: AsyncSession, supplier_id: UUID, tenant_id: UUID, metrics: Dict[str, Any], activity: Dict[str, Any], alerts: Dict[str, Any]) -> Dict[str, Any]:
        return {
            'strengths': ['Consistent quality', 'Reliable delivery'],
            'improvements': ['Cost optimization', 'Communication'],
            'actions': [{'action': 'Negotiate better pricing', 'priority': 'high'}]
        }
    
    async def _get_performance_distribution(self, db: AsyncSession, tenant_id: UUID, date_from: datetime, date_to: datetime) -> Dict[str, Any]:
        return {
            'total_suppliers': 25,
            'distribution': {'excellent': 5, 'good': 12, 'acceptable': 6, 'poor': 2},
            'score_ranges': {'excellent': [95, 100, 97.5], 'good': [80, 94, 87.0]}
        }
    
    async def _get_detailed_trends(self, db: AsyncSession, tenant_id: UUID, date_from: datetime, date_to: datetime) -> Dict[str, Any]:
        return {
            'overall': {'month_over_month': 2.5},
            'delivery': {'month_over_month': 1.8},
            'quality': {'month_over_month': 3.2},
            'cost': {'month_over_month': -1.5}
        }
    
    async def _get_comparative_analysis(self, db: AsyncSession, tenant_id: UUID, date_from: datetime, date_to: datetime) -> Dict[str, Any]:
        return {
            'top_performers': [],
            'underperformers': [],
            'most_improved': [],
            'biggest_declines': []
        }
    
    async def _get_risk_analysis(self, db: AsyncSession, tenant_id: UUID, date_from: datetime, date_to: datetime) -> Dict[str, Any]:
        return {
            'high_risk': [],
            'contract_renewals': [],
            'certification_expiries': []
        }
    
    async def _get_financial_impact(self, db: AsyncSession, tenant_id: UUID, date_from: datetime, date_to: datetime) -> Dict[str, Any]:
        return {
            'total_value': Decimal('150000'),
            'cost_savings': Decimal('5000'),
            'cost_avoidance': Decimal('2000'),
            'risk_exposure': Decimal('10000')
        }
    
    async def _analyze_supplier_patterns(self, db: AsyncSession, tenant_id: UUID) -> Dict[str, Any]:
        return {
            'diversity_score': 75.0,
            'volume_patterns': {'peak_months': ['March', 'December']},
            'delivery_patterns': {'frequency': 'weekly'},
            'order_size_patterns': {'average_size': 'medium'}
        }
    
    async def _detect_business_model_detailed(self, db: AsyncSession, tenant_id: UUID) -> Dict[str, Any]:
        return {
            'model': 'individual_bakery',
            'confidence': 0.85,
            'characteristics': {'supplier_count': 15, 'order_frequency': 'weekly'}
        }
    
    async def _generate_optimization_recommendations(self, db: AsyncSession, tenant_id: UUID, business_model: Dict[str, Any]) -> Dict[str, Any]:
        return {
            'opportunities': [{'type': 'consolidation', 'potential_savings': '10%'}],
            'supplier_mix': {'ingredients': '60%', 'packaging': '25%', 'services': '15%'},
            'cost_potential': Decimal('5000'),
            'risk_mitigation': ['Diversify supplier base', 'Implement backup suppliers']
        }
    
    async def _get_benchmarking_data(self, db: AsyncSession, tenant_id: UUID, business_model: str) -> Dict[str, Any]:
        return {
            'industry': {'delivery_rate': 88.5, 'quality_score': 91.2},
            'peer': {'delivery_rate': 86.8, 'quality_score': 89.5}
        }