Building a Resilient Remote Workforce: Transforming Organizations Through Strategic Flexibility, Digital Innovation, and Employee-Centric Approaches for Future Success

import asyncio
import json
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Callable
from dataclasses import dataclass, field
from enum import Enum
import uuid
import time
from concurrent.futures import ThreadPoolExecutor

class WorkLocation(Enum):
    FULLY_REMOTE = "fully_remote"
    HYBRID = "hybrid"
    OFFICE_BASED = "office_based"
    FLEXIBLE = "flexible"

class EmployeeStatus(Enum):
    ACTIVE = "active"
    ON_LEAVE = "on_leave"
    PART_TIME = "part_time"
    CONTRACTOR = "contractor"

class WellbeingStatus(Enum):
    THRIVING = "thriving"
    STABLE = "stable"
    AT_RISK = "at_risk"
    REQUIRES_SUPPORT = "requires_support"

@dataclass
class Employee:
    """Represents a remote workforce employee"""
    id: str
    name: str
    role: str
    department: str
    work_location: WorkLocation
    status: EmployeeStatus
    timezone: str
    skills: List[str] = field(default_factory=list)
    performance_score: float = 0.0
    engagement_score: float = 0.0
    wellbeing_status: WellbeingStatus = WellbeingStatus.STABLE
    last_check_in: datetime = field(default_factory=datetime.now)
    productivity_metrics: Dict[str, float] = field(default_factory=dict)
    training_completed: List[str] = field(default_factory=list)
    
@dataclass
class WorkflowMetrics:
    """Tracks workforce performance and well-being metrics"""
    timestamp: datetime
    employee_id: str
    productivity_score: float
    collaboration_index: float
    work_life_balance_score: float
    stress_level: float
    engagement_level: float
    task_completion_rate: float
    communication_frequency: float
    
@dataclass
class TeamCollaboration:
    """Represents team collaboration patterns and effectiveness"""
    team_id: str
    team_name: str
    members: List[str]
    collaboration_score: float
    communication_patterns: Dict[str, Any]
    project_success_rate: float
    meeting_efficiency: float
    knowledge_sharing_index: float
    
class ResilientWorkforceManager:
    """Comprehensive remote workforce management system"""
    
    def __init__(self, organization_id: str):
        self.organization_id = organization_id
        self.employees: Dict[str, Employee] = {}
        self.teams: Dict[str, TeamCollaboration] = {}
        self.metrics_history: List[WorkflowMetrics] = []
        
        # AI-powered components
        self.performance_analyzer = PerformanceAnalyzer()
        self.wellbeing_monitor = WellbeingMonitor()
        self.collaboration_optimizer = CollaborationOptimizer()
        
        # Technology and infrastructure
        self.technology_stack = {
            "communication_platforms": [],
            "productivity_tools": [],
            "security_systems": [],
            "analytics_platforms": []
        }
        
        # Policies and frameworks
        self.remote_work_policies = {}
        self.performance_standards = {}
        self.wellbeing_programs = []
        
        # Training and development
        self.training_programs = {}
        self.skill_development_paths = {}
        
        print(f"Resilient Workforce Manager initialized for {organization_id}")
        
    def onboard_employee(self, employee: Employee) -> Dict[str, Any]:
        """Comprehensive employee onboarding for remote work"""
        print(f"Onboarding employee: {employee.name} ({employee.role})")
        
        # Add employee to system
        self.employees[employee.id] = employee
        
        # Create personalized onboarding plan
        onboarding_plan = self._create_onboarding_plan(employee)
        
        # Set up technology access and training
        tech_setup = self._configure_employee_technology(employee)
        
        # Establish mentorship and support networks
        support_network = self._create_support_network(employee)
        
        # Initialize performance tracking
        self._initialize_performance_tracking(employee)
        
        onboarding_result = {
            "employee_id": employee.id,
            "onboarding_plan": onboarding_plan,
            "technology_setup": tech_setup,
            "support_network": support_network,
            "expected_completion_date": (datetime.now() + timedelta(days=30)).isoformat(),
            "success_metrics": {
                "technology_proficiency": "target_85%",
                "role_competency": "target_80%",
                "team_integration": "target_90%",
                "wellbeing_score": "target_75%"
            }
        }
        
        print(f"Employee {employee.name} onboarded successfully")
        return onboarding_result
        
    def _create_onboarding_plan(self, employee: Employee) -> Dict[str, Any]:
        """Create personalized onboarding plan based on role and work location"""
        base_plan = {
            "week_1": [
                "Technology setup and security training",
                "Company culture and values orientation",
                "Role-specific training and expectations",
                "Introduction to team members and key stakeholders"
            ],
            "week_2": [
                "Deep dive into role responsibilities",
                "Project assignment and goal setting",
                "Communication protocols and meeting schedules",
                "Performance measurement and feedback processes"
            ],
            "week_3_4": [
                "Hands-on project work with mentorship",
                "Skills assessment and development planning",
                "Wellbeing check-ins and support resource introduction",
                "Integration into team workflows and processes"
            ]
        }
        
        # Customize based on work location and role
        if employee.work_location in [WorkLocation.FULLY_REMOTE, WorkLocation.HYBRID]:
            base_plan["week_1"].extend([
                "Remote work best practices training",
                "Home office setup guidance and stipend allocation",
                "Time zone coordination and async communication training"
            ])
            
        # Add role-specific elements
        if "manager" in employee.role.lower():
            base_plan["week_2"].append("Remote team management training")
            base_plan["week_3_4"].append("Leadership in distributed teams workshop")
            
        return base_plan
        
    def _configure_employee_technology(self, employee: Employee) -> Dict[str, Any]:
        """Configure technology stack for employee based on role and location"""
        tech_config = {
            "hardware_allocation": {
                "laptop": "high_performance_model",
                "monitor": "dual_screen_setup" if employee.work_location != WorkLocation.OFFICE_BASED else "single_screen",
                "accessories": ["wireless_headset", "ergonomic_keyboard", "mouse"],
                "home_office_stipend": 1500 if employee.work_location == WorkLocation.FULLY_REMOTE else 750
            },
            "software_access": {
                "communication": ["teams", "slack", "zoom"],
                "productivity": ["office_365", "project_management_tool", "time_tracking"],
                "security": ["vpn_access", "password_manager", "endpoint_protection"],
                "role_specific": self._get_role_specific_software(employee.role)
            },
            "training_requirements": [
                "cybersecurity_fundamentals",
                "remote_collaboration_tools",
                "data_privacy_and_compliance",
                "productivity_optimization"
            ],
            "setup_timeline": "5_business_days",
            "technical_support_contact": "remote_support_team@company.com"
        }
        
        return tech_config
        
    def analyze_workforce_resilience(self) -> Dict[str, Any]:
        """Comprehensive analysis of workforce resilience and adaptation capacity"""
        resilience_analysis = {
            "overall_resilience_score": 0,
            "department_analysis": {},
            "risk_factors": [],
            "strength_areas": [],
            "improvement_recommendations": []
        }
        
        # Analyze overall workforce metrics
        total_employees = len(self.employees)
        if total_employees == 0:
            return {"error": "No employees to analyze"}
            
        # Calculate resilience indicators
        engagement_scores = [emp.engagement_score for emp in self.employees.values()]
        performance_scores = [emp.performance_score for emp in self.employees.values()]
        wellbeing_distribution = self._analyze_wellbeing_distribution()
        
        avg_engagement = np.mean(engagement_scores) if engagement_scores else 0
        avg_performance = np.mean(performance_scores) if performance_scores else 0
        
        # Department-level analysis
        departments = {}
        for employee in self.employees.values():
            dept = employee.department
            if dept not in departments:
                departments[dept] = []
            departments[dept].append(employee)
            
        for dept, employees in departments.items():
            dept_engagement = np.mean([emp.engagement_score for emp in employees])
            dept_performance = np.mean([emp.performance_score for emp in employees])
            dept_wellbeing = self._calculate_department_wellbeing(employees)
            
            resilience_analysis["department_analysis"][dept] = {
                "employee_count": len(employees),
                "avg_engagement": dept_engagement,
                "avg_performance": dept_performance,
                "wellbeing_score": dept_wellbeing,
                "resilience_score": (dept_engagement + dept_performance + dept_wellbeing) / 3
            }
            
        # Identify risk factors and strengths
        if avg_engagement < 70:
            resilience_analysis["risk_factors"].append({
                "factor": "Low employee engagement",
                "severity": "high",
                "current_score": avg_engagement,
                "recommended_actions": [
                    "Implement regular check-ins and feedback sessions",
                    "Enhance recognition and rewards programs",
                    "Provide career development opportunities"
                ]
            })
            
        if wellbeing_distribution["at_risk"] + wellbeing_distribution["requires_support"] > 0.25:
            resilience_analysis["risk_factors"].append({
                "factor": "Employee wellbeing concerns",
                "severity": "high",
                "affected_percentage": (wellbeing_distribution["at_risk"] + wellbeing_distribution["requires_support"]) * 100,
                "recommended_actions": [
                    "Expand mental health and wellness programs",
                    "Implement flexible work arrangements",
                    "Provide stress management resources"
                ]
            })
            
        # Calculate overall resilience score
        resilience_factors = [
            avg_engagement / 100,
            avg_performance / 100,
            wellbeing_distribution["thriving"] + wellbeing_distribution["stable"],
            self._calculate_adaptability_score(),
            self._calculate_collaboration_effectiveness()
        ]
        
        resilience_analysis["overall_resilience_score"] = np.mean(resilience_factors) * 100
        
        return resilience_analysis
        
    def implement_wellbeing_program(self, program_type: str = "comprehensive") -> Dict[str, Any]:
        """Implement comprehensive employee wellbeing program"""
        wellbeing_program = {
            "program_id": f"wellbeing_{uuid.uuid4()}",
            "program_type": program_type,
            "launch_date": datetime.now(),
            "components": [],
            "target_metrics": {},
            "expected_outcomes": []
        }
        
        if program_type == "comprehensive":
            wellbeing_program["components"] = [
                {
                    "name": "Mental Health Support",
                    "description": "24/7 counseling services, stress management workshops, mindfulness training",
                    "delivery_method": "virtual_and_app_based",
                    "frequency": "ongoing"
                },
                {
                    "name": "Work-Life Balance Initiatives",
                    "description": "Flexible scheduling, no-meeting days, digital wellness boundaries",
                    "delivery_method": "policy_implementation",
                    "frequency": "ongoing"
                },
                {
                    "name": "Physical Wellness Programs",
                    "description": "Virtual fitness classes, ergonomic assessments, health screenings",
                    "delivery_method": "virtual_platforms",
                    "frequency": "weekly"
                },
                {
                    "name": "Social Connection Initiatives",
                    "description": "Virtual coffee chats, team building activities, interest-based groups",
                    "delivery_method": "facilitated_events",
                    "frequency": "bi_weekly"
                },
                {
                    "name": "Professional Development",
                    "description": "Skills training, career coaching, leadership development",
                    "delivery_method": "online_learning_platforms",
                    "frequency": "monthly"
                }
            ]
            
            wellbeing_program["target_metrics"] = {
                "wellbeing_score_improvement": "20%",
                "stress_level_reduction": "25%",
                "engagement_score_increase": "15%",
                "retention_rate_improvement": "10%",
                "sick_leave_reduction": "30%"
            }
            
        # Implement program tracking
        self._initialize_wellbeing_tracking(wellbeing_program)
        
        return wellbeing_program
        
    def optimize_collaboration_patterns(self) -> Dict[str, Any]:
        """Analyze and optimize team collaboration patterns"""
        optimization_results = {
            "current_collaboration_score": 0,
            "identified_issues": [],
            "optimization_strategies": [],
            "implementation_plan": {},
            "expected_improvements": {}
        }
        
        # Analyze current collaboration patterns
        collaboration_data = self._analyze_current_collaboration()
        
        optimization_results["current_collaboration_score"] = collaboration_data["overall_score"]
        
        # Identify collaboration issues
        if collaboration_data["meeting_efficiency"] < 70:
            optimization_results["identified_issues"].append({
                "issue": "Low meeting efficiency",
                "current_score": collaboration_data["meeting_efficiency"],
                "impact": "Reduced productivity and employee satisfaction"
            })
            
            optimization_results["optimization_strategies"].append({
                "strategy": "Meeting Optimization Program",
                "description": "Implement structured meeting formats, time limits, and clear agendas",
                "expected_improvement": "25% increase in meeting efficiency"
            })
            
        if collaboration_data["cross_team_communication"] < 60:
            optimization_results["identified_issues"].append({
                "issue": "Limited cross-team communication",
                "current_score": collaboration_data["cross_team_communication"],
                "impact": "Reduced innovation and knowledge sharing"
            })
            
            optimization_results["optimization_strategies"].append({
                "strategy": "Cross-Team Collaboration Initiative",
                "description": "Regular inter-departmental projects and communication channels",
                "expected_improvement": "30% increase in cross-team collaboration"
            })
            
        # Create implementation plan
        optimization_results["implementation_plan"] = {
            "phase_1": {
                "duration": "30_days",
                "activities": [
                    "Baseline measurement and team assessment",
                    "Training program launch for managers",
                    "Technology platform optimization"
                ]
            },
            "phase_2": {
                "duration": "60_days",
                "activities": [
                    "Roll out new collaboration practices",
                    "Monitor and adjust strategies",
                    "Gather employee feedback"
                ]
            },
            "phase_3": {
                "duration": "30_days",
                "activities": [
                    "Measure improvements and ROI",
                    "Refine and standardize successful practices",
                    "Plan for continuous improvement"
                ]
            }
        }
        
        return optimization_results
        
    def predict_workforce_trends(self, time_horizon_months: int = 12) -> Dict[str, Any]:
        """Predict workforce trends and potential challenges"""
        predictions = {
            "time_horizon": f"{time_horizon_months}_months",
            "workforce_size_prediction": {},
            "skills_gap_analysis": {},
            "retention_risk_analysis": {},
            "productivity_projections": {},
            "recommended_actions": []
        }
        
        # Workforce size predictions
        current_size = len(self.employees)
        growth_rate = self._calculate_historical_growth_rate()
        predicted_size = current_size * (1 + growth_rate) ** (time_horizon_months / 12)
        
        predictions["workforce_size_prediction"] = {
            "current_size": current_size,
            "predicted_size": int(predicted_size),
            "growth_rate": growth_rate * 100,
            "hiring_needs": max(0, int(predicted_size - current_size))
        }
        
        # Skills gap analysis
        current_skills = self._analyze_current_skills_inventory()
        future_skills_needed = self._predict_future_skills_requirements()
        
        skills_gaps = []
        for skill, future_demand in future_skills_needed.items():
            current_supply = current_skills.get(skill, 0)
            if future_demand > current_supply:
                skills_gaps.append({
                    "skill": skill,
                    "current_supply": current_supply,
                    "future_demand": future_demand,
                    "gap": future_demand - current_supply,
                    "priority": "high" if future_demand > current_supply * 1.5 else "medium"
                })
                
        predictions["skills_gap_analysis"] = {
            "total_gaps_identified": len(skills_gaps),
            "high_priority_gaps": len([g for g in skills_gaps if g["priority"] == "high"]),
            "detailed_gaps": skills_gaps[:10]  # Top 10 gaps
        }
        
        # Retention risk analysis
        at_risk_employees = self._identify_retention_risks()
        
        predictions["retention_risk_analysis"] = {
            "total_at_risk": len(at_risk_employees),
            "risk_percentage": (len(at_risk_employees) / current_size) * 100 if current_size > 0 else 0,
            "primary_risk_factors": self._analyze_retention_risk_factors(at_risk_employees),
            "estimated_turnover_cost": len(at_risk_employees) * 50000  # Average replacement cost
        }
        
        # Generate recommendations
        predictions["recommended_actions"] = self._generate_workforce_recommendations(
            predictions["skills_gap_analysis"],
            predictions["retention_risk_analysis"],
            predictions["workforce_size_prediction"]
        )
        
        return predictions
        
    def generate_comprehensive_report(self) -> Dict[str, Any]:
        """Generate comprehensive remote workforce management report"""
        report = {
            "organization_id": self.organization_id,
            "report_timestamp": datetime.now(),
            "workforce_overview": {
                "total_employees": len(self.employees),
                "work_location_distribution": self._calculate_work_location_distribution(),
                "department_distribution": self._calculate_department_distribution(),
                "employee_status_distribution": self._calculate_status_distribution()
            },
            "performance_metrics": self._generate_performance_summary(),
            "wellbeing_analysis": self._generate_wellbeing_summary(),
            "collaboration_effectiveness": self._generate_collaboration_summary(),
            "technology_utilization": self._generate_technology_summary(),
            "resilience_assessment": self.analyze_workforce_resilience(),
            "trend_predictions": self.predict_workforce_trends(6),
            "strategic_recommendations": self._generate_strategic_recommendations()
        }
        
        return report
        
    # Helper methods for data analysis and calculations
    def _analyze_wellbeing_distribution(self) -> Dict[str, float]:
        """Analyze distribution of employee wellbeing statuses"""
        total = len(self.employees)
        if total == 0:
            return {status.value: 0 for status in WellbeingStatus}
            
        distribution = {status.value: 0 for status in WellbeingStatus}
        for employee in self.employees.values():
            distribution[employee.wellbeing_status.value] += 1
            
        # Convert to percentages
        return {k: v/total for k, v in distribution.items()}
        
    def _calculate_department_wellbeing(self, employees: List[Employee]) -> float:
        """Calculate average wellbeing score for department"""
        wellbeing_scores = {
            WellbeingStatus.THRIVING: 100,
            WellbeingStatus.STABLE: 75,
            WellbeingStatus.AT_RISK: 50,
            WellbeingStatus.REQUIRES_SUPPORT: 25
        }
        
        total_score = sum(wellbeing_scores[emp.wellbeing_status] for emp in employees)
        return total_score / len(employees) if employees else 0
        
    def _calculate_adaptability_score(self) -> float:
        """Calculate workforce adaptability based on various factors"""
        # Simulate adaptability calculation
        factors = {
            "skill_diversity": 0.8,
            "technology_adoption": 0.85,
            "change_management": 0.75,
            "learning_agility": 0.9
        }
        return np.mean(list(factors.values()))
        
    def _calculate_collaboration_effectiveness(self) -> float:
        """Calculate overall collaboration effectiveness score"""
        if not self.teams:
            return 0.7  # Default moderate score
            
        team_scores = [team.collaboration_score for team in self.teams.values()]
        return np.mean(team_scores) / 100 if team_scores else 0.7
        
    def _generate_performance_summary(self) -> Dict[str, Any]:
        """Generate performance metrics summary"""
        if not self.employees:
            return {"status": "No employee data available"}
            
        performance_scores = [emp.performance_score for emp in self.employees.values()]
        engagement_scores = [emp.engagement_score for emp in self.employees.values()]
        
        return {
            "average_performance": np.mean(performance_scores),
            "average_engagement": np.mean(engagement_scores),
            "high_performers": len([s for s in performance_scores if s >= 85]),
            "performance_trends": "stable",  # Would be calculated from historical data
            "top_performing_departments": self._identify_top_departments()
        }
        
    def _generate_strategic_recommendations(self) -> List[Dict[str, Any]]:
        """Generate strategic recommendations for workforce management"""
        recommendations = []
        
        # Analyze current state and generate recommendations
        wellbeing_dist = self._analyze_wellbeing_distribution()
        
        if wellbeing_dist["at_risk"] + wellbeing_dist["requires_support"] > 0.2:
            recommendations.append({
                "category": "Employee Wellbeing",
                "recommendation": "Implement comprehensive mental health and wellness program",
                "priority": "high",
                "expected_impact": "25% improvement in wellbeing scores",
                "implementation_timeline": "60 days",
                "resource_requirements": "Wellness platform subscription, training budget"
            })
            
        avg_performance = np.mean([emp.performance_score for emp in self.employees.values()])
        if avg_performance < 80:
            recommendations.append({
                "category": "Performance Management",
                "recommendation": "Enhance remote performance management and feedback systems",
                "priority": "medium",
                "expected_impact": "15% improvement in performance scores",
                "implementation_timeline": "90 days",
                "resource_requirements": "Performance management platform, manager training"
            })
            
        # Technology and infrastructure recommendations
        recommendations.append({
            "category": "Technology Infrastructure",
            "recommendation": "Upgrade collaboration platforms with AI-powered productivity features",
            "priority": "medium",
            "expected_impact": "20% improvement in collaboration efficiency",
            "implementation_timeline": "45 days",
            "resource_requirements": "Technology budget, IT support resources"
        })
        
        return recommendations

# Specialized helper classes
class PerformanceAnalyzer:
    """Analyze employee performance patterns and trends"""
    
    def analyze_individual_performance(self, employee: Employee, metrics_history: List[WorkflowMetrics]) -> Dict[str, Any]:
        """Analyze individual employee performance"""
        employee_metrics = [m for m in metrics_history if m.employee_id == employee.id]
        
        if not employee_metrics:
            return {"status": "Insufficient data"}
            
        recent_metrics = employee_metrics[-30:]  # Last 30 data points
        
        return {
            "average_productivity": np.mean([m.productivity_score for m in recent_metrics]),
            "collaboration_index": np.mean([m.collaboration_index for m in recent_metrics]),
            "work_life_balance": np.mean([m.work_life_balance_score for m in recent_metrics]),
            "performance_trend": "improving" if len(recent_metrics) > 10 else "stable",
            "strengths": self._identify_performance_strengths(recent_metrics),
            "development_areas": self._identify_development_areas(recent_metrics)
        }
        
    def _identify_performance_strengths(self, metrics: List[WorkflowMetrics]) -> List[str]:
        """Identify performance strengths based on metrics"""
        strengths = []
        avg_productivity = np.mean([m.productivity_score for m in metrics])
        avg_collaboration = np.mean([m.collaboration_index for m in metrics])
        avg_completion = np.mean([m.task_completion_rate for m in metrics])
        
        if avg_productivity > 85:
            strengths.append("High productivity and output quality")
        if avg_collaboration > 80:
            strengths.append("Excellent collaboration and teamwork")
        if avg_completion > 90:
            strengths.append("Reliable task completion and deadline management")
            
        return strengths
        
    def _identify_development_areas(self, metrics: List[WorkflowMetrics]) -> List[str]:
        """Identify areas for performance development"""
        development_areas = []
        avg_stress = np.mean([m.stress_level for m in metrics])
        avg_engagement = np.mean([m.engagement_level for m in metrics])
        avg_communication = np.mean([m.communication_frequency for m in metrics])
        
        if avg_stress > 70:
            development_areas.append("Stress management and workload balance")
        if avg_engagement < 70:
            development_areas.append("Employee engagement and motivation")
        if avg_communication < 60:
            development_areas.append("Communication frequency and effectiveness")
            
        return development_areas

class WellbeingMonitor:
    """Monitor and analyze employee wellbeing patterns"""
    
    def assess_employee_wellbeing(self, employee: Employee, metrics_history: List[WorkflowMetrics]) -> Dict[str, Any]:
        """Comprehensive wellbeing assessment for individual employee"""
        employee_metrics = [m for m in metrics_history if m.employee_id == employee.id]
        
        if not employee_metrics:
            return {"status": "Insufficient data for assessment"}
            
        recent_metrics = employee_metrics[-14:]  # Last 2 weeks
        
        # Calculate wellbeing indicators
        avg_stress = np.mean([m.stress_level for m in recent_metrics])
        avg_work_life_balance = np.mean([m.work_life_balance_score for m in recent_metrics])
        avg_engagement = np.mean([m.engagement_level for m in recent_metrics])
        
        # Determine wellbeing status
        wellbeing_score = (100 - avg_stress + avg_work_life_balance + avg_engagement) / 3
        
        if wellbeing_score >= 80:
            status = WellbeingStatus.THRIVING
        elif wellbeing_score >= 70:
            status = WellbeingStatus.STABLE
        elif wellbeing_score >= 60:
            status = WellbeingStatus.AT_RISK
        else:
            status = WellbeingStatus.REQUIRES_SUPPORT
            
        return {
            "wellbeing_score": wellbeing_score,
            "status": status.value,
            "stress_level": avg_stress,
            "work_life_balance": avg_work_life_balance,
            "engagement_level": avg_engagement,
            "recommendations": self._generate_wellbeing_recommendations(status, avg_stress, avg_work_life_balance, avg_engagement)
        }
        
    def _generate_wellbeing_recommendations(self, status: WellbeingStatus, stress: float, 
                                         work_life_balance: float, engagement: float) -> List[str]:
        """Generate personalized wellbeing recommendations"""
        recommendations = []
        
        if status in [WellbeingStatus.AT_RISK, WellbeingStatus.REQUIRES_SUPPORT]:
            recommendations.append("Schedule immediate check-in with manager or HR")
            
        if stress > 70:
            recommendations.extend([
                "Consider stress management workshop or counseling",
                "Review workload distribution and priorities",
                "Implement daily mindfulness or relaxation practices"
            ])
            
        if work_life_balance < 70:
            recommendations.extend([
                "Establish clearer work-life boundaries",
                "Consider flexible work arrangements",
                "Discuss time management strategies with supervisor"
            ])
            
        if engagement < 70:
            recommendations.extend([
                "Explore career development opportunities",
                "Discuss role alignment and job satisfaction",
                "Consider involvement in cross-functional projects"
            ])
            
        return recommendations

class CollaborationOptimizer:
    """Optimize team collaboration and communication patterns"""
    
    def analyze_team_dynamics(self, team: TeamCollaboration) -> Dict[str, Any]:
        """Analyze team collaboration dynamics and effectiveness"""
        return {
            "team_id": team.team_id,
            "collaboration_score": team.collaboration_score,
            "communication_effectiveness": team.communication_patterns.get("effectiveness", 0),
            "meeting_efficiency": team.meeting_efficiency,
            "knowledge_sharing": team.knowledge_sharing_index,
            "improvement_areas": self._identify_collaboration_improvements(team),
            "optimization_strategies": self._suggest_collaboration_strategies(team)
        }
        
    def _identify_collaboration_improvements(self, team: TeamCollaboration) -> List[str]:
        """Identify areas for collaboration improvement"""
        improvements = []
        
        if team.collaboration_score < 75:
            improvements.append("Overall team collaboration effectiveness")
        if team.meeting_efficiency < 70:
            improvements.append("Meeting structure and time management")
        if team.knowledge_sharing_index < 65:
            improvements.append("Knowledge sharing and documentation practices")
            
        return improvements
        
    def _suggest_collaboration_strategies(self, team: TeamCollaboration) -> List[str]:
        """Suggest specific strategies to improve collaboration"""
        strategies = []
        
        if team.meeting_efficiency < 70:
            strategies.extend([
                "Implement structured meeting agendas with time limits",
                "Use asynchronous communication for status updates",
                "Establish clear meeting objectives and outcomes"
            ])
            
        if team.knowledge_sharing_index < 65:
            strategies.extend([
                "Create shared knowledge repositories and documentation",
                "Implement regular knowledge sharing sessions",
                "Encourage cross-training and skill sharing"
            ])
            
        return strategies

# Example usage and demonstration
def create_sample_remote_workforce():
    """Create sample remote workforce for demonstration"""
    workforce_manager = ResilientWorkforceManager("tech_company_001")
    
    # Create sample employees
    employees = [
        Employee(
            id="emp_001",
            name="Sarah Johnson",
            role="Senior Software Engineer",
            department="Engineering",
            work_location=WorkLocation.FULLY_REMOTE,
            status=EmployeeStatus.ACTIVE,
            timezone="UTC-05:00",
            skills=["Python", "React", "AWS", "Machine Learning"],
            performance_score=88.5,
            engagement_score=82.0,
            wellbeing_status=WellbeingStatus.THRIVING
        ),
        Employee(
            id="emp_002",
            name="Marcus Chen",
            role="Product Manager",
            department="Product",
            work_location=WorkLocation.HYBRID,
            status=EmployeeStatus.ACTIVE,
            timezone="UTC-08:00",
            skills=["Product Strategy", "Data Analysis", "User Research", "Agile"],
            performance_score=91.2,
            engagement_score=89.5,
            wellbeing_status=WellbeingStatus.STABLE
        ),
        Employee(
            id="emp_003",
            name="Emily Rodriguez",
            role="UX Designer",
            department="Design",
            work_location=WorkLocation.FULLY_REMOTE,
            status=EmployeeStatus.ACTIVE,
            timezone="UTC-06:00",
            skills=["UI/UX Design", "Figma", "User Testing", "Prototyping"],
            performance_score=85.8,
            engagement_score=78.5,
            wellbeing_status=WellbeingStatus.AT_RISK
        )
    ]
    
    # Add employees to workforce
    for employee in employees:
        workforce_manager.employees[employee.id] = employee
        
    return workforce_manager, employees

def run_remote_workforce_demo():
    print("=== Resilient Remote Workforce Management Demo ===")
    
    # Create sample workforce
    workforce_manager, employees = create_sample_remote_workforce()
    print(f"Created workforce with {len(employees)} employees")
    
    # Demonstrate employee onboarding
    print("\n--- Employee Onboarding Demo ---")
    new_employee = Employee(
        id="emp_004",
        name="David Kim",
        role="Data Scientist",
        department="Analytics",
        work_location=WorkLocation.FULLY_REMOTE,
        status=EmployeeStatus.ACTIVE,
        timezone="UTC-05:00"
    )
    
    onboarding_result = workforce_manager.onboard_employee(new_employee)
    print(f"Onboarding completed for {new_employee.name}")
    print(f"Expected completion: {onboarding_result['expected_completion_date']}")
    
    # Analyze workforce resilience
    print("\n--- Workforce Resilience Analysis ---")
    resilience_analysis = workforce_manager.analyze_workforce_resilience()
    print(f"Overall Resilience Score: {resilience_analysis['overall_resilience_score']:.1f}/100")
    print(f"Risk Factors Identified: {len(resilience_analysis['risk_factors'])}")
    
    # Implement wellbeing program
    print("\n--- Wellbeing Program Implementation ---")
    wellbeing_program = workforce_manager.implement_wellbeing_program()
    print(f"Launched comprehensive wellbeing program: {wellbeing_program['program_id']}")
    print(f"Program components: {len(wellbeing_program['components'])}")
    
    # Optimize collaboration
    print("\n--- Collaboration Optimization ---")
    collaboration_optimization = workforce_manager.optimize_collaboration_patterns()
    print(f"Current collaboration score: {collaboration_optimization['current_collaboration_score']:.1f}/100")
    print(f"Optimization strategies: {len(collaboration_optimization['optimization_strategies'])}")
    
    # Predict workforce trends
    print("\n--- Workforce Trend Predictions ---")
    trend_predictions = workforce_manager.predict_workforce_trends()
    print(f"Predicted workforce size growth: {trend_predictions['workforce_size_prediction']['growth_rate']:.1f}%")
    print(f"Skills gaps identified: {trend_predictions['skills_gap_analysis']['total_gaps_identified']}")
    print(f"Employees at retention risk: {trend_predictions['retention_risk_analysis']['total_at_risk']}")
    
    # Generate comprehensive report
    print("\n--- Comprehensive Workforce Report ---")
    comprehensive_report = workforce_manager.generate_comprehensive_report()
    print(f"Total employees: {comprehensive_report['workforce_overview']['total_employees']}")
    print(f"Overall resilience score: {comprehensive_report['resilience_assessment']['overall_resilience_score']:.1f}/100")
    print(f"Strategic recommendations: {len(comprehensive_report['strategic_recommendations'])}")
    
    # Display top recommendations
    print("\n=== Top Strategic Recommendations ===")
    for i, recommendation in enumerate(comprehensive_report['strategic_recommendations'][:3], 1):
        print(f"{i}. {recommendation['recommendation']} (Priority: {recommendation['priority']})")
        print(f"   Expected Impact: {recommendation['expected_impact']}")
    
    return workforce_manager, comprehensive_report

# Run demonstration
if __name__ == "__main__":
    demo_manager, demo_report = run_remote_workforce_demo()