"""
COO Operational Excellence Dashboard
===================================

Executive operational dashboard for Chief Operating Officers with comprehensive
operational metrics, process optimization, and business intelligence.
"""

# Copyright (c) 2025 Infinidatum
# Author: Duraimurugan Rajamanickam
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple
import json
import logging
from dataclasses import dataclass, field
from enum import Enum

# Import VizlyChart for professional visualizations
try:
    import finopsmetrics.vizlychart as vc
    from finopsmetrics.vizlychart.enterprise.charts import OperationalDashboard, ProcessAnalytics
    from finopsmetrics.vizlychart.charts.advanced import BusinessIntelligence
    from finopsmetrics.vizlychart.enterprise.themes import ExecutiveTheme
    VIZLYCHART_AVAILABLE = True
except ImportError:
    VIZLYCHART_AVAILABLE = False
    # VizlyChart is optional - dashboards work with fallback visualizations

from .iam_system import get_iam_manager, DashboardType, DataClassification

logger = logging.getLogger(__name__)


class ProcessStatus(Enum):
    """Process status levels."""
    OPTIMAL = "optimal"
    EFFICIENT = "efficient"
    NEEDS_ATTENTION = "needs_attention"
    CRITICAL = "critical"


class OperationalRisk(Enum):
    """Operational risk levels."""
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"


@dataclass
class OperationalKPI:
    """Operational KPI with targets and trends."""
    name: str
    current_value: float
    target_value: Optional[float]
    benchmark_value: Optional[float]
    unit: str
    trend_direction: str  # 'improving', 'declining', 'stable'
    status: ProcessStatus
    impact_level: str  # 'high', 'medium', 'low'
    category: str
    last_updated: datetime = field(default_factory=datetime.utcnow)


@dataclass
class BusinessProcess:
    """Business process with efficiency and performance metrics."""
    process_id: str
    name: str
    category: str
    owner: str
    status: ProcessStatus
    efficiency_score: float  # 0-100
    automation_level: float  # 0-100
    cycle_time: float  # in hours
    error_rate: float  # percentage
    cost_per_execution: float
    volume_per_day: int
    sla_compliance: float  # percentage
    last_optimization: Optional[datetime] = None
    dependencies: List[str] = field(default_factory=list)


@dataclass
class TeamPerformance:
    """Team performance metrics and analytics."""
    team_id: str
    team_name: str
    department: str
    team_size: int
    productivity_score: float
    engagement_score: float
    attrition_rate: float
    training_hours: float
    project_success_rate: float
    cost_per_fte: float
    utilization_rate: float


class COODashboard:
    """COO Operational Excellence Dashboard with comprehensive business analytics."""

    def __init__(self):
        self.iam_manager = get_iam_manager()
        self.theme = self._get_executive_theme()
        self.data_sources = self._initialize_data_sources()

    def _get_executive_theme(self) -> Dict[str, Any]:
        """Get COO executive theme configuration."""
        return {
            "name": "COO Executive Operations",
            "colors": {
                "primary": "#1e3a8a",        # Deep blue
                "secondary": "#0f766e",      # Teal
                "success": "#16a34a",        # Green
                "warning": "#d97706",        # Orange
                "danger": "#dc2626",         # Red
                "info": "#2563eb",          # Blue
                "neutral": "#6b7280",        # Gray
                "background": "#f8fafc",     # Light background
                "surface": "#ffffff",        # White
                "text": "#1f2937",          # Dark text
                "border": "#e5e7eb",        # Light border
                "accent": "#7c3aed"         # Purple
            },
            "operational_colors": {
                "optimal": "#16a34a",
                "efficient": "#65a30d",
                "needs_attention": "#d97706",
                "critical": "#dc2626",
                "automation": "#3b82f6",
                "manual": "#6b7280"
            },
            "chart_palette": [
                "#1e3a8a", "#16a34a", "#d97706", "#dc2626",
                "#7c3aed", "#0f766e", "#be185d", "#059669"
            ],
            "fonts": {
                "primary": "Inter, system-ui, sans-serif",
                "headers": "Inter, system-ui, sans-serif",
                "metrics": "JetBrains Mono, monospace"
            },
            "watermark": {
                "enabled": True,
                "text": "OpenFinOps Operational Excellence - Restricted",
                "position": "bottom_right",
                "opacity": 0.08
            }
        }

    def _initialize_data_sources(self) -> Dict[str, Any]:
        """Initialize data sources for operational analytics."""
        return {
            "erp_system": {
                "url": "https://api.company.com/erp",
                "modules": ["finance", "hr", "operations", "supply_chain"],
                "refresh_interval": 1800  # 30 minutes
            },
            "crm_system": {
                "url": "https://api.company.com/crm",
                "modules": ["sales", "customer_service", "marketing"],
                "refresh_interval": 900  # 15 minutes
            },
            "hr_systems": {
                "hris": "https://api.company.com/hris",
                "performance_management": "https://api.company.com/performance",
                "learning_management": "https://api.company.com/lms"
            },
            "project_management": {
                "jira": "https://company.atlassian.net",
                "confluence": "https://company.atlassian.net/wiki",
                "slack": "https://company.slack.com/api"
            },
            "business_intelligence": {
                "data_warehouse": "postgresql://bi-warehouse:5432/analytics",
                "metrics_store": "https://api.company.com/metrics",
                "kpi_dashboard": "https://bi.company.com/api"
            },
            "external_benchmarks": {
                "industry_data": "https://api.industry-benchmarks.com",
                "market_research": "https://api.market-data.com",
                "compliance_standards": "https://api.compliance-hub.com"
            }
        }

    def generate_dashboard(self, user_id: str, time_period: str = "current_month",
                          focus_area: str = "overview") -> Dict[str, Any]:
        """Generate comprehensive COO operational dashboard."""

        # Verify user access
        if not self.iam_manager.can_access_dashboard(user_id, DashboardType.COO_OPERATIONAL):
            raise PermissionError("User does not have access to COO dashboard")

        user_access_level = self.iam_manager.get_user_data_access_level(user_id)
        if user_access_level != DataClassification.RESTRICTED:
            raise PermissionError("Insufficient data access level for COO dashboard")

        logger.info(f"Generating COO dashboard for user {user_id}, period: {time_period}, focus: {focus_area}")

        # Generate dashboard components
        dashboard_data = {
            "metadata": self._get_dashboard_metadata(user_id, time_period, focus_area),
            "executive_summary": self._generate_executive_summary(time_period),
            "operational_kpis": self._generate_operational_kpis(time_period),
            "process_efficiency": self._generate_process_efficiency_analysis(time_period),
            "team_performance": self._generate_team_performance_analysis(time_period),
            "customer_operations": self._generate_customer_operations_metrics(time_period),
            "supply_chain": self._generate_supply_chain_analytics(time_period),
            "quality_metrics": self._generate_quality_metrics(time_period),
            "risk_assessment": self._generate_operational_risk_assessment(time_period),
            "automation_status": self._generate_automation_analytics(time_period),
            "cost_efficiency": self._generate_cost_efficiency_analysis(time_period),
            "strategic_initiatives": self._generate_strategic_initiatives_status(time_period),
            "benchmarking": self._generate_industry_benchmarking(time_period),
            "visualizations": self._generate_visualizations(time_period, focus_area)
        }

        # Add specialized focus areas
        if focus_area == "process_optimization":
            dashboard_data.update({
                "process_deep_dive": self._generate_process_deep_dive(time_period),
                "bottleneck_analysis": self._analyze_process_bottlenecks(),
                "optimization_roadmap": self._generate_optimization_roadmap()
            })
        elif focus_area == "team_excellence":
            dashboard_data.update({
                "talent_analytics": self._generate_talent_analytics(time_period),
                "performance_deep_dive": self._generate_performance_deep_dive(),
                "development_programs": self._analyze_development_programs()
            })
        elif focus_area == "customer_experience":
            dashboard_data.update({
                "customer_journey": self._analyze_customer_journey(),
                "satisfaction_analytics": self._generate_satisfaction_analytics(),
                "service_excellence": self._analyze_service_excellence()
            })

        # Log dashboard access
        self.iam_manager._log_audit_event("dashboard_access", user_id, {
            "dashboard_type": "coo_operational",
            "time_period": time_period,
            "focus_area": focus_area,
            "components_generated": list(dashboard_data.keys())
        })

        return dashboard_data

    def _get_dashboard_metadata(self, user_id: str, time_period: str, focus_area: str) -> Dict[str, Any]:
        """Get dashboard metadata and context."""
        user = self.iam_manager.users.get(user_id)

        return {
            "dashboard_id": f"coo_ops_{user_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "generated_at": datetime.utcnow().isoformat(),
            "generated_for": {
                "user_id": user_id,
                "name": user.full_name if user else "Unknown",
                "department": user.department if user else "Unknown",
                "role": "Chief Operating Officer"
            },
            "time_period": time_period,
            "focus_area": focus_area,
            "data_classification": "RESTRICTED",
            "refresh_interval": 300,  # 5 minutes
            "version": "2.1.0",
            "theme": self.theme["name"],
            "organizational_scope": {
                "departments": 12,
                "teams": 85,
                "employees": 1250,
                "processes": 450,
                "locations": 8
            },
            "last_data_refresh": datetime.utcnow().isoformat()
        }

    def _generate_executive_summary(self, time_period: str) -> Dict[str, Any]:
        """Generate executive summary with key insights and alerts."""

        # Key performance indicators
        operational_performance = {
            "overall_efficiency": 89.2,
            "process_optimization": 85.6,
            "team_productivity": 92.4,
            "customer_satisfaction": 94.1,
            "cost_efficiency": 87.8,
            "quality_score": 91.5
        }

        # Strategic highlights
        strategic_highlights = [
            {
                "type": "achievement",
                "title": "Process Automation Initiative Exceeds Target",
                "description": "Automated 65% of manual processes, exceeding 60% target by Q4",
                "impact": "Saved 1,200 hours monthly, $2.4M annual cost reduction",
                "status": "completed"
            },
            {
                "type": "opportunity",
                "title": "Customer Experience Enhancement Program",
                "description": "Customer satisfaction improved 8.5% with new service delivery model",
                "impact": "15% increase in customer retention, $3.2M revenue protection",
                "status": "ongoing"
            },
            {
                "type": "attention",
                "title": "Supply Chain Optimization Required",
                "description": "Lead times increased 12% due to supplier constraints",
                "impact": "Risk to delivery commitments, customer satisfaction",
                "status": "action_required"
            }
        ]

        # Operational alerts
        operational_alerts = [
            {
                "priority": "high",
                "category": "Process Performance",
                "message": "Order fulfillment process efficiency dropped 8% this week",
                "impact": "Customer delivery delays, potential SLA breaches",
                "recommended_action": "Immediate process review and resource reallocation",
                "owner": "Operations Manager"
            },
            {
                "priority": "medium",
                "category": "Team Performance",
                "message": "Engineering team utilization at 95% capacity",
                "impact": "Risk of burnout, potential quality issues",
                "recommended_action": "Capacity planning review, consider hiring",
                "owner": "VP Engineering"
            }
        ]

        # Financial impact summary
        financial_impact = {
            "operational_cost_savings": 8500000,  # Annual
            "efficiency_gains": 12500000,
            "quality_cost_avoidance": 3200000,
            "automation_roi": 285.5,  # Percentage
            "process_optimization_value": 15200000
        }

        return {
            "performance_summary": operational_performance,
            "strategic_highlights": strategic_highlights,
            "operational_alerts": operational_alerts,
            "financial_impact": financial_impact,
            "key_metrics": {
                "total_processes_managed": 450,
                "automated_processes": 292,
                "automation_percentage": 64.9,
                "teams_managed": 85,
                "total_employees": 1250,
                "customer_touchpoints": 12,
                "operational_locations": 8
            },
            "trend_analysis": {
                "efficiency_trend": "improving",
                "cost_trend": "decreasing",
                "satisfaction_trend": "improving",
                "quality_trend": "stable",
                "automation_trend": "accelerating"
            }
        }

    def _generate_operational_kpis(self, time_period: str) -> List[OperationalKPI]:
        """Generate comprehensive operational KPIs."""

        kpis = [
            OperationalKPI(
                name="Overall Operational Efficiency",
                current_value=89.2,
                target_value=90.0,
                benchmark_value=85.5,
                unit="percent",
                trend_direction="improving",
                status=ProcessStatus.EFFICIENT,
                impact_level="high",
                category="efficiency"
            ),
            OperationalKPI(
                name="Process Automation Rate",
                current_value=64.9,
                target_value=70.0,
                benchmark_value=45.2,
                unit="percent",
                trend_direction="improving",
                status=ProcessStatus.EFFICIENT,
                impact_level="high",
                category="automation"
            ),
            OperationalKPI(
                name="Customer Satisfaction Score",
                current_value=94.1,
                target_value=95.0,
                benchmark_value=87.8,
                unit="score",
                trend_direction="improving",
                status=ProcessStatus.OPTIMAL,
                impact_level="high",
                category="customer"
            ),
            OperationalKPI(
                name="Average Order Fulfillment Time",
                current_value=2.8,
                target_value=2.5,
                benchmark_value=3.5,
                unit="days",
                trend_direction="stable",
                status=ProcessStatus.NEEDS_ATTENTION,
                impact_level="medium",
                category="operations"
            ),
            OperationalKPI(
                name="Team Productivity Index",
                current_value=92.4,
                target_value=90.0,
                benchmark_value=82.1,
                unit="index",
                trend_direction="improving",
                status=ProcessStatus.OPTIMAL,
                impact_level="high",
                category="productivity"
            ),
            OperationalKPI(
                name="Quality Score",
                current_value=91.5,
                target_value=93.0,
                benchmark_value=88.2,
                unit="percent",
                trend_direction="stable",
                status=ProcessStatus.EFFICIENT,
                impact_level="high",
                category="quality"
            ),
            OperationalKPI(
                name="Cost per Transaction",
                current_value=12.45,
                target_value=11.50,
                benchmark_value=15.80,
                unit="USD",
                trend_direction="improving",
                status=ProcessStatus.EFFICIENT,
                impact_level="medium",
                category="cost"
            ),
            OperationalKPI(
                name="Employee Utilization Rate",
                current_value=87.8,
                target_value=85.0,
                benchmark_value=78.5,
                unit="percent",
                trend_direction="stable",
                status=ProcessStatus.NEEDS_ATTENTION,
                impact_level="medium",
                category="resources"
            ),
            OperationalKPI(
                name="First Call Resolution Rate",
                current_value=78.5,
                target_value=85.0,
                benchmark_value=72.8,
                unit="percent",
                trend_direction="improving",
                status=ProcessStatus.NEEDS_ATTENTION,
                impact_level="medium",
                category="service"
            ),
            OperationalKPI(
                name="Process Cycle Time Reduction",
                current_value=25.8,
                target_value=30.0,
                benchmark_value=18.5,
                unit="percent",
                trend_direction="improving",
                status=ProcessStatus.EFFICIENT,
                impact_level="high",
                category="improvement"
            )
        ]

        return kpis

    def _generate_process_efficiency_analysis(self, time_period: str) -> Dict[str, Any]:
        """Generate comprehensive process efficiency analysis."""

        # Key business processes
        processes = [
            BusinessProcess(
                process_id="PROC001",
                name="Order to Cash",
                category="Revenue Operations",
                owner="Sales Operations",
                status=ProcessStatus.EFFICIENT,
                efficiency_score=88.5,
                automation_level=75.2,
                cycle_time=2.8,
                error_rate=0.85,
                cost_per_execution=45.20,
                volume_per_day=1250,
                sla_compliance=94.2,
                last_optimization=datetime.utcnow() - timedelta(days=30),
                dependencies=["CRM", "ERP", "Payment Gateway"]
            ),
            BusinessProcess(
                process_id="PROC002",
                name="Customer Onboarding",
                category="Customer Operations",
                owner="Customer Success",
                status=ProcessStatus.OPTIMAL,
                efficiency_score=94.8,
                automation_level=82.5,
                cycle_time=1.5,
                error_rate=0.32,
                cost_per_execution=125.80,
                volume_per_day=85,
                sla_compliance=98.5,
                last_optimization=datetime.utcnow() - timedelta(days=15),
                dependencies=["CRM", "Identity Management", "Training Platform"]
            ),
            BusinessProcess(
                process_id="PROC003",
                name="Supply Chain Management",
                category="Supply Chain",
                owner="Supply Chain Manager",
                status=ProcessStatus.NEEDS_ATTENTION,
                efficiency_score=76.2,
                automation_level=55.8,
                cycle_time=5.2,
                error_rate=2.15,
                cost_per_execution=285.50,
                volume_per_day=450,
                sla_compliance=86.8,
                last_optimization=datetime.utcnow() - timedelta(days=90),
                dependencies=["ERP", "Supplier Portal", "Inventory Management"]
            ),
            BusinessProcess(
                process_id="PROC004",
                name="Employee Performance Review",
                category="Human Resources",
                owner="HR Operations",
                status=ProcessStatus.EFFICIENT,
                efficiency_score=89.2,
                automation_level=68.5,
                cycle_time=4.5,
                error_rate=1.25,
                cost_per_execution=185.20,
                volume_per_day=25,
                sla_compliance=92.5,
                last_optimization=datetime.utcnow() - timedelta(days=45),
                dependencies=["HRIS", "Performance Management", "Learning Management"]
            ),
            BusinessProcess(
                process_id="PROC005",
                name="IT Service Management",
                category="IT Operations",
                owner="IT Operations Manager",
                status=ProcessStatus.OPTIMAL,
                efficiency_score=95.2,
                automation_level=88.5,
                cycle_time=0.8,
                error_rate=0.15,
                cost_per_execution=28.50,
                volume_per_day=180,
                sla_compliance=97.8,
                last_optimization=datetime.utcnow() - timedelta(days=10),
                dependencies=["ITSM Tool", "Monitoring System", "Knowledge Base"]
            )
        ]

        # Process efficiency metrics
        total_processes = len(processes)
        avg_efficiency = sum(p.efficiency_score for p in processes) / total_processes
        avg_automation = sum(p.automation_level for p in processes) / total_processes
        avg_sla_compliance = sum(p.sla_compliance for p in processes) / total_processes

        # Process improvement opportunities
        improvement_opportunities = [
            {
                "process_id": "PROC003",
                "process_name": "Supply Chain Management",
                "opportunity": "Implement predictive analytics for demand forecasting",
                "potential_efficiency_gain": 18.5,
                "automation_increase": 25.0,
                "cost_reduction": 125000,
                "implementation_effort": "high",
                "timeline": "6 months"
            },
            {
                "process_id": "PROC001",
                "process_name": "Order to Cash",
                "opportunity": "Integrate AI-powered credit risk assessment",
                "potential_efficiency_gain": 12.8,
                "automation_increase": 15.0,
                "cost_reduction": 85000,
                "implementation_effort": "medium",
                "timeline": "3 months"
            },
            {
                "process_id": "PROC004",
                "process_name": "Employee Performance Review",
                "opportunity": "Automate performance data collection and analysis",
                "potential_efficiency_gain": 22.5,
                "automation_increase": 20.0,
                "cost_reduction": 45000,
                "implementation_effort": "low",
                "timeline": "2 months"
            }
        ]

        return {
            "process_summary": {
                "total_processes": total_processes,
                "average_efficiency": avg_efficiency,
                "average_automation": avg_automation,
                "average_sla_compliance": avg_sla_compliance,
                "processes_needing_attention": len([p for p in processes if p.status == ProcessStatus.NEEDS_ATTENTION]),
                "optimal_processes": len([p for p in processes if p.status == ProcessStatus.OPTIMAL])
            },
            "processes": [
                {
                    "process_id": p.process_id,
                    "name": p.name,
                    "category": p.category,
                    "owner": p.owner,
                    "status": p.status.value,
                    "efficiency_score": p.efficiency_score,
                    "automation_level": p.automation_level,
                    "cycle_time": p.cycle_time,
                    "error_rate": p.error_rate,
                    "cost_per_execution": p.cost_per_execution,
                    "volume_per_day": p.volume_per_day,
                    "sla_compliance": p.sla_compliance,
                    "last_optimization": p.last_optimization.isoformat() if p.last_optimization else None,
                    "dependencies": p.dependencies
                } for p in processes
            ],
            "improvement_opportunities": improvement_opportunities,
            "process_categories": {
                "Revenue Operations": {"count": 1, "avg_efficiency": 88.5, "automation": 75.2},
                "Customer Operations": {"count": 1, "avg_efficiency": 94.8, "automation": 82.5},
                "Supply Chain": {"count": 1, "avg_efficiency": 76.2, "automation": 55.8},
                "Human Resources": {"count": 1, "avg_efficiency": 89.2, "automation": 68.5},
                "IT Operations": {"count": 1, "avg_efficiency": 95.2, "automation": 88.5}
            },
            "automation_roadmap": [
                {
                    "quarter": "Q1",
                    "target_automation": 72.0,
                    "initiatives": ["Supply chain automation", "HR process optimization"]
                },
                {
                    "quarter": "Q2",
                    "target_automation": 78.0,
                    "initiatives": ["Order processing AI", "Customer service automation"]
                },
                {
                    "quarter": "Q3",
                    "target_automation": 82.0,
                    "initiatives": ["Financial close automation", "Compliance workflows"]
                }
            ]
        }

    def _generate_team_performance_analysis(self, time_period: str) -> Dict[str, Any]:
        """Generate comprehensive team performance analysis."""

        # Team performance data
        teams = [
            TeamPerformance(
                team_id="TEAM001",
                team_name="AI Engineering",
                department="Engineering",
                team_size=25,
                productivity_score=94.2,
                engagement_score=89.5,
                attrition_rate=5.2,
                training_hours=125.5,
                project_success_rate=92.8,
                cost_per_fte=185000,
                utilization_rate=88.5
            ),
            TeamPerformance(
                team_id="TEAM002",
                team_name="Customer Success",
                department="Customer Operations",
                team_size=18,
                productivity_score=91.8,
                engagement_score=94.2,
                attrition_rate=8.5,
                training_hours=95.2,
                project_success_rate=96.5,
                cost_per_fte=125000,
                utilization_rate=85.2
            ),
            TeamPerformance(
                team_id="TEAM003",
                team_name="Sales Operations",
                department="Sales",
                team_size=32,
                productivity_score=87.5,
                engagement_score=82.1,
                attrition_rate=12.5,
                training_hours=68.5,
                project_success_rate=89.2,
                cost_per_fte=145000,
                utilization_rate=92.8
            ),
            TeamPerformance(
                team_id="TEAM004",
                team_name="Data Science",
                department="Analytics",
                team_size=15,
                productivity_score=96.8,
                engagement_score=91.2,
                attrition_rate=6.8,
                training_hours=158.5,
                project_success_rate=94.5,
                cost_per_fte=195000,
                utilization_rate=89.5
            ),
            TeamPerformance(
                team_id="TEAM005",
                team_name="DevOps",
                department="Engineering",
                team_size=12,
                productivity_score=93.5,
                engagement_score=88.9,
                attrition_rate=4.2,
                training_hours=142.8,
                project_success_rate=97.2,
                cost_per_fte=165000,
                utilization_rate=91.2
            )
        ]

        # Calculate aggregate metrics
        total_employees = sum(team.team_size for team in teams)
        avg_productivity = sum(team.productivity_score * team.team_size for team in teams) / total_employees
        avg_engagement = sum(team.engagement_score * team.team_size for team in teams) / total_employees
        avg_attrition = sum(team.attrition_rate * team.team_size for team in teams) / total_employees

        # Performance trends
        performance_trends = {
            "productivity": {
                "current": avg_productivity,
                "previous_quarter": 89.2,
                "trend": "improving",
                "variance": avg_productivity - 89.2
            },
            "engagement": {
                "current": avg_engagement,
                "previous_quarter": 86.5,
                "trend": "improving",
                "variance": avg_engagement - 86.5
            },
            "attrition": {
                "current": avg_attrition,
                "previous_quarter": 9.8,
                "trend": "improving",
                "variance": avg_attrition - 9.8
            }
        }

        # Skills and development
        skills_analysis = {
            "skill_gaps": [
                {"skill": "AI/ML Engineering", "gap_percentage": 25.5, "priority": "high"},
                {"skill": "Cloud Architecture", "gap_percentage": 18.2, "priority": "medium"},
                {"skill": "Data Engineering", "gap_percentage": 22.8, "priority": "high"},
                {"skill": "Cybersecurity", "gap_percentage": 35.2, "priority": "critical"},
                {"skill": "Product Management", "gap_percentage": 15.8, "priority": "medium"}
            ],
            "training_programs": [
                {
                    "program": "AI/ML Certification",
                    "participants": 45,
                    "completion_rate": 82.5,
                    "cost": 125000,
                    "roi": 285.5
                },
                {
                    "program": "Leadership Development",
                    "participants": 25,
                    "completion_rate": 96.8,
                    "cost": 85000,
                    "roi": 195.2
                }
            ]
        }

        return {
            "team_summary": {
                "total_teams": len(teams),
                "total_employees": total_employees,
                "average_productivity": avg_productivity,
                "average_engagement": avg_engagement,
                "average_attrition": avg_attrition,
                "high_performing_teams": len([t for t in teams if t.productivity_score > 90]),
                "teams_needing_attention": len([t for t in teams if t.attrition_rate > 10])
            },
            "teams": [
                {
                    "team_id": team.team_id,
                    "team_name": team.team_name,
                    "department": team.department,
                    "team_size": team.team_size,
                    "productivity_score": team.productivity_score,
                    "engagement_score": team.engagement_score,
                    "attrition_rate": team.attrition_rate,
                    "training_hours": team.training_hours,
                    "project_success_rate": team.project_success_rate,
                    "cost_per_fte": team.cost_per_fte,
                    "utilization_rate": team.utilization_rate
                } for team in teams
            ],
            "performance_trends": performance_trends,
            "skills_analysis": skills_analysis,
            "department_performance": {
                "Engineering": {
                    "teams": 2,
                    "employees": 37,
                    "avg_productivity": 93.85,
                    "avg_attrition": 4.7
                },
                "Customer Operations": {
                    "teams": 1,
                    "employees": 18,
                    "avg_productivity": 91.8,
                    "avg_attrition": 8.5
                },
                "Sales": {
                    "teams": 1,
                    "employees": 32,
                    "avg_productivity": 87.5,
                    "avg_attrition": 12.5
                },
                "Analytics": {
                    "teams": 1,
                    "employees": 15,
                    "avg_productivity": 96.8,
                    "avg_attrition": 6.8
                }
            },
            "talent_pipeline": {
                "open_positions": 25,
                "time_to_fill": 45.2,  # days
                "offer_acceptance_rate": 78.5,
                "internal_mobility_rate": 15.8,
                "succession_planning_coverage": 68.5
            }
        }

    def _generate_customer_operations_metrics(self, time_period: str) -> Dict[str, Any]:
        """Generate customer operations and experience metrics."""

        # Customer satisfaction metrics
        customer_metrics = {
            "satisfaction_scores": {
                "overall_csat": 94.1,
                "nps_score": 68.5,
                "customer_effort_score": 82.3,
                "retention_rate": 94.8,
                "churn_rate": 5.2
            },
            "service_delivery": {
                "first_call_resolution": 78.5,
                "average_response_time": 2.8,  # hours
                "escalation_rate": 8.5,
                "sla_compliance": 94.2,
                "service_availability": 99.8
            },
            "customer_journey": {
                "onboarding_completion_rate": 92.5,
                "time_to_value": 12.8,  # days
                "feature_adoption_rate": 75.2,
                "support_ticket_volume": 1250,
                "self_service_usage": 68.5
            }
        }

        # Customer segments performance
        segment_performance = {
            "enterprise": {
                "csat": 96.2,
                "nps": 75.8,
                "retention": 98.5,
                "expansion_rate": 125.8,
                "support_tickets": 285
            },
            "mid_market": {
                "csat": 93.5,
                "nps": 65.2,
                "retention": 92.8,
                "expansion_rate": 108.5,
                "support_tickets": 685
            },
            "smb": {
                "csat": 91.8,
                "nps": 58.5,
                "retention": 89.2,
                "expansion_rate": 95.2,
                "support_tickets": 1850
            }
        }

        # Customer success initiatives
        success_initiatives = [
            {
                "initiative": "AI-Powered Support Chat",
                "status": "implemented",
                "impact": "35% reduction in response time",
                "roi": 245.5,
                "customer_feedback": 4.6
            },
            {
                "initiative": "Proactive Health Monitoring",
                "status": "pilot",
                "impact": "28% reduction in support tickets",
                "roi": 185.2,
                "customer_feedback": 4.8
            },
            {
                "initiative": "Self-Service Portal Enhancement",
                "status": "planning",
                "impact": "Expected 40% increase in self-service",
                "roi": 320.5,
                "customer_feedback": "N/A"
            }
        ]

        return {
            "customer_metrics": customer_metrics,
            "segment_performance": segment_performance,
            "success_initiatives": success_initiatives,
            "operational_excellence": {
                "process_efficiency": 89.2,
                "team_productivity": 91.8,
                "technology_utilization": 85.5,
                "cost_per_customer": 285.50,
                "value_delivered_per_customer": 12500
            },
            "improvement_areas": [
                {
                    "area": "First Call Resolution",
                    "current": 78.5,
                    "target": 85.0,
                    "gap": 6.5,
                    "action_plan": "Enhanced agent training and knowledge base"
                },
                {
                    "area": "Customer Effort Score",
                    "current": 82.3,
                    "target": 88.0,
                    "gap": 5.7,
                    "action_plan": "Process simplification and automation"
                }
            ],
            "customer_feedback_trends": {
                "satisfaction_trend": "improving",
                "common_complaints": ["Response time", "Process complexity", "Feature requests"],
                "praise_areas": ["Product quality", "Team expertise", "Innovation"],
                "feedback_volume": 2850,
                "response_rate": 68.5
            }
        }

    def _generate_supply_chain_analytics(self, time_period: str) -> Dict[str, Any]:
        """Generate supply chain and vendor management analytics."""

        # Supply chain metrics
        supply_chain_metrics = {
            "delivery_performance": {
                "on_time_delivery": 89.2,
                "order_accuracy": 96.8,
                "lead_time_average": 12.5,  # days
                "inventory_turnover": 8.5,
                "stockout_rate": 2.8
            },
            "vendor_performance": {
                "vendor_count": 125,
                "strategic_vendors": 15,
                "vendor_score_average": 87.5,
                "contract_compliance": 94.2,
                "cost_savings_achieved": 2500000
            },
            "procurement_efficiency": {
                "procurement_cycle_time": 8.5,  # days
                "purchase_order_accuracy": 98.2,
                "contract_renewal_rate": 89.5,
                "spend_under_management": 92.8,
                "maverick_spending": 7.2
            }
        }

        # Key vendors analysis
        key_vendors = [
            {
                "vendor_id": "VEN001",
                "vendor_name": "Cloud Infrastructure Corp",
                "category": "Technology",
                "annual_spend": 12500000,
                "performance_score": 94.2,
                "delivery_rating": 96.8,
                "quality_rating": 92.5,
                "relationship_score": 89.2,
                "contract_expiry": "2024-12-31",
                "risk_level": "low"
            },
            {
                "vendor_id": "VEN002",
                "vendor_name": "AI Platform Solutions",
                "category": "Software",
                "annual_spend": 8500000,
                "performance_score": 91.8,
                "delivery_rating": 88.5,
                "quality_rating": 95.2,
                "relationship_score": 92.8,
                "contract_expiry": "2024-06-30",
                "risk_level": "medium"
            },
            {
                "vendor_id": "VEN003",
                "vendor_name": "Professional Services Inc",
                "category": "Services",
                "annual_spend": 4200000,
                "performance_score": 86.5,
                "delivery_rating": 85.2,
                "quality_rating": 88.9,
                "relationship_score": 85.8,
                "contract_expiry": "2024-03-31",
                "risk_level": "high"
            }
        ]

        # Supply chain risks
        supply_chain_risks = [
            {
                "risk_type": "Supplier Concentration",
                "description": "High dependency on single cloud provider",
                "probability": "medium",
                "impact": "high",
                "mitigation": "Multi-cloud strategy implementation",
                "owner": "CTO"
            },
            {
                "risk_type": "Contract Expiration",
                "description": "5 critical contracts expiring in Q2",
                "probability": "high",
                "impact": "medium",
                "mitigation": "Early renewal negotiations",
                "owner": "Procurement Manager"
            },
            {
                "risk_type": "Price Volatility",
                "description": "AI platform pricing increases expected",
                "probability": "high",
                "impact": "medium",
                "mitigation": "Long-term contracts and alternatives",
                "owner": "Finance Director"
            }
        ]

        return {
            "supply_chain_metrics": supply_chain_metrics,
            "key_vendors": key_vendors,
            "supply_chain_risks": supply_chain_risks,
            "procurement_opportunities": [
                {
                    "opportunity": "Vendor Consolidation",
                    "potential_savings": 1200000,
                    "complexity": "medium",
                    "timeline": "6 months"
                },
                {
                    "opportunity": "Contract Renegotiation",
                    "potential_savings": 850000,
                    "complexity": "low",
                    "timeline": "3 months"
                },
                {
                    "opportunity": "Alternative Sourcing",
                    "potential_savings": 650000,
                    "complexity": "high",
                    "timeline": "12 months"
                }
            ],
            "vendor_diversity": {
                "minority_owned": 15.8,
                "women_owned": 12.5,
                "local_suppliers": 28.5,
                "sustainable_vendors": 45.2
            },
            "contract_portfolio": {
                "total_contracts": 285,
                "expiring_next_quarter": 15,
                "up_for_renewal": 25,
                "renegotiation_opportunities": 8,
                "auto_renewal_rate": 65.2
            }
        }

    def _generate_quality_metrics(self, time_period: str) -> Dict[str, Any]:
        """Generate quality metrics and continuous improvement analytics."""

        quality_metrics = {
            "overall_quality_score": 91.5,
            "defect_rate": 1.2,  # percentage
            "customer_complaints": 25,
            "quality_incidents": 8,
            "compliance_score": 96.8,
            "process_capability": 1.67,  # Cpk
            "first_pass_yield": 94.2,
            "rework_rate": 3.8
        }

        # Quality by category
        quality_categories = {
            "product_quality": {
                "score": 94.2,
                "defect_density": 0.8,
                "customer_reported_issues": 12,
                "field_failure_rate": 0.5
            },
            "service_quality": {
                "score": 89.8,
                "service_level_breaches": 5,
                "customer_escalations": 8,
                "service_availability": 99.8
            },
            "process_quality": {
                "score": 90.5,
                "process_variations": 15,
                "non_conformances": 18,
                "corrective_actions": 22
            }
        }

        # Continuous improvement initiatives
        improvement_initiatives = [
            {
                "initiative_id": "QI001",
                "name": "Six Sigma Black Belt Program",
                "category": "Process Improvement",
                "status": "active",
                "start_date": "2024-01-01",
                "expected_completion": "2024-06-30",
                "investment": 250000,
                "expected_savings": 1200000,
                "roi": 480.0,
                "participants": 25
            },
            {
                "initiative_id": "QI002",
                "name": "Automated Quality Control",
                "category": "Technology",
                "status": "implementation",
                "start_date": "2024-02-01",
                "expected_completion": "2024-04-30",
                "investment": 450000,
                "expected_savings": 850000,
                "roi": 188.9,
                "participants": 15
            },
            {
                "initiative_id": "QI003",
                "name": "Customer Feedback Loop Enhancement",
                "category": "Customer Experience",
                "status": "planning",
                "start_date": "2024-03-01",
                "expected_completion": "2024-08-31",
                "investment": 125000,
                "expected_savings": 650000,
                "roi": 520.0,
                "participants": 35
            }
        ]

        return {
            "quality_metrics": quality_metrics,
            "quality_categories": quality_categories,
            "improvement_initiatives": improvement_initiatives,
            "quality_trends": {
                "score_trend": "improving",
                "defect_trend": "decreasing",
                "complaint_trend": "stable",
                "compliance_trend": "improving"
            },
            "benchmarking": {
                "industry_average_quality": 85.2,
                "top_quartile_benchmark": 95.8,
                "our_percentile_ranking": 78
            },
            "quality_costs": {
                "prevention_costs": 485000,
                "appraisal_costs": 285000,
                "internal_failure_costs": 125000,
                "external_failure_costs": 85000,
                "total_quality_costs": 980000,
                "cost_of_quality_ratio": 2.8  # percentage of revenue
            }
        }

    def _generate_operational_risk_assessment(self, time_period: str) -> Dict[str, Any]:
        """Generate operational risk assessment and mitigation strategies."""

        operational_risks = [
            {
                "risk_id": "RISK001",
                "category": "Process Risk",
                "description": "Supply chain disruption affecting delivery commitments",
                "probability": OperationalRisk.MEDIUM.value,
                "impact": OperationalRisk.HIGH.value,
                "risk_score": 7.5,
                "mitigation_status": "active",
                "mitigation_actions": [
                    "Diversify supplier base",
                    "Increase safety stock levels",
                    "Develop contingency suppliers"
                ],
                "owner": "Supply Chain Manager",
                "review_date": "2024-03-31"
            },
            {
                "risk_id": "RISK002",
                "category": "Technology Risk",
                "description": "AI platform dependency creating single point of failure",
                "probability": OperationalRisk.MEDIUM.value,
                "impact": OperationalRisk.CRITICAL.value,
                "risk_score": 8.2,
                "mitigation_status": "planning",
                "mitigation_actions": [
                    "Implement multi-vendor AI strategy",
                    "Develop in-house capabilities",
                    "Create failover procedures"
                ],
                "owner": "CTO",
                "review_date": "2024-02-29"
            },
            {
                "risk_id": "RISK003",
                "category": "Human Resources Risk",
                "description": "Key talent retention in competitive AI market",
                "probability": OperationalRisk.HIGH.value,
                "impact": OperationalRisk.MEDIUM.value,
                "risk_score": 6.8,
                "mitigation_status": "ongoing",
                "mitigation_actions": [
                    "Competitive compensation review",
                    "Enhanced career development programs",
                    "Flexible work arrangements"
                ],
                "owner": "CHRO",
                "review_date": "2024-04-30"
            },
            {
                "risk_id": "RISK004",
                "category": "Regulatory Risk",
                "description": "AI governance and compliance requirements",
                "probability": OperationalRisk.HIGH.value,
                "impact": OperationalRisk.MEDIUM.value,
                "risk_score": 6.5,
                "mitigation_status": "active",
                "mitigation_actions": [
                    "Establish AI ethics committee",
                    "Implement compliance framework",
                    "Regular regulatory monitoring"
                ],
                "owner": "Chief Legal Officer",
                "review_date": "2024-03-15"
            }
        ]

        # Risk heat map data
        risk_heatmap = {
            "high_probability_high_impact": 1,
            "high_probability_medium_impact": 2,
            "high_probability_low_impact": 0,
            "medium_probability_high_impact": 1,
            "medium_probability_medium_impact": 3,
            "medium_probability_low_impact": 2,
            "low_probability_high_impact": 2,
            "low_probability_medium_impact": 1,
            "low_probability_low_impact": 3
        }

        # Business continuity
        business_continuity = {
            "disaster_recovery": {
                "rto": 2,  # hours
                "rpo": 30,  # minutes
                "backup_frequency": "continuous",
                "last_test": "2024-01-15",
                "test_success_rate": 98.5
            },
            "crisis_management": {
                "response_team_size": 15,
                "escalation_procedures": "documented",
                "communication_plan": "tested",
                "last_drill": "2024-01-10",
                "drill_effectiveness": 92.8
            },
            "operational_resilience": {
                "redundancy_score": 87.5,
                "failover_capability": 94.2,
                "monitoring_coverage": 96.8,
                "automated_response": 78.5
            }
        }

        return {
            "operational_risks": operational_risks,
            "risk_summary": {
                "total_risks": len(operational_risks),
                "critical_risks": len([r for r in operational_risks if r["risk_score"] >= 8.0]),
                "high_risks": len([r for r in operational_risks if 6.0 <= r["risk_score"] < 8.0]),
                "medium_risks": len([r for r in operational_risks if 4.0 <= r["risk_score"] < 6.0]),
                "low_risks": len([r for r in operational_risks if r["risk_score"] < 4.0])
            },
            "risk_heatmap": risk_heatmap,
            "business_continuity": business_continuity,
            "risk_appetite": {
                "strategic_risks": "medium",
                "operational_risks": "low",
                "financial_risks": "low",
                "compliance_risks": "very_low"
            },
            "mitigation_effectiveness": {
                "average_mitigation_score": 82.5,
                "risks_with_mitigation": 85.2,
                "overdue_actions": 3,
                "effectiveness_trend": "improving"
            }
        }

    def _generate_automation_analytics(self, time_period: str) -> Dict[str, Any]:
        """Generate automation status and ROI analytics."""

        automation_metrics = {
            "overall_automation_rate": 64.9,
            "processes_automated": 292,
            "total_processes": 450,
            "automation_roi": 285.5,
            "time_saved_hours": 12500,
            "cost_savings_annual": 8500000,
            "error_reduction": 68.5
        }

        # Automation by category
        automation_categories = {
            "financial_processes": {
                "automation_rate": 78.5,
                "processes_automated": 45,
                "cost_savings": 2800000,
                "time_saved": 3500
            },
            "hr_processes": {
                "automation_rate": 68.2,
                "processes_automated": 35,
                "cost_savings": 1250000,
                "time_saved": 2800
            },
            "customer_service": {
                "automation_rate": 55.8,
                "processes_automated": 28,
                "cost_savings": 1850000,
                "time_saved": 2200
            },
            "supply_chain": {
                "automation_rate": 52.5,
                "processes_automated": 85,
                "cost_savings": 1650000,
                "time_saved": 2500
            },
            "it_operations": {
                "automation_rate": 88.5,
                "processes_automated": 99,
                "cost_savings": 950000,
                "time_saved": 1500
            }
        }

        # Automation pipeline
        automation_pipeline = [
            {
                "initiative": "Financial Close Automation",
                "status": "implementation",
                "completion": 75,
                "expected_go_live": "2024-03-31",
                "expected_savings": 450000,
                "complexity": "high"
            },
            {
                "initiative": "Customer Onboarding AI",
                "status": "testing",
                "completion": 90,
                "expected_go_live": "2024-02-28",
                "expected_savings": 280000,
                "complexity": "medium"
            },
            {
                "initiative": "Inventory Management Automation",
                "status": "planning",
                "completion": 25,
                "expected_go_live": "2024-06-30",
                "expected_savings": 350000,
                "complexity": "medium"
            }
        ]

        return {
            "automation_metrics": automation_metrics,
            "automation_categories": automation_categories,
            "automation_pipeline": automation_pipeline,
            "automation_benefits": {
                "productivity_improvement": 35.8,
                "quality_improvement": 28.5,
                "cost_reduction": 25.2,
                "employee_satisfaction": 18.5,
                "customer_satisfaction": 12.8
            },
            "automation_challenges": [
                {
                    "challenge": "Change Management",
                    "impact": "medium",
                    "mitigation": "Enhanced training and communication"
                },
                {
                    "challenge": "Technical Complexity",
                    "impact": "high",
                    "mitigation": "Phased implementation approach"
                },
                {
                    "challenge": "Integration Issues",
                    "impact": "medium",
                    "mitigation": "API-first architecture"
                }
            ],
            "automation_roadmap": {
                "q1_2024": "Customer service automation completion",
                "q2_2024": "Financial process automation",
                "q3_2024": "Supply chain optimization",
                "q4_2024": "AI-powered decision support"
            }
        }

    def _generate_cost_efficiency_analysis(self, time_period: str) -> Dict[str, Any]:
        """Generate cost efficiency and optimization analysis."""

        cost_metrics = {
            "operational_cost_ratio": 72.8,  # percent of revenue
            "cost_per_employee": 125000,
            "cost_per_customer": 285.50,
            "cost_per_transaction": 12.45,
            "efficiency_improvement": 15.8,  # percent year over year
            "cost_avoidance": 5200000  # annual
        }

        # Cost optimization opportunities
        cost_optimizations = [
            {
                "category": "Process Automation",
                "current_cost": 12500000,
                "optimized_cost": 8750000,
                "savings": 3750000,
                "implementation_cost": 650000,
                "payback_months": 2.1,
                "risk": "low"
            },
            {
                "category": "Vendor Consolidation",
                "current_cost": 8500000,
                "optimized_cost": 7350000,
                "savings": 1150000,
                "implementation_cost": 125000,
                "payback_months": 1.3,
                "risk": "low"
            },
            {
                "category": "Resource Optimization",
                "current_cost": 15000000,
                "optimized_cost": 13200000,
                "savings": 1800000,
                "implementation_cost": 280000,
                "payback_months": 1.9,
                "risk": "medium"
            }
        ]

        # Cost efficiency trends
        monthly_costs = [28500000, 29200000, 28800000, 29100000, 28600000, 28350000]
        efficiency_trend = {
            "cost_trend": "decreasing",
            "efficiency_improvement_rate": 2.8,  # percent per month
            "cost_per_unit_trend": "improving",
            "benchmark_comparison": "above_average"
        }

        return {
            "cost_metrics": cost_metrics,
            "cost_optimizations": cost_optimizations,
            "efficiency_trends": efficiency_trend,
            "monthly_costs": monthly_costs,
            "cost_categories": {
                "personnel": {"cost": 18500000, "percentage": 65.1},
                "technology": {"cost": 5200000, "percentage": 18.3},
                "facilities": {"cost": 2800000, "percentage": 9.9},
                "external_services": {"cost": 1950000, "percentage": 6.7}
            },
            "benchmarking": {
                "industry_average": 78.5,
                "top_quartile": 68.2,
                "our_percentile": 72,
                "improvement_potential": 5.7
            },
            "cost_drivers": [
                {"driver": "Headcount Growth", "impact": 45.2},
                {"driver": "Technology Investment", "impact": 28.5},
                {"driver": "Market Expansion", "impact": 18.8},
                {"driver": "Regulatory Compliance", "impact": 7.5}
            ]
        }

    def _generate_strategic_initiatives_status(self, time_period: str) -> Dict[str, Any]:
        """Generate strategic initiatives tracking and status."""

        strategic_initiatives = [
            {
                "initiative_id": "STR001",
                "name": "Digital Transformation Program",
                "category": "Technology",
                "priority": "high",
                "status": "on_track",
                "completion": 68.5,
                "start_date": "2024-01-01",
                "target_completion": "2024-12-31",
                "budget": 5000000,
                "spent": 2850000,
                "expected_roi": 285.5,
                "key_milestones": [
                    {"milestone": "Process mapping complete", "status": "completed"},
                    {"milestone": "Technology selection", "status": "completed"},
                    {"milestone": "Pilot implementation", "status": "in_progress"},
                    {"milestone": "Full rollout", "status": "planned"}
                ],
                "risks": ["Resource availability", "Change management"],
                "owner": "CTO"
            },
            {
                "initiative_id": "STR002",
                "name": "Customer Experience Enhancement",
                "category": "Customer",
                "priority": "high",
                "status": "ahead_of_schedule",
                "completion": 78.2,
                "start_date": "2023-10-01",
                "target_completion": "2024-06-30",
                "budget": 2500000,
                "spent": 1850000,
                "expected_roi": 195.8,
                "key_milestones": [
                    {"milestone": "Customer journey mapping", "status": "completed"},
                    {"milestone": "Technology implementation", "status": "completed"},
                    {"milestone": "Team training", "status": "completed"},
                    {"milestone": "Go-live", "status": "in_progress"}
                ],
                "risks": ["Customer adoption"],
                "owner": "Chief Customer Officer"
            },
            {
                "initiative_id": "STR003",
                "name": "Sustainability Program",
                "category": "ESG",
                "priority": "medium",
                "status": "at_risk",
                "completion": 45.2,
                "start_date": "2024-02-01",
                "target_completion": "2024-12-31",
                "budget": 1800000,
                "spent": 650000,
                "expected_roi": 125.5,
                "key_milestones": [
                    {"milestone": "Baseline assessment", "status": "completed"},
                    {"milestone": "Target setting", "status": "in_progress"},
                    {"milestone": "Implementation plan", "status": "delayed"},
                    {"milestone": "Monitoring system", "status": "planned"}
                ],
                "risks": ["Regulatory changes", "Technology readiness"],
                "owner": "Chief Sustainability Officer"
            }
        ]

        # Initiative portfolio summary
        portfolio_summary = {
            "total_initiatives": len(strategic_initiatives),
            "on_track": len([i for i in strategic_initiatives if i["status"] == "on_track"]),
            "ahead_of_schedule": len([i for i in strategic_initiatives if i["status"] == "ahead_of_schedule"]),
            "at_risk": len([i for i in strategic_initiatives if i["status"] == "at_risk"]),
            "delayed": len([i for i in strategic_initiatives if i["status"] == "delayed"]),
            "total_budget": sum(i["budget"] for i in strategic_initiatives),
            "total_spent": sum(i["spent"] for i in strategic_initiatives),
            "average_completion": sum(i["completion"] for i in strategic_initiatives) / len(strategic_initiatives)
        }

        return {
            "strategic_initiatives": strategic_initiatives,
            "portfolio_summary": portfolio_summary,
            "initiative_performance": {
                "budget_utilization": 58.5,
                "schedule_performance": 82.8,
                "quality_metrics": 91.2,
                "stakeholder_satisfaction": 87.5
            },
            "resource_allocation": {
                "technology_initiatives": 65.8,
                "customer_initiatives": 20.5,
                "operational_initiatives": 8.2,
                "esg_initiatives": 5.5
            },
            "upcoming_milestones": [
                {
                    "initiative": "Digital Transformation Program",
                    "milestone": "Pilot implementation completion",
                    "due_date": "2024-03-31",
                    "risk_level": "medium"
                },
                {
                    "initiative": "Customer Experience Enhancement",
                    "milestone": "Full go-live",
                    "due_date": "2024-02-28",
                    "risk_level": "low"
                }
            ]
        }

    def _generate_industry_benchmarking(self, time_period: str) -> Dict[str, Any]:
        """Generate industry benchmarking and competitive analysis."""

        benchmarking_data = {
            "operational_efficiency": {
                "our_score": 89.2,
                "industry_average": 82.5,
                "top_quartile": 94.8,
                "percentile_rank": 78,
                "gap_to_leader": 5.6
            },
            "customer_satisfaction": {
                "our_score": 94.1,
                "industry_average": 87.2,
                "top_quartile": 96.5,
                "percentile_rank": 85,
                "gap_to_leader": 2.4
            },
            "automation_rate": {
                "our_score": 64.9,
                "industry_average": 58.2,
                "top_quartile": 78.5,
                "percentile_rank": 72,
                "gap_to_leader": 13.6
            },
            "cost_efficiency": {
                "our_score": 87.8,
                "industry_average": 79.5,
                "top_quartile": 92.1,
                "percentile_rank": 81,
                "gap_to_leader": 4.3
            }
        }

        # Competitive positioning
        competitive_analysis = {
            "market_position": "Top 3",
            "operational_ranking": 5,
            "technology_adoption": "Leading",
            "customer_service": "Above Average",
            "innovation_index": 92.5,
            "digital_maturity": 85.8
        }

        # Best practices adoption
        best_practices = [
            {
                "practice": "Agile Operations",
                "adoption_status": "implemented",
                "maturity_level": "advanced",
                "impact": "high",
                "next_steps": "Scale to all departments"
            },
            {
                "practice": "Predictive Analytics",
                "adoption_status": "pilot",
                "maturity_level": "developing",
                "impact": "high",
                "next_steps": "Full implementation plan"
            },
            {
                "practice": "Customer-Centric Design",
                "adoption_status": "implemented",
                "maturity_level": "mature",
                "impact": "medium",
                "next_steps": "Continuous improvement"
            }
        ]

        return {
            "benchmarking_data": benchmarking_data,
            "competitive_analysis": competitive_analysis,
            "best_practices": best_practices,
            "improvement_opportunities": [
                {
                    "metric": "Automation Rate",
                    "gap": 13.6,
                    "priority": "high",
                    "effort": "medium",
                    "timeline": "6 months"
                },
                {
                    "metric": "Process Efficiency",
                    "gap": 5.6,
                    "priority": "medium",
                    "effort": "low",
                    "timeline": "3 months"
                }
            ],
            "industry_trends": [
                "Increased AI adoption in operations",
                "Focus on employee experience",
                "Sustainability integration",
                "Real-time decision making",
                "Customer-centric operations"
            ],
            "peer_comparison": {
                "similar_size_companies": 15,
                "our_rank": 3,
                "performance_gap": "+12.5%",
                "areas_of_excellence": ["Technology adoption", "Customer service"],
                "areas_for_improvement": ["Cost efficiency", "Process automation"]
            }
        }

    def _generate_visualizations(self, time_period: str, focus_area: str) -> Dict[str, Any]:
        """Generate comprehensive COO dashboard visualizations."""

        visualizations = {}

        if VIZLYCHART_AVAILABLE:
            # Executive overview dashboard
            visualizations["executive_overview"] = self._create_executive_overview_chart()

            # Operational KPIs
            visualizations["operational_kpis"] = self._create_operational_kpis_chart()

            # Process efficiency
            visualizations["process_efficiency"] = self._create_process_efficiency_chart()

            # Team performance
            visualizations["team_performance"] = self._create_team_performance_chart()

            # Risk assessment
            visualizations["risk_assessment"] = self._create_risk_assessment_chart()

            # Strategic initiatives
            visualizations["strategic_initiatives"] = self._create_strategic_initiatives_chart()

            # Focus area specific visualizations
            if focus_area == "process_optimization":
                visualizations["process_deep_dive"] = self._create_process_deep_dive_chart()
                visualizations["automation_roadmap"] = self._create_automation_roadmap_chart()

            elif focus_area == "customer_experience":
                visualizations["customer_journey"] = self._create_customer_journey_chart()
                visualizations["satisfaction_trends"] = self._create_satisfaction_trends_chart()

        return visualizations

    def _create_executive_overview_chart(self) -> Dict[str, Any]:
        """Create executive overview visualization."""
        overview_data = self._generate_executive_summary("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = OperationalDashboard()
            return chart.create_executive_overview(
                data=overview_data,
                title="COO Executive Overview",
                theme="executive_operations"
            )
        else:
            return {"chart_type": "executive_overview", "data": overview_data}

    def _create_operational_kpis_chart(self) -> Dict[str, Any]:
        """Create operational KPIs visualization."""
        kpis_data = self._generate_operational_kpis("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = OperationalDashboard()
            return chart.create_kpi_dashboard(
                kpis=kpis_data,
                title="Operational KPIs Dashboard",
                theme="operational_kpis"
            )
        else:
            return {"chart_type": "operational_kpis", "data": kpis_data}

    def _create_process_efficiency_chart(self) -> Dict[str, Any]:
        """Create process efficiency visualization."""
        process_data = self._generate_process_efficiency_analysis("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = ProcessAnalytics()
            return chart.create_efficiency_analysis(
                processes=process_data["processes"],
                title="Process Efficiency Analysis",
                theme="process_optimization"
            )
        else:
            return {"chart_type": "process_efficiency", "data": process_data}

    def _create_team_performance_chart(self) -> Dict[str, Any]:
        """Create team performance visualization."""
        team_data = self._generate_team_performance_analysis("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = OperationalDashboard()
            return chart.create_team_performance(
                teams=team_data["teams"],
                title="Team Performance Analysis",
                theme="team_excellence"
            )
        else:
            return {"chart_type": "team_performance", "data": team_data}

    def _create_risk_assessment_chart(self) -> Dict[str, Any]:
        """Create risk assessment visualization."""
        risk_data = self._generate_operational_risk_assessment("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = BusinessIntelligence()
            return chart.create_risk_heatmap(
                risks=risk_data["operational_risks"],
                title="Operational Risk Assessment",
                theme="risk_management"
            )
        else:
            return {"chart_type": "risk_assessment", "data": risk_data}

    def _create_strategic_initiatives_chart(self) -> Dict[str, Any]:
        """Create strategic initiatives visualization."""
        initiatives_data = self._generate_strategic_initiatives_status("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = OperationalDashboard()
            return chart.create_initiatives_tracker(
                initiatives=initiatives_data["strategic_initiatives"],
                title="Strategic Initiatives Status",
                theme="strategic_planning"
            )
        else:
            return {"chart_type": "strategic_initiatives", "data": initiatives_data}

    def get_intelligent_recommendations(
        self,
        current_metrics: Optional[Dict[str, Any]] = None,
        historical_data: Optional[List[Dict[str, Any]]] = None
    ) -> Dict[str, Any]:
        """
        Get intelligent recommendations for operational optimization.

        Returns hardware, scaling, and cost optimization recommendations.
        """
        from .recommendations_dashboard import RecommendationsDashboard

        # Use provided metrics or generate from current state
        if current_metrics is None:
            current_metrics = {
                'instance_count': 4,
                'avg_cpu_utilization': 65,
                'avg_gpu_utilization': 70,
                'cost_per_instance_hour': 2.5,
                'workload_type': 'inference',
                'has_gpu': True,
                'gpu_count': 1,
                'requests_per_second': 25
            }

        recommendations_dash = RecommendationsDashboard()
        return recommendations_dash.get_dashboard_data(
            current_metrics,
            historical_data,
            workload_type=current_metrics.get('workload_type', 'inference'),
            cloud_provider='aws'
        )

    def export_operational_report(self, dashboard_data: Dict[str, Any], format: str = "pdf") -> str:
        """Export comprehensive operational report."""

        report_id = f"coo_operational_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

        if format == "pdf":
            # Generate executive PDF report
            report_path = f"/exports/{report_id}.pdf"
            logger.info(f"Generated COO Operational Report: {report_path}")

        elif format == "powerpoint":
            # Generate PowerPoint presentation
            report_path = f"/exports/{report_id}.pptx"
            logger.info(f"Generated COO PowerPoint Report: {report_path}")

        elif format == "excel":
            # Generate Excel workbook
            report_path = f"/exports/{report_id}.xlsx"
            logger.info(f"Generated COO Excel Report: {report_path}")

        return report_path


# Initialize COO Dashboard
coo_dashboard = COODashboard()


def get_coo_dashboard() -> COODashboard:
    """Get COO dashboard instance."""
    return coo_dashboard