The Impact of 5G on Industries in 2025: Revolutionizing Business Operations Through Ultra-Fast, Low-Latency Connectivity

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

class IndustryType(Enum):
    MANUFACTURING = "manufacturing"
    HEALTHCARE = "healthcare"
    LOGISTICS = "logistics"
    ENERGY = "energy"
    TRANSPORTATION = "transportation"
    RETAIL = "retail"
    AGRICULTURE = "agriculture"
    ENTERTAINMENT = "entertainment"

class NetworkMetric(Enum):
    LATENCY = "latency"
    THROUGHPUT = "throughput"
    RELIABILITY = "reliability"
    COVERAGE = "coverage"
    DEVICE_DENSITY = "device_density"

@dataclass
class Industry5GDeployment:
    """5G deployment configuration for specific industry"""
    deployment_id: str
    industry_type: IndustryType
    location: str
    deployment_date: datetime
    network_type: str  # public, private, hybrid
    coverage_area_km2: float
    connected_devices: int
    use_cases: List[str] = field(default_factory=list)
    performance_requirements: Dict[str, float] = field(default_factory=dict)
    roi_target: float = 2.0
    
@dataclass
class NetworkPerformanceMetric:
    """Real-time 5G network performance measurement"""
    metric_id: str
    deployment_id: str
    timestamp: datetime
    metric_type: NetworkMetric
    value: float
    unit: str
    quality_threshold_met: bool = True
    
@dataclass
class BusinessImpactMeasurement:
    """Business impact measurement from 5G deployment"""
    measurement_id: str
    deployment_id: str
    impact_category: str  # productivity, cost_reduction, revenue_growth, safety
    baseline_value: float
    current_value: float
    improvement_percentage: float
    measurement_date: datetime
    validation_status: str = "verified"
    
class FiveGIndustryAnalytics:
    """Comprehensive 5G industry analytics and monitoring system"""
    
    def __init__(self, system_name: str):
        self.system_name = system_name
        self.deployments: Dict[str, Industry5GDeployment] = {}
        self.performance_metrics: List[NetworkPerformanceMetric] = []
        self.business_impacts: List[BusinessImpactMeasurement] = []
        
        # Industry-specific 5G performance requirements
        self.performance_standards = {
            IndustryType.MANUFACTURING: {
                NetworkMetric.LATENCY: {'max': 1.0, 'unit': 'ms'},  # Ultra-low latency for automation
                NetworkMetric.THROUGHPUT: {'min': 1000, 'unit': 'Mbps'},  # High bandwidth for data
                NetworkMetric.RELIABILITY: {'min': 99.999, 'unit': '%'},  # Five nines reliability
                NetworkMetric.DEVICE_DENSITY: {'max': 1000000, 'unit': 'devices/km2'}
            },
            IndustryType.HEALTHCARE: {
                NetworkMetric.LATENCY: {'max': 5.0, 'unit': 'ms'},  # Low latency for telemedicine
                NetworkMetric.THROUGHPUT: {'min': 500, 'unit': 'Mbps'},  # High quality video/data
                NetworkMetric.RELIABILITY: {'min': 99.99, 'unit': '%'},  # High reliability for patient care
                NetworkMetric.COVERAGE: {'min': 95, 'unit': '%'}  # Comprehensive coverage
            },
            IndustryType.LOGISTICS: {
                NetworkMetric.LATENCY: {'max': 10.0, 'unit': 'ms'},  # Real-time tracking
                NetworkMetric.THROUGHPUT: {'min': 100, 'unit': 'Mbps'},  # Asset monitoring
                NetworkMetric.DEVICE_DENSITY: {'max': 100000, 'unit': 'devices/km2'},  # IoT sensors
                NetworkMetric.COVERAGE: {'min': 90, 'unit': '%'}  # Wide area coverage
            },
            IndustryType.ENERGY: {
                NetworkMetric.LATENCY: {'max': 20.0, 'unit': 'ms'},  # Grid monitoring
                NetworkMetric.RELIABILITY: {'min': 99.95, 'unit': '%'},  # Critical infrastructure
                NetworkMetric.COVERAGE: {'min': 85, 'unit': '%'}  # Remote asset coverage
            }
        }
        
        # Expected business impact benchmarks
        self.impact_benchmarks = {
            IndustryType.MANUFACTURING: {
                'productivity_improvement': 30.0,
                'cost_reduction': 25.0,
                'safety_improvement': 40.0,
                'quality_improvement': 35.0
            },
            IndustryType.HEALTHCARE: {
                'patient_satisfaction': 45.0,
                'operational_efficiency': 35.0,
                'cost_reduction': 20.0,
                'access_improvement': 60.0
            },
            IndustryType.LOGISTICS: {
                'delivery_speed': 25.0,
                'cost_reduction': 20.0,
                'inventory_optimization': 30.0,
                'visibility_improvement': 50.0
            }
        }
        
        # 5G use case templates by industry
        self.use_case_templates = {
            IndustryType.MANUFACTURING: [
                'Predictive Maintenance', 'Quality Control Automation', 'Flexible Production Lines',
                'Real-time Asset Tracking', 'Collaborative Robotics', 'Digital Twin Operations'
            ],
            IndustryType.HEALTHCARE: [
                'Remote Patient Monitoring', 'Telemedicine Consultations', 'Robotic Surgery',
                'Emergency Response Systems', 'Medical Asset Tracking', 'AI Diagnostics'
            ],
            IndustryType.LOGISTICS: [
                'Real-time Shipment Tracking', 'Warehouse Automation', 'Fleet Management',
                'Predictive Logistics', 'Autonomous Delivery', 'Supply Chain Visibility'
            ]
        }
        
    def register_deployment(self, deployment: Industry5GDeployment) -> bool:
        """Register new 5G industry deployment"""
        # Set default use cases based on industry type
        if not deployment.use_cases:
            deployment.use_cases = self.use_case_templates.get(deployment.industry_type, [])
            
        # Set performance requirements based on industry standards
        if not deployment.performance_requirements:
            industry_standards = self.performance_standards.get(deployment.industry_type, {})
            for metric, requirements in industry_standards.items():
                deployment.performance_requirements[metric.value] = requirements
                
        self.deployments[deployment.deployment_id] = deployment
        
        print(f"Registered 5G deployment: {deployment.deployment_id} ({deployment.industry_type.value})")
        print(f"  Location: {deployment.location}")
        print(f"  Network Type: {deployment.network_type}")
        print(f"  Use Cases: {len(deployment.use_cases)}")
        print(f"  Connected Devices: {deployment.connected_devices:,}")
        
        return True
        
    def collect_performance_data(self, deployment_id: str, metric_type: NetworkMetric, 
                               value: float, unit: str) -> NetworkPerformanceMetric:
        """Collect 5G network performance metrics"""
        if deployment_id not in self.deployments:
            raise ValueError(f"Deployment {deployment_id} not found")
            
        deployment = self.deployments[deployment_id]
        
        # Check if metric meets quality thresholds
        quality_met = self._evaluate_performance_quality(
            deployment.industry_type, metric_type, value
        )
        
        metric = NetworkPerformanceMetric(
            metric_id=f"METRIC_{uuid.uuid4()}",
            deployment_id=deployment_id,
            timestamp=datetime.now(),
            metric_type=metric_type,
            value=value,
            unit=unit,
            quality_threshold_met=quality_met
        )
        
        self.performance_metrics.append(metric)
        
        # Generate alerts for performance issues
        if not quality_met:
            self._generate_performance_alert(deployment, metric)
            
        return metric
        
    def measure_business_impact(self, deployment_id: str, impact_category: str,
                              baseline_value: float, current_value: float) -> BusinessImpactMeasurement:
        """Measure and record business impact from 5G deployment"""
        if deployment_id not in self.deployments:
            raise ValueError(f"Deployment {deployment_id} not found")
            
        # Calculate improvement percentage
        if baseline_value != 0:
            improvement = ((current_value - baseline_value) / baseline_value) * 100
        else:
            improvement = 0
            
        impact_measurement = BusinessImpactMeasurement(
            measurement_id=f"IMPACT_{uuid.uuid4()}",
            deployment_id=deployment_id,
            impact_category=impact_category,
            baseline_value=baseline_value,
            current_value=current_value,
            improvement_percentage=improvement,
            measurement_date=datetime.now()
        )
        
        self.business_impacts.append(impact_measurement)
        
        # Compare against industry benchmarks
        deployment = self.deployments[deployment_id]
        benchmark = self._get_impact_benchmark(deployment.industry_type, impact_category)
        
        if benchmark and improvement >= benchmark:
            print(f"✅ {impact_category} improvement ({improvement:.1f}%) exceeds benchmark ({benchmark:.1f}%)")
        elif benchmark:
            print(f"⚠️ {impact_category} improvement ({improvement:.1f}%) below benchmark ({benchmark:.1f}%)")
            
        return impact_measurement
        
    def analyze_industry_performance(self, industry_type: IndustryType) -> Dict[str, Any]:
        """Analyze 5G performance across specific industry"""
        industry_deployments = [d for d in self.deployments.values() if d.industry_type == industry_type]
        
        if not industry_deployments:
            return {'error': f'No deployments found for {industry_type.value} industry'}
            
        # Aggregate performance metrics
        deployment_ids = [d.deployment_id for d in industry_deployments]
        industry_metrics = [m for m in self.performance_metrics if m.deployment_id in deployment_ids]
        
        # Calculate performance statistics by metric type
        performance_stats = {}
        for metric_type in NetworkMetric:
            type_metrics = [m for m in industry_metrics if m.metric_type == metric_type]
            if type_metrics:
                performance_stats[metric_type.value] = {
                    'average': np.mean([m.value for m in type_metrics]),
                    'min': np.min([m.value for m in type_metrics]),
                    'max': np.max([m.value for m in type_metrics]),
                    'quality_compliance': (len([m for m in type_metrics if m.quality_threshold_met]) / len(type_metrics)) * 100
                }
                
        # Analyze business impact
        industry_impacts = [i for i in self.business_impacts if i.deployment_id in deployment_ids]
        impact_summary = {}
        
        for impact in industry_impacts:
            category = impact.impact_category
            if category not in impact_summary:
                impact_summary[category] = []
            impact_summary[category].append(impact.improvement_percentage)
            
        # Calculate average improvements by category
        avg_improvements = {}
        for category, improvements in impact_summary.items():
            avg_improvements[category] = {
                'average_improvement': np.mean(improvements),
                'measurements_count': len(improvements),
                'benchmark_comparison': self._compare_to_benchmark(industry_type, category, np.mean(improvements))
            }
            
        analysis = {
            'industry_type': industry_type.value,
            'total_deployments': len(industry_deployments),
            'total_connected_devices': sum(d.connected_devices for d in industry_deployments),
            'coverage_area_km2': sum(d.coverage_area_km2 for d in industry_deployments),
            'network_performance': performance_stats,
            'business_impact': avg_improvements,
            'deployment_types': self._analyze_deployment_types(industry_deployments),
            'roi_analysis': self._calculate_industry_roi(industry_deployments, industry_impacts)
        }
        
        return analysis
        
    def generate_cross_industry_report(self) -> Dict[str, Any]:
        """Generate comprehensive cross-industry 5G impact report"""
        report = {
            'report_timestamp': datetime.now().isoformat(),
            'total_deployments': len(self.deployments),
            'industries_covered': len(set(d.industry_type for d in self.deployments.values())),
            'industry_analysis': {},
            'performance_comparison': {},
            'business_impact_comparison': {},
            'technology_trends': self._analyze_technology_trends(),
            'recommendations': self._generate_industry_recommendations()
        }
        
        # Analyze each industry
        for industry_type in IndustryType:
            industry_analysis = self.analyze_industry_performance(industry_type)
            if 'error' not in industry_analysis:
                report['industry_analysis'][industry_type.value] = industry_analysis
                
        # Cross-industry performance comparison
        report['performance_comparison'] = self._compare_industry_performance()
        report['business_impact_comparison'] = self._compare_industry_impacts()
        
        return report
        
    def simulate_5g_deployment_scenario(self, industry_type: IndustryType, 
                                      scenarios: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Simulate different 5G deployment scenarios for industry"""
        simulation_results = {}
        
        for i, scenario in enumerate(scenarios):
            scenario_name = scenario.get('name', f'Scenario_{i+1}')
            
            # Extract scenario parameters
            network_type = scenario.get('network_type', 'private')
            coverage_area = scenario.get('coverage_area_km2', 10.0)
            device_count = scenario.get('connected_devices', 10000)
            investment = scenario.get('investment_amount', 5000000)
            
            # Simulate performance based on scenario parameters
            simulated_performance = self._simulate_network_performance(
                industry_type, network_type, coverage_area, device_count
            )
            
            # Estimate business impact
            estimated_impact = self._estimate_business_impact(
                industry_type, simulated_performance, investment
            )
            
            # Calculate ROI projection
            roi_projection = self._calculate_roi_projection(
                investment, estimated_impact, 5  # 5-year projection
            )
            
            simulation_results[scenario_name] = {
                'scenario_parameters': scenario,
                'simulated_performance': simulated_performance,
                'estimated_business_impact': estimated_impact,
                'roi_projection': roi_projection,
                'implementation_timeline': self._estimate_implementation_timeline(scenario),
                'risk_factors': self._assess_deployment_risks(industry_type, scenario)
            }
            
        return {
            'industry_type': industry_type.value,
            'simulation_results': simulation_results,
            'recommended_scenario': self._select_optimal_scenario(simulation_results),
            'comparative_analysis': self._compare_scenarios(simulation_results)
        }
        
    # Helper methods for analysis and simulation
    def _evaluate_performance_quality(self, industry_type: IndustryType, 
                                     metric_type: NetworkMetric, value: float) -> bool:
        """Evaluate if performance metric meets industry quality thresholds"""
        standards = self.performance_standards.get(industry_type, {})
        metric_standard = standards.get(metric_type, {})
        
        if 'max' in metric_standard:
            return value <= metric_standard['max']
        elif 'min' in metric_standard:
            return value >= metric_standard['min']
        else:
            return True  # No standard defined
            
    def _generate_performance_alert(self, deployment: Industry5GDeployment, 
                                  metric: NetworkPerformanceMetric):
        """Generate alert for performance issues"""
        alert = {
            'alert_id': f"ALERT_{uuid.uuid4()}",
            'timestamp': datetime.now().isoformat(),
            'deployment_id': deployment.deployment_id,
            'industry': deployment.industry_type.value,
            'metric_type': metric.metric_type.value,
            'actual_value': metric.value,
            'expected_range': deployment.performance_requirements.get(metric.metric_type.value, 'Not defined'),
            'severity': self._determine_alert_severity(deployment.industry_type, metric.metric_type, metric.value)
        }
        
        print(f"🚨 PERFORMANCE ALERT: {deployment.deployment_id}")
        print(f"   {metric.metric_type.value}: {metric.value} {metric.unit}")
        print(f"   Severity: {alert['severity']}")
        
    def _get_impact_benchmark(self, industry_type: IndustryType, impact_category: str) -> Optional[float]:
        """Get industry benchmark for specific impact category"""
        benchmarks = self.impact_benchmarks.get(industry_type, {})
        return benchmarks.get(impact_category)
        
    def _compare_to_benchmark(self, industry_type: IndustryType, category: str, value: float) -> str:
        """Compare impact value to industry benchmark"""
        benchmark = self._get_impact_benchmark(industry_type, category)
        if not benchmark:
            return "No benchmark available"
        elif value >= benchmark:
            return f"Exceeds benchmark by {value - benchmark:.1f}%"
        else:
            return f"Below benchmark by {benchmark - value:.1f}%"
            
    def _analyze_deployment_types(self, deployments: List[Industry5GDeployment]) -> Dict[str, int]:
        """Analyze distribution of deployment types"""
        types = {}
        for deployment in deployments:
            network_type = deployment.network_type
            types[network_type] = types.get(network_type, 0) + 1
        return types
        
    def _calculate_industry_roi(self, deployments: List[Industry5GDeployment], 
                              impacts: List[BusinessImpactMeasurement]) -> Dict[str, float]:
        """Calculate ROI analysis for industry deployments"""
        # Simplified ROI calculation
        total_improvements = sum(impact.improvement_percentage for impact in impacts)
        avg_improvement = total_improvements / len(impacts) if impacts else 0
        
        return {
            'average_improvement_percentage': avg_improvement,
            'estimated_annual_savings': avg_improvement * 100000,  # Simplified calculation
            'payback_period_years': max(1, min(10, 5 / max(avg_improvement / 100, 0.1)))
        }
        
    # Additional helper methods (simplified implementations)
    def _analyze_technology_trends(self) -> List[str]:
        trends = [
            "Private 5G networks gaining adoption in manufacturing",
            "Edge computing integration becoming standard",
            "AI-powered network optimization increasing efficiency",
            "Network slicing enabling specialized applications"
        ]
        return trends
        
    def _generate_industry_recommendations(self) -> List[str]:
        recommendations = [
            "Prioritize private 5G for mission-critical manufacturing applications",
            "Invest in edge computing infrastructure for real-time processing",
            "Develop industry-specific use cases to maximize ROI",
            "Ensure robust cybersecurity frameworks for 5G deployments"
        ]
        return recommendations
        
    def _compare_industry_performance(self) -> Dict[str, Any]:
        # Simplified cross-industry comparison
        return {
            'highest_performance_industry': 'manufacturing',
            'fastest_adoption_industry': 'healthcare',
            'highest_roi_industry': 'logistics'
        }
        
    def _compare_industry_impacts(self) -> Dict[str, Any]:
        return {
            'productivity_leader': 'manufacturing',
            'cost_reduction_leader': 'logistics',
            'innovation_leader': 'healthcare'
        }
        
    def _simulate_network_performance(self, industry_type: IndustryType, network_type: str, 
                                    coverage_area: float, device_count: int) -> Dict[str, float]:
        # Simplified performance simulation
        base_latency = 1.0 if network_type == 'private' else 5.0
        base_throughput = 1000 if network_type == 'private' else 500
        
        return {
            'latency_ms': base_latency * (1 + device_count / 100000),
            'throughput_mbps': base_throughput * (coverage_area / 10),
            'reliability_percent': 99.9 if network_type == 'private' else 99.5
        }
        
    def _estimate_business_impact(self, industry_type: IndustryType, 
                                performance: Dict[str, float], investment: float) -> Dict[str, float]:
        # Simplified impact estimation
        benchmarks = self.impact_benchmarks.get(industry_type, {})
        base_improvement = list(benchmarks.values())[0] if benchmarks else 20.0
        
        performance_factor = min(2.0, performance.get('reliability_percent', 99) / 99)
        investment_factor = min(2.0, investment / 1000000)
        
        return {
            'productivity_improvement': base_improvement * performance_factor,
            'cost_reduction': (base_improvement * 0.8) * investment_factor,
            'revenue_impact': base_improvement * performance_factor * investment_factor
        }
        
    def _calculate_roi_projection(self, investment: float, impact: Dict[str, float], years: int) -> Dict[str, float]:
        annual_benefit = sum(impact.values()) * 10000  # Simplified calculation
        total_benefit = annual_benefit * years
        net_benefit = total_benefit - investment
        roi_percentage = (net_benefit / investment) * 100 if investment > 0 else 0
        
        return {
            'investment_amount': investment,
            'annual_benefit': annual_benefit,
            'total_benefit_5_years': total_benefit,
            'net_benefit': net_benefit,
            'roi_percentage': roi_percentage,
            'payback_period_years': investment / annual_benefit if annual_benefit > 0 else float('inf')
        }
        
    # Additional simplified helper methods
    def _determine_alert_severity(self, industry_type, metric_type, value): return "high" if value > 10 else "medium"
    def _estimate_implementation_timeline(self, scenario): return {"planning_months": 3, "deployment_months": 6, "optimization_months": 2}
    def _assess_deployment_risks(self, industry_type, scenario): return ["Technical complexity", "Integration challenges", "Change management"]
    def _select_optimal_scenario(self, results): return max(results.items(), key=lambda x: x['roi_projection']['roi_percentage'])[0]
    def _compare_scenarios(self, results): return {"best_roi": "Scenario_1", "fastest_deployment": "Scenario_2", "lowest_risk": "Scenario_3"}

# Example usage and demonstration
def run_5g_industry_analytics_demo():
    print("=== 5G Industry Analytics System Demo ===")
    
    # Initialize 5G analytics system
    analytics_system = FiveGIndustryAnalytics("Global 5G Industry Monitor")
    
    # Register industry deployments
    deployments = [
        Industry5GDeployment(
            deployment_id="MFG_001",
            industry_type=IndustryType.MANUFACTURING,
            location="Smart Factory - Detroit",
            deployment_date=datetime(2024, 6, 1),
            network_type="private",
            coverage_area_km2=2.5,
            connected_devices=15000,
            roi_target=3.0
        ),
        Industry5GDeployment(
            deployment_id="HEALTH_001",
            industry_type=IndustryType.HEALTHCARE,
            location="Metro Hospital - Boston",
            deployment_date=datetime(2024, 8, 15),
            network_type="hybrid",
            coverage_area_km2=1.2,
            connected_devices=8000,
            roi_target=2.5
        ),
        Industry5GDeployment(
            deployment_id="LOG_001",
            industry_type=IndustryType.LOGISTICS,
            location="Distribution Center - Chicago",
            deployment_date=datetime(2024, 4, 20),
            network_type="public",
            coverage_area_km2=5.0,
            connected_devices=25000,
            roi_target=2.2
        )
    ]
    
    for deployment in deployments:
        analytics_system.register_deployment(deployment)
        
    print(f"\nRegistered {len(deployments)} 5G deployments across industries")
    
    # Collect performance metrics
    print("\n=== Collecting Network Performance Data ===")
    
    performance_data = [
        ("MFG_001", NetworkMetric.LATENCY, 0.8, "ms"),
        ("MFG_001", NetworkMetric.THROUGHPUT, 1200, "Mbps"),
        ("MFG_001", NetworkMetric.RELIABILITY, 99.99, "%"),
        ("HEALTH_001", NetworkMetric.LATENCY, 3.2, "ms"),
        ("HEALTH_001", NetworkMetric.THROUGHPUT, 650, "Mbps"),
        ("LOG_001", NetworkMetric.LATENCY, 8.5, "ms"),
        ("LOG_001", NetworkMetric.THROUGHPUT, 150, "Mbps"),
        ("LOG_001", NetworkMetric.DEVICE_DENSITY, 5000, "devices/km2")
    ]
    
    for deployment_id, metric_type, value, unit in performance_data:
        metric = analytics_system.collect_performance_data(deployment_id, metric_type, value, unit)
        status = "✅ MEETS" if metric.quality_threshold_met else "❌ BELOW"
        print(f"{deployment_id}: {metric_type.value} = {value} {unit} [{status} THRESHOLD]")
        
    # Measure business impacts
    print("\n=== Measuring Business Impacts ===")
    
    impact_measurements = [
        ("MFG_001", "productivity_improvement", 100, 132),  # 32% improvement
        ("MFG_001", "cost_reduction", 1000000, 750000),     # 25% cost reduction
        ("MFG_001", "safety_improvement", 50, 30),          # 40% fewer incidents
        ("HEALTH_001", "patient_satisfaction", 75, 85),    # 13% improvement
        ("HEALTH_001", "operational_efficiency", 100, 140), # 40% improvement
        ("LOG_001", "delivery_speed", 48, 36),             # 25% faster delivery
        ("LOG_001", "inventory_optimization", 100, 125)     # 25% improvement
    ]
    
    for deployment_id, category, baseline, current in impact_measurements:
        impact = analytics_system.measure_business_impact(deployment_id, category, baseline, current)
        print(f"{deployment_id}: {category} improved by {impact.improvement_percentage:.1f}%")
        
    # Analyze industry performance
    print("\n=== Industry Performance Analysis ===")
    
    for industry in [IndustryType.MANUFACTURING, IndustryType.HEALTHCARE, IndustryType.LOGISTICS]:
        analysis = analytics_system.analyze_industry_performance(industry)
        
        if 'error' not in analysis:
            print(f"\n{industry.value.title()} Industry Analysis:")
            print(f"  Deployments: {analysis['total_deployments']}")
            print(f"  Connected Devices: {analysis['total_connected_devices']:,}")
            print(f"  Coverage Area: {analysis['coverage_area_km2']:.1f} km²")
            
            # Network performance summary
            if 'latency' in analysis['network_performance']:
                latency = analysis['network_performance']['latency']
                print(f"  Average Latency: {latency['average']:.1f} ms")
                print(f"  Quality Compliance: {latency['quality_compliance']:.1f}%")
                
            # Business impact summary
            if analysis['business_impact']:
                print(f"  Business Impact Categories: {len(analysis['business_impact'])}")
                for category, metrics in analysis['business_impact'].items():
                    print(f"    {category}: {metrics['average_improvement']:.1f}% improvement")
                    print(f"      {metrics['benchmark_comparison']}")
                    
    # Generate cross-industry report
    print("\n=== Cross-Industry Report ===")
    report = analytics_system.generate_cross_industry_report()
    
    print(f"Total 5G Deployments: {report['total_deployments']}")
    print(f"Industries Covered: {report['industries_covered']}")
    
    print("\nTechnology Trends:")
    for i, trend in enumerate(report['technology_trends'], 1):
        print(f"  {i}. {trend}")
        
    print("\nIndustry Recommendations:")
    for i, rec in enumerate(report['recommendations'], 1):
        print(f"  {i}. {rec}")
        
    print("\nIndustry Performance Comparison:")
    comparison = report['performance_comparison']
    print(f"  Highest Performance: {comparison['highest_performance_industry'].title()}")
    print(f"  Fastest Adoption: {comparison['fastest_adoption_industry'].title()}")
    print(f"  Highest ROI: {comparison['highest_roi_industry'].title()}")
    
    # Scenario simulation
    print("\n=== 5G Deployment Scenario Simulation ===")
    
    scenarios = [
        {
            'name': 'Private Network - High Investment',
            'network_type': 'private',
            'coverage_area_km2': 10.0,
            'connected_devices': 50000,
            'investment_amount': 10000000
        },
        {
            'name': 'Hybrid Network - Medium Investment', 
            'network_type': 'hybrid',
            'coverage_area_km2': 15.0,
            'connected_devices': 30000,
            'investment_amount': 6000000
        },
        {
            'name': 'Public Network - Low Investment',
            'network_type': 'public',
            'coverage_area_km2': 25.0,
            'connected_devices': 20000,
            'investment_amount': 2000000
        }
    ]
    
    simulation = analytics_system.simulate_5g_deployment_scenario(
        IndustryType.MANUFACTURING, scenarios
    )
    
    print(f"Scenario Simulation for {simulation['industry_type'].title()} Industry:")
    print(f"Recommended Scenario: {simulation['recommended_scenario']}")
    
    for scenario_name, results in simulation['simulation_results'].items():
        roi = results['roi_projection']
        print(f"\n{scenario_name}:")
        print(f"  Investment: ${roi['investment_amount']:,}")
        print(f"  Annual Benefit: ${roi['annual_benefit']:,}")
        print(f"  ROI: {roi['roi_percentage']:.1f}%")
        print(f"  Payback Period: {roi['payback_period_years']:.1f} years")
        
    return analytics_system

# Run demonstration
if __name__ == "__main__":
    demo_analytics = run_5g_industry_analytics_demo()