Resiliencia Organizacional

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import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json

class OrganizationalResilienceManagement:
    def __init__(self):
        self.resilience_framework = {}
        self.resilience_assessments = {}
        self.resilience_metrics = {}
        self.improvement_plans = {}
        self.crisis_scenarios = {}
        self.resilience_training = {}
    
    def create_resilience_framework(self, framework_id, framework_config):
        """Crear marco de resiliencia organizacional"""
        self.resilience_framework[framework_id] = {
            'framework_id': framework_id,
            'name': framework_config['name'],
            'description': framework_config['description'],
            'dimensions': framework_config.get('dimensions', []),
            'capabilities': framework_config.get('capabilities', []),
            'indicators': framework_config.get('indicators', []),
            'assessment_criteria': framework_config.get('assessment_criteria', []),
            'improvement_areas': framework_config.get('improvement_areas', []),
            'maturity_levels': framework_config.get('maturity_levels', []),
            'created_date': datetime.now()
        }
    
    def conduct_resilience_assessment(self, assessment_id, assessment_config):
        """Realizar evaluación de resiliencia"""
        self.resilience_assessments[assessment_id] = {
            'assessment_id': assessment_id,
            'framework_id': assessment_config['framework_id'],
            'assessment_date': datetime.now(),
            'assessor': assessment_config['assessor'],
            'scope': assessment_config['scope'],
            'dimensions_assessed': assessment_config.get('dimensions_assessed', []),
            'scores': {},
            'strengths': [],
            'weaknesses': [],
            'opportunities': [],
            'threats': [],
            'overall_score': 0,
            'maturity_level': 'initial',
            'recommendations': []
        }
        
        # Calcular scores por dimensión
        for dimension in assessment_config.get('dimensions_assessed', []):
            dimension_score = self.calculate_dimension_score(dimension, assessment_config)
            self.resilience_assessments[assessment_id]['scores'][dimension] = dimension_score
        
        # Calcular score general
        if self.resilience_assessments[assessment_id]['scores']:
            overall_score = sum(self.resilience_assessments[assessment_id]['scores'].values()) / len(self.resilience_assessments[assessment_id]['scores'])
            self.resilience_assessments[assessment_id]['overall_score'] = overall_score
        
        # Determinar nivel de madurez
        maturity_level = self.determine_maturity_level(overall_score)
        self.resilience_assessments[assessment_id]['maturity_level'] = maturity_level
        
        # Generar recomendaciones
        recommendations = self.generate_resilience_recommendations(assessment_id)
        self.resilience_assessments[assessment_id]['recommendations'] = recommendations
        
        return assessment_id
    
    def calculate_dimension_score(self, dimension, assessment_config):
        """Calcular score de dimensión específica"""
        dimension_scores = {
            'operational': {
                'continuity_planning': assessment_config.get('continuity_planning_score', 0),
                'process_flexibility': assessment_config.get('process_flexibility_score', 0),
                'redundancy': assessment_config.get('redundancy_score', 0),
                'automation': assessment_config.get('automation_score', 0)
            },
            'technological': {
                'system_architecture': assessment_config.get('system_architecture_score', 0),
                'cybersecurity': assessment_config.get('cybersecurity_score', 0),
                'data_recovery': assessment_config.get('data_recovery_score', 0),
                'monitoring': assessment_config.get('monitoring_score', 0)
            },
            'human': {
                'adaptive_leadership': assessment_config.get('adaptive_leadership_score', 0),
                'learning_culture': assessment_config.get('learning_culture_score', 0),
                'training_development': assessment_config.get('training_development_score', 0),
                'communication': assessment_config.get('communication_score', 0)
            },
            'strategic': {
                'adaptive_vision': assessment_config.get('adaptive_vision_score', 0),
                'flexible_strategy': assessment_config.get('flexible_strategy_score', 0),
                'continuous_innovation': assessment_config.get('continuous_innovation_score', 0),
                'risk_management': assessment_config.get('risk_management_score', 0)
            }
        }
        
        if dimension in dimension_scores:
            scores = dimension_scores[dimension]
            return sum(scores.values()) / len(scores) if scores else 0
        
        return 0
    
    def determine_maturity_level(self, overall_score):
        """Determinar nivel de madurez basado en score"""
        if overall_score >= 90:
            return 'optimizing'
        elif overall_score >= 75:
            return 'managed'
        elif overall_score >= 60:
            return 'defined'
        elif overall_score >= 40:
            return 'repeatable'
        else:
            return 'initial'
    
    def generate_resilience_recommendations(self, assessment_id):
        """Generar recomendaciones de resiliencia"""
        if assessment_id not in self.resilience_assessments:
            return []
        
        assessment = self.resilience_assessments[assessment_id]
        recommendations = []
        
        # Recomendaciones basadas en scores bajos
        for dimension, score in assessment['scores'].items():
            if score < 60:
                recommendations.append({
                    'dimension': dimension,
                    'priority': 'high',
                    'description': f"Mejorar resiliencia en {dimension} (score: {score:.1f})",
                    'action': f"Develop {dimension} resilience capabilities"
                })
            elif score < 75:
                recommendations.append({
                    'dimension': dimension,
                    'priority': 'medium',
                    'description': f"Optimizar resiliencia en {dimension} (score: {score:.1f})",
                    'action': f"Enhance {dimension} resilience capabilities"
                })
        
        # Recomendaciones basadas en nivel de madurez
        maturity_level = assessment['maturity_level']
        if maturity_level == 'initial':
            recommendations.append({
                'dimension': 'overall',
                'priority': 'critical',
                'description': 'Establecer fundamentos de resiliencia organizacional',
                'action': 'Develop basic resilience framework and capabilities'
            })
        elif maturity_level == 'repeatable':
            recommendations.append({
                'dimension': 'overall',
                'priority': 'high',
                'description': 'Estandarizar procesos de resiliencia',
                'action': 'Standardize resilience processes and procedures'
            })
        elif maturity_level == 'defined':
            recommendations.append({
                'dimension': 'overall',
                'priority': 'medium',
                'description': 'Gestionar y medir resiliencia',
                'action': 'Implement resilience management and measurement'
            })
        elif maturity_level == 'managed':
            recommendations.append({
                'dimension': 'overall',
                'priority': 'low',
                'description': 'Optimizar resiliencia organizacional',
                'action': 'Optimize organizational resilience capabilities'
            })
        
        return recommendations
    
    def create_improvement_plan(self, plan_id, plan_config):
        """Crear plan de mejora de resiliencia"""
        self.improvement_plans[plan_id] = {
            'plan_id': plan_id,
            'name': plan_config['name'],
            'description': plan_config['description'],
            'assessment_id': plan_config['assessment_id'],
            'improvement_areas': plan_config.get('improvement_areas', []),
            'objectives': plan_config.get('objectives', []),
            'actions': plan_config.get('actions', []),
            'timeline': plan_config.get('timeline', {}),
            'resources': plan_config.get('resources', []),
            'success_metrics': plan_config.get('success_metrics', []),
            'status': 'draft',
            'created_date': datetime.now(),
            'last_updated': datetime.now()
        }
    
    def track_improvement_progress(self, plan_id, progress_data):
        """Rastrear progreso de mejora"""
        if plan_id not in self.improvement_plans:
            return False
        
        plan = self.improvement_plans[plan_id]
        
        progress_entry = {
            'progress_id': f"PROG-{len(plan.get('progress_entries', [])) + 1}",
            'date': datetime.now(),
            'completed_actions': progress_data.get('completed_actions', []),
            'in_progress_actions': progress_data.get('in_progress_actions', []),
            'blocked_actions': progress_data.get('blocked_actions', []),
            'metrics_achieved': progress_data.get('metrics_achieved', {}),
            'challenges': progress_data.get('challenges', []),
            'next_steps': progress_data.get('next_steps', []),
            'overall_progress': progress_data.get('overall_progress', 0)
        }
        
        if 'progress_entries' not in plan:
            plan['progress_entries'] = []
        
        plan['progress_entries'].append(progress_entry)
        
        # Actualizar estado del plan
        if progress_entry['overall_progress'] >= 100:
            plan['status'] = 'completed'
        elif progress_entry['overall_progress'] > 0:
            plan['status'] = 'in_progress'
        
        plan['last_updated'] = datetime.now()
        
        return True
    
    def create_crisis_scenario(self, scenario_id, scenario_config):
        """Crear escenario de crisis para pruebas"""
        self.crisis_scenarios[scenario_id] = {
            'scenario_id': scenario_id,
            'name': scenario_config['name'],
            'description': scenario_config['description'],
            'crisis_type': scenario_config['crisis_type'],
            'severity_level': scenario_config['severity_level'],
            'affected_systems': scenario_config.get('affected_systems', []),
            'affected_processes': scenario_config.get('affected_processes', []),
            'timeline': scenario_config.get('timeline', {}),
            'challenges': scenario_config.get('challenges', []),
            'success_criteria': scenario_config.get('success_criteria', []),
            'learning_objectives': scenario_config.get('learning_objectives', []),
            'created_date': datetime.now()
        }
    
    def conduct_resilience_training(self, training_id, training_config):
        """Realizar entrenamiento de resiliencia"""
        self.resilience_training[training_id] = {
            'training_id': training_id,
            'name': training_config['name'],
            'description': training_config['description'],
            'training_type': training_config['training_type'],
            'participants': training_config.get('participants', []),
            'scenario_id': training_config.get('scenario_id'),
            'duration_hours': training_config.get('duration_hours', 4),
            'scheduled_date': training_config.get('scheduled_date'),
            'status': 'scheduled',
            'results': {},
            'created_date': datetime.now()
        }
    
    def execute_resilience_training(self, training_id, execution_data):
        """Ejecutar entrenamiento de resiliencia"""
        if training_id not in self.resilience_training:
            return False
        
        training = self.resilience_training[training_id]
        
        training['status'] = 'in_progress'
        training['start_time'] = datetime.now()
        
        # Simular ejecución de entrenamiento
        training_results = {
            'training_id': training_id,
            'start_time': training['start_time'],
            'end_time': training['start_time'] + timedelta(hours=training['duration_hours']),
            'participants_attended': len(execution_data.get('participants_attended', [])),
            'scenarios_completed': len(execution_data.get('scenarios_completed', [])),
            'learning_objectives_met': len(execution_data.get('learning_objectives_met', [])),
            'participant_feedback': execution_data.get('participant_feedback', {}),
            'instructor_feedback': execution_data.get('instructor_feedback', {}),
            'improvement_areas': execution_data.get('improvement_areas', []),
            'overall_success': execution_data.get('overall_success', False),
            'effectiveness_score': execution_data.get('effectiveness_score', 0)
        }
        
        training['results'] = training_results
        training['status'] = 'completed'
        training['end_time'] = training_results['end_time']
        
        return True
    
    def calculate_resilience_metrics(self, framework_id):
        """Calcular métricas de resiliencia"""
        if framework_id not in self.resilience_framework:
            return None
        
        # Obtener evaluaciones del framework
        framework_assessments = [a for a in self.resilience_assessments.values() if a['framework_id'] == framework_id]
        
        if not framework_assessments:
            return None
        
        # Calcular métricas
        total_assessments = len(framework_assessments)
        avg_overall_score = sum(a['overall_score'] for a in framework_assessments) / total_assessments
        
        # Distribución por nivel de madurez
        maturity_distribution = {}
        for assessment in framework_assessments:
            maturity = assessment['maturity_level']
            if maturity not in maturity_distribution:
                maturity_distribution[maturity] = 0
            maturity_distribution[maturity] += 1
        
        # Tendencias de mejora
        recent_assessments = [a for a in framework_assessments if a['assessment_date'] >= datetime.now() - timedelta(days=90)]
        if len(recent_assessments) >= 2:
            recent_scores = [a['overall_score'] for a in recent_assessments]
            trend = 'improving' if recent_scores[-1] > recent_scores[0] else 'declining'
        else:
            trend = 'stable'
        
        # Obtener planes de mejora activos
        active_plans = [p for p in self.improvement_plans.values() if p['status'] == 'in_progress']
        
        # Obtener entrenamientos recientes
        recent_training = [t for t in self.resilience_training.values() if t.get('scheduled_date', datetime.min) >= datetime.now() - timedelta(days=30)]
        
        metrics = {
            'framework_id': framework_id,
            'total_assessments': total_assessments,
            'avg_overall_score': avg_overall_score,
            'maturity_distribution': maturity_distribution,
            'trend': trend,
            'active_improvement_plans': len(active_plans),
            'recent_training_sessions': len(recent_training),
            'resilience_level': self.determine_resilience_level(avg_overall_score)
        }
        
        return metrics
    
    def determine_resilience_level(self, avg_score):
        """Determinar nivel de resiliencia basado en score promedio"""
        if avg_score >= 85:
            return 'highly_resilient'
        elif avg_score >= 70:
            return 'resilient'
        elif avg_score >= 55:
            return 'moderately_resilient'
        elif avg_score >= 40:
            return 'developing_resilience'
        else:
            return 'low_resilience'
    
    def generate_resilience_report(self, framework_id):
        """Generar reporte de resiliencia"""
        if framework_id not in self.resilience_framework:
            return None
        
        framework = self.resilience_framework[framework_id]
        metrics = self.calculate_resilience_metrics(framework_id)
        
        if not metrics:
            return None
        
        # Obtener evaluaciones recientes
        recent_assessments = [a for a in self.resilience_assessments.values() if a['framework_id'] == framework_id and a['assessment_date'] >= datetime.now() - timedelta(days=90)]
        
        # Obtener planes de mejora
        improvement_plans = [p for p in self.improvement_plans.values() if p['assessment_id'] in [a['assessment_id'] for a in recent_assessments]]
        
        # Generar recomendaciones estratégicas
        strategic_recommendations = self.generate_strategic_recommendations(metrics, recent_assessments, improvement_plans)
        
        report = {
            'framework_id': framework_id,
            'framework_name': framework['name'],
            'report_date': datetime.now(),
            'metrics': metrics,
            'recent_assessments': len(recent_assessments),
            'improvement_plans': len(improvement_plans),
            'strategic_recommendations': strategic_recommendations,
            'next_steps': self.generate_resilience_next_steps(metrics, strategic_recommendations)
        }
        
        return report
    
    def generate_strategic_recommendations(self, metrics, assessments, improvement_plans):
        """Generar recomendaciones estratégicas"""
        recommendations = []
        
        # Recomendaciones basadas en nivel de resiliencia
        resilience_level = metrics['resilience_level']
        
        if resilience_level == 'low_resilience':
            recommendations.append({
                'type': 'foundational_development',
                'priority': 'critical',
                'description': 'Desarrollar fundamentos de resiliencia organizacional',
                'action': 'Implement comprehensive resilience framework'
            })
        elif resilience_level == 'developing_resilience':
            recommendations.append({
                'type': 'capability_building',
                'priority': 'high',
                'description': 'Construir capacidades de resiliencia',
                'action': 'Focus on building specific resilience capabilities'
            })
        elif resilience_level == 'moderately_resilient':
            recommendations.append({
                'type': 'optimization',
                'priority': 'medium',
                'description': 'Optimizar capacidades de resiliencia existentes',
                'action': 'Optimize and enhance current resilience capabilities'
            })
        elif resilience_level == 'resilient':
            recommendations.append({
                'type': 'continuous_improvement',
                'priority': 'low',
                'description': 'Mantener y mejorar resiliencia',
                'action': 'Maintain and continuously improve resilience'
            })
        
        # Recomendaciones basadas en tendencias
        if metrics['trend'] == 'declining':
            recommendations.append({
                'type': 'trend_reversal',
                'priority': 'high',
                'description': 'Revertir tendencia de declive en resiliencia',
                'action': 'Investigate and address factors causing decline'
            })
        
        # Recomendaciones basadas en planes de mejora
        if metrics['active_improvement_plans'] == 0:
            recommendations.append({
                'type': 'improvement_planning',
                'priority': 'medium',
                'description': 'Desarrollar planes de mejora de resiliencia',
                'action': 'Create and implement improvement plans'
            })
        
        return recommendations
    
    def generate_resilience_next_steps(self, metrics, recommendations):
        """Generar próximos pasos para resiliencia"""
        next_steps = []
        
        critical_recommendations = [r for r in recommendations if r['priority'] == 'critical']
        high_recommendations = [r for r in recommendations if r['priority'] == 'high']
        
        if critical_recommendations:
            next_steps.append({
                'timeline': 'Immediate',
                'actions': [r['action'] for r in critical_recommendations],
                'priority': 'Critical'
            })
        
        if high_recommendations:
            next_steps.append({
                'timeline': 'Within 1 month',
                'actions': [r['action'] for r in high_recommendations],
                'priority': 'High'
            })
        
        medium_recommendations = [r for r in recommendations if r['priority'] == 'medium']
        if medium_recommendations:
            next_steps.append({
                'timeline': 'Within 3 months',
                'actions': [r['action'] for r in medium_recommendations],
                'priority': 'Medium'
            })
        
        return next_steps

# Ejemplo de uso
resilience_mgmt = OrganizationalResilienceManagement()

# Crear marco de resiliencia
resilience_mgmt.create_resilience_framework('FRAMEWORK-001', {
    'name': 'Organizational Resilience Framework 2025',
    'description': 'Marco de resiliencia organizacional para 2025',
    'dimensions': ['operational', 'technological', 'human', 'strategic'],
    'capabilities': [
        'Continuity Planning',
        'Crisis Management',
        'Adaptive Leadership',
        'Innovation Culture'
    ],
    'maturity_levels': ['initial', 'repeatable', 'defined', 'managed', 'optimizing']
})

# Realizar evaluación de resiliencia
assessment_id = resilience_mgmt.conduct_resilience_assessment('ASSESS-001', {
    'framework_id': 'FRAMEWORK-001',
    'assessor': 'Resilience Team',
    'scope': 'organization_wide',
    'dimensions_assessed': ['operational', 'technological', 'human', 'strategic'],
    'continuity_planning_score': 75,
    'process_flexibility_score': 60,
    'redundancy_score': 80,
    'automation_score': 70,
    'system_architecture_score': 85,
    'cybersecurity_score': 90,
    'data_recovery_score': 75,
    'monitoring_score': 80,
    'adaptive_leadership_score': 65,
    'learning_culture_score': 70,
    'training_development_score': 60,
    'communication_score': 75,
    'adaptive_vision_score': 80,
    'flexible_strategy_score': 70,
    'continuous_innovation_score': 65,
    'risk_management_score': 85
})

# Crear plan de mejora
resilience_mgmt.create_improvement_plan('PLAN-001', {
    'name': 'Resilience Improvement Plan 2025',
    'description': 'Plan de mejora de resiliencia para 2025',
    'assessment_id': assessment_id,
    'improvement_areas': ['human', 'strategic'],
    'objectives': [
        'Improve adaptive leadership capabilities',
        'Enhance learning culture',
        'Strengthen strategic flexibility'
    ],
    'timeline': {
        'start_date': datetime.now(),
        'end_date': datetime.now() + timedelta(days=180)
    }
})

# Crear escenario de crisis
resilience_mgmt.create_crisis_scenario('SCENARIO-001', {
    'name': 'Cyber Attack Simulation',
    'description': 'Simulación de ataque cibernético masivo',
    'crisis_type': 'cyber_attack',
    'severity_level': 'high',
    'affected_systems': ['Order Processing', 'Customer Database'],
    'challenges': ['Data breach', 'System downtime', 'Customer impact'],
    'learning_objectives': [
        'Test incident response procedures',
        'Evaluate communication effectiveness',
        'Assess recovery capabilities'
    ]
})

# Generar reporte
report = resilience_mgmt.generate_resilience_report('FRAMEWORK-001')
print(f"Reporte de resiliencia: {report['framework_name']}")
print(f"Nivel de resiliencia: {report['metrics']['resilience_level']}")
print(f"Score promedio: {report['metrics']['avg_overall_score']:.1f}")
print(f"Recomendaciones estratégicas: {len(report['strategic_recommendations'])}")

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class CrisisAdaptationManagement:
    def __init__(self):
        self.crisis_events = {}
        self.adaptation_strategies = {}
        self.learning_captures = {}
        self.organizational_changes = {}
    
    def document_crisis_event(self, event_id, event_data):
        """Documentar evento de crisis"""
        self.crisis_events[event_id] = {
            'event_id': event_id,
            'title': event_data['title'],
            'description': event_data['description'],
            'crisis_type': event_data['crisis_type'],
            'severity': event_data['severity'],
            'start_date': event_data['start_date'],
            'end_date': event_data.get('end_date'),
            'impact_assessment': event_data.get('impact_assessment', {}),
            'response_actions': event_data.get('response_actions', []),
            'lessons_learned': [],
            'adaptation_opportunities': [],
            'status': 'active'
        }
    
    def capture_lessons_learned(self, event_id, lessons_data):
        """Capturar lecciones aprendidas"""
        if event_id not in self.crisis_events:
            return False
        
        event = self.crisis_events[event_id]
        
        lesson = {
            'lesson_id': f"LESSON-{len(event['lessons_learned']) + 1}",
            'category': lessons_data['category'],
            'description': lessons_data['description'],
            'impact': lessons_data['impact'],
            'recommendations': lessons_data.get('recommendations', []),
            'captured_by': lessons_data['captured_by'],
            'captured_date': datetime.now(),
            'status': 'new'
        }
        
        event['lessons_learned'].append(lesson)
        
        return True
    
    def identify_adaptation_opportunities(self, event_id, opportunities_data):
        """Identificar oportunidades de adaptación"""
        if event_id not in self.crisis_events:
            return False
        
        event = self.crisis_events[event_id]
        
        opportunity = {
            'opportunity_id': f"OPP-{len(event['adaptation_opportunities']) + 1}",
            'type': opportunities_data['type'],
            'description': opportunities_data['description'],
            'potential_benefits': opportunities_data.get('potential_benefits', []),
            'implementation_effort': opportunities_data.get('implementation_effort', 'medium'),
            'timeline': opportunities_data.get('timeline', '3-6 months'),
            'identified_by': opportunities_data['identified_by'],
            'identified_date': datetime.now(),
            'status': 'identified'
        }
        
        event['adaptation_opportunities'].append(opportunity)
        
        return True
    
    def implement_organizational_change(self, change_id, change_data):
        """Implementar cambio organizacional"""
        self.organizational_changes[change_id] = {
            'change_id': change_id,
            'title': change_data['title'],
            'description': change_data['description'],
            'change_type': change_data['change_type'],
            'scope': change_data['scope'],
            'objectives': change_data.get('objectives', []),
            'implementation_plan': change_data.get('implementation_plan', []),
            'success_metrics': change_data.get('success_metrics', []),
            'stakeholders': change_data.get('stakeholders', []),
            'start_date': change_data.get('start_date', datetime.now()),
            'target_completion': change_data.get('target_completion'),
            'status': 'planned',
            'progress': 0,
            'created_date': datetime.now()
        }
    
    def track_change_progress(self, change_id, progress_data):
        """Rastrear progreso de cambio"""
        if change_id not in self.organizational_changes:
            return False
        
        change = self.organizational_changes[change_id]
        
        progress_entry = {
            'progress_id': f"PROG-{len(change.get('progress_entries', [])) + 1}",
            'date': datetime.now(),
            'progress_percentage': progress_data['progress_percentage'],
            'milestones_achieved': progress_data.get('milestones_achieved', []),
            'challenges': progress_data.get('challenges', []),
            'next_steps': progress_data.get('next_steps', []),
            'updated_by': progress_data.get('updated_by', 'system')
        }
        
        if 'progress_entries' not in change:
            change['progress_entries'] = []
        
        change['progress_entries'].append(progress_entry)
        change['progress'] = progress_data['progress_percentage']
        
        # Actualizar estado
        if change['progress'] >= 100:
            change['status'] = 'completed'
        elif change['progress'] > 0:
            change['status'] = 'in_progress'
        
        return True
    
    def generate_adaptation_report(self, event_id):
        """Generar reporte de adaptación"""
        if event_id not in self.crisis_events:
            return None
        
        event = self.crisis_events[event_id]
        
        # Obtener cambios relacionados
        related_changes = [c for c in self.organizational_changes.values() if event_id in c.get('related_events', [])]
        
        report = {
            'event_id': event_id,
            'event_title': event['title'],
            'report_date': datetime.now(),
            'crisis_type': event['crisis_type'],
            'severity': event['severity'],
            'lessons_learned_count': len(event['lessons_learned']),
            'adaptation_opportunities_count': len(event['adaptation_opportunities']),
            'related_changes_count': len(related_changes),
            'adaptation_effectiveness': self.calculate_adaptation_effectiveness(event, related_changes),
            'recommendations': self.generate_adaptation_recommendations(event, related_changes)
        }
        
        return report
    
    def calculate_adaptation_effectiveness(self, event, related_changes):
        """Calcular efectividad de adaptación"""
        if not related_changes:
            return 0
        
        # Calcular métricas de efectividad
        completed_changes = len([c for c in related_changes if c['status'] == 'completed'])
        total_changes = len(related_changes)
        
        completion_rate = (completed_changes / total_changes * 100) if total_changes > 0 else 0
        
        # Calcular score de efectividad basado en lecciones aprendidas
        lessons_count = len(event['lessons_learned'])
        opportunities_count = len(event['adaptation_opportunities'])
        
        effectiveness_score = (completion_rate * 0.6) + (min(lessons_count * 10, 40)) + (min(opportunities_count * 5, 20))
        
        return min(effectiveness_score, 100)
    
    def generate_adaptation_recommendations(self, event, related_changes):
        """Generar recomendaciones de adaptación"""
        recommendations = []
        
        # Recomendaciones basadas en lecciones aprendidas
        if len(event['lessons_learned']) > 5:
            recommendations.append({
                'type': 'lesson_implementation',
                'priority': 'high',
                'description': 'Implementar lecciones aprendidas identificadas',
                'action': 'Create action plans for key lessons learned'
            })
        
        # Recomendaciones basadas en oportunidades
        if len(event['adaptation_opportunities']) > 3:
            recommendations.append({
                'type': 'opportunity_exploitation',
                'priority': 'medium',
                'description': 'Explotar oportunidades de adaptación identificadas',
                'action': 'Prioritize and implement adaptation opportunities'
            })
        
        # Recomendaciones basadas en cambios
        if not related_changes:
            recommendations.append({
                'type': 'change_initiation',
                'priority': 'high',
                'description': 'Iniciar cambios organizacionales basados en la crisis',
                'action': 'Identify and implement necessary organizational changes'
            })
        
        return recommendations

# Ejemplo de uso
adaptation_mgmt = CrisisAdaptationManagement()

# Documentar evento de crisis
adaptation_mgmt.document_crisis_event('EVENT-001', {
    'title': 'Major Cyber Attack',
    'description': 'Ataque cibernético masivo que afectó múltiples sistemas',
    'crisis_type': 'cyber_attack',
    'severity': 'high',
    'start_date': datetime.now() - timedelta(days=30),
    'end_date': datetime.now() - timedelta(days=25),
    'impact_assessment': {
        'financial_impact': 'high',
        'operational_impact': 'critical',
        'reputation_impact': 'medium'
    }
})

# Capturar lecciones aprendidas
adaptation_mgmt.capture_lessons_learned('EVENT-001', {
    'category': 'incident_response',
    'description': 'Necesidad de mejorar comunicación durante crisis',
    'impact': 'high',
    'recommendations': ['Mejorar canales de comunicación', 'Establecer protocolos claros'],
    'captured_by': 'Crisis Management Team'
})

# Identificar oportunidades de adaptación
adaptation_mgmt.identify_adaptation_opportunities('EVENT-001', {
    'type': 'process_improvement',
    'description': 'Automatizar procesos de respuesta a incidentes',
    'potential_benefits': ['Respuesta más rápida', 'Menor error humano'],
    'implementation_effort': 'high',
    'timeline': '6-12 months',
    'identified_by': 'IT Team'
})

# Implementar cambio organizacional
adaptation_mgmt.implement_organizational_change('CHANGE-001', {
    'title': 'Enhanced Incident Response Procedures',
    'description': 'Mejora de procedimientos de respuesta a incidentes',
    'change_type': 'process_improvement',
    'scope': 'organization_wide',
    'objectives': ['Reducir tiempo de respuesta', 'Mejorar comunicación'],
    'target_completion': datetime.now() + timedelta(days=90)
})

# Generar reporte
report = adaptation_mgmt.generate_adaptation_report('EVENT-001')
print(f"Reporte de adaptación: {report['event_title']}")
print(f"Efectividad de adaptación: {report['adaptation_effectiveness']:.1f}%")
print(f"Lecciones aprendidas: {report['lessons_learned_count']}")