# Comprehensive workflow analysis and optimization system
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import matplotlib.pyplot as plt
import seaborn as sns

@dataclass
class ProcessStep:
    name: str
    duration_minutes: float
    cost_per_hour: float
    error_rate: float
    automation_potential: float  # 0-1 scale
    bottleneck_severity: int  # 1-5 scale
    user_satisfaction: float  # 1-10 scale

@dataclass
class WorkflowMetrics:
    total_cycle_time: float
    active_work_time: float
    wait_time: float
    cost_per_execution: float
    error_rate: float
    throughput_per_day: float
    employee_satisfaction: float

class WorkflowOptimizer:
    def __init__(self):
        self.current_state = {}
        self.future_state = {}
        self.optimization_opportunities = []
        self.automation_recommendations = []
    
    def analyze_current_workflow(self, process_steps: List[ProcessStep]) -> WorkflowMetrics:
        """Comprehensive current state analysis"""
        total_duration = sum(step.duration_minutes for step in process_steps)
        total_cost = sum(
            (step.duration_minutes / 60) * step.cost_per_hour 
            for step in process_steps
        )
        
        # Calculate weighted error rate
        weighted_errors = sum(
            step.error_rate * (step.duration_minutes / total_duration)
            for step in process_steps
        )
        
        # Identify bottlenecks
        bottlenecks = [
            step for step in process_steps 
            if step.bottleneck_severity >= 4
        ]
        
        # Calculate throughput (assuming 8-hour workday)
        daily_capacity = (8 * 60) / total_duration
        
        metrics = WorkflowMetrics(
            total_cycle_time=total_duration,
            active_work_time=sum(step.duration_minutes for step in process_steps),
            wait_time=0,  # Will be calculated from process mapping
            cost_per_execution=total_cost,
            error_rate=weighted_errors,
            throughput_per_day=daily_capacity,
            employee_satisfaction=np.mean([step.user_satisfaction for step in process_steps])
        )
        
        return metrics
    
    def identify_optimization_opportunities(self, process_steps: List[ProcessStep]) -> List[Dict]:
        """Systematic opportunity identification using multiple frameworks"""
        opportunities = []
        
        # Lean analysis - eliminate waste
        for step in process_steps:
            if step.error_rate > 0.05:  # >5% error rate
                opportunities.append({
                    "type": "quality_improvement",
                    "step": step.name,
                    "issue": f"High error rate: {step.error_rate:.1%}",
                    "impact": "high",
                    "effort": "medium",
                    "recommendation": "Implement error prevention controls and training"
                })
            
            if step.bottleneck_severity >= 4:
                opportunities.append({
                    "type": "bottleneck_resolution",
                    "step": step.name,
                    "issue": f"Process bottleneck (severity: {step.bottleneck_severity})",
                    "impact": "high",
                    "effort": "high",
                    "recommendation": "Resource reallocation or process redesign"
                })
            
            if step.automation_potential > 0.7:
                opportunities.append({
                    "type": "automation",
                    "step": step.name,
                    "issue": f"Manual work with high automation potential: {step.automation_potential:.1%}",
                    "impact": "high",
                    "effort": "medium",
                    "recommendation": "Implement workflow automation solution"
                })
            
            if step.user_satisfaction < 5:
                opportunities.append({
                    "type": "user_experience",
                    "step": step.name,
                    "issue": f"Low user satisfaction: {step.user_satisfaction}/10",
                    "impact": "medium",
                    "effort": "low",
                    "recommendation": "Redesign user interface and experience"
                })
        
        return opportunities
    
    def design_optimized_workflow(self, current_steps: List[ProcessStep], 
                                 opportunities: List[Dict]) -> List[ProcessStep]:
        """Create optimized future state workflow"""
        optimized_steps = current_steps.copy()
        
        for opportunity in opportunities:
            step_name = opportunity["step"]
            step_index = next(
                i for i, step in enumerate(optimized_steps) 
                if step.name == step_name
            )
            
            current_step = optimized_steps[step_index]
            
            if opportunity["type"] == "automation":
                # Reduce duration and cost through automation
                new_duration = current_step.duration_minutes * (1 - current_step.automation_potential * 0.8)
                new_cost = current_step.cost_per_hour * 0.3  # Automation reduces labor cost
                new_error_rate = current_step.error_rate * 0.2  # Automation reduces errors
                
                optimized_steps[step_index] = ProcessStep(
                    name=f"{current_step.name} (Automated)",
                    duration_minutes=new_duration,
                    cost_per_hour=new_cost,
                    error_rate=new_error_rate,
                    automation_potential=0.1,  # Already automated
                    bottleneck_severity=max(1, current_step.bottleneck_severity - 2),
                    user_satisfaction=min(10, current_step.user_satisfaction + 2)
                )
            
            elif opportunity["type"] == "quality_improvement":
                # Reduce error rate through process improvement
                optimized_steps[step_index] = ProcessStep(
                    name=f"{current_step.name} (Improved)",
                    duration_minutes=current_step.duration_minutes * 1.1,  # Slight increase for quality
                    cost_per_hour=current_step.cost_per_hour,
                    error_rate=current_step.error_rate * 0.3,  # Significant error reduction
                    automation_potential=current_step.automation_potential,
                    bottleneck_severity=current_step.bottleneck_severity,
                    user_satisfaction=min(10, current_step.user_satisfaction + 1)
                )
            
            elif opportunity["type"] == "bottleneck_resolution":
                # Resolve bottleneck through resource optimization
                optimized_steps[step_index] = ProcessStep(
                    name=f"{current_step.name} (Optimized)",
                    duration_minutes=current_step.duration_minutes * 0.6,  # Reduce bottleneck time
                    cost_per_hour=current_step.cost_per_hour * 1.2,  # Higher skilled resource
                    error_rate=current_step.error_rate,
                    automation_potential=current_step.automation_potential,
                    bottleneck_severity=1,  # Bottleneck resolved
                    user_satisfaction=min(10, current_step.user_satisfaction + 2)
                )
        
        return optimized_steps
    
    def calculate_improvement_impact(self, current_metrics: WorkflowMetrics, 
                                   optimized_metrics: WorkflowMetrics) -> Dict:
        """Calculate quantified improvement impact"""
        improvements = {
            "cycle_time_reduction": {
                "absolute": current_metrics.total_cycle_time - optimized_metrics.total_cycle_time,
                "percentage": ((current_metrics.total_cycle_time - optimized_metrics.total_cycle_time) 
                              / current_metrics.total_cycle_time) * 100
            },
            "cost_reduction": {
                "absolute": current_metrics.cost_per_execution - optimized_metrics.cost_per_execution,
                "percentage": ((current_metrics.cost_per_execution - optimized_metrics.cost_per_execution)
                              / current_metrics.cost_per_execution) * 100
            },
            "quality_improvement": {
                "absolute": current_metrics.error_rate - optimized_metrics.error_rate,
                "percentage": ((current_metrics.error_rate - optimized_metrics.error_rate)
                              / current_metrics.error_rate) * 100 if current_metrics.error_rate > 0 else 0
            },
            "throughput_increase": {
                "absolute": optimized_metrics.throughput_per_day - current_metrics.throughput_per_day,
                "percentage": ((optimized_metrics.throughput_per_day - current_metrics.throughput_per_day)
                              / current_metrics.throughput_per_day) * 100
            },
            "satisfaction_improvement": {
                "absolute": optimized_metrics.employee_satisfaction - current_metrics.employee_satisfaction,
                "percentage": ((optimized_metrics.employee_satisfaction - current_metrics.employee_satisfaction)
                              / current_metrics.employee_satisfaction) * 100
            }
        }
        
        return improvements
    
    def create_implementation_plan(self, opportunities: List[Dict]) -> Dict:
        """Create prioritized implementation roadmap"""
        # Score opportunities by impact vs effort
        for opp in opportunities:
            impact_score = {"high": 3, "medium": 2, "low": 1}[opp["impact"]]
            effort_score = {"low": 1, "medium": 2, "high": 3}[opp["effort"]]
            opp["priority_score"] = impact_score / effort_score
        
        # Sort by priority score (higher is better)
        opportunities.sort(key=lambda x: x["priority_score"], reverse=True)
        
        # Create implementation phases
        phases = {
            "quick_wins": [opp for opp in opportunities if opp["effort"] == "low"],
            "medium_term": [opp for opp in opportunities if opp["effort"] == "medium"],
            "strategic": [opp for opp in opportunities if opp["effort"] == "high"]
        }
        
        return {
            "prioritized_opportunities": opportunities,
            "implementation_phases": phases,
            "timeline_weeks": {
                "quick_wins": 4,
                "medium_term": 12,
                "strategic": 26
            }
        }
    
    def generate_automation_strategy(self, process_steps: List[ProcessStep]) -> Dict:
        """Create comprehensive automation strategy"""
        automation_candidates = [
            step for step in process_steps 
            if step.automation_potential > 0.5
        ]
        
        automation_tools = {
            "data_entry": "RPA (UiPath, Automation Anywhere)",
            "document_processing": "OCR + AI (Adobe Document Services)",
            "approval_workflows": "Workflow automation (Zapier, Microsoft Power Automate)",
            "data_validation": "Custom scripts + API integration",
            "reporting": "Business Intelligence tools (Power BI, Tableau)",
            "communication": "Chatbots + integration platforms"
        }
        
        implementation_strategy = {
            "automation_candidates": [
                {
                    "step": step.name,
                    "potential": step.automation_potential,
                    "estimated_savings_hours_month": (step.duration_minutes / 60) * 22 * step.automation_potential,
                    "recommended_tool": "RPA platform",  # Simplified for example
                    "implementation_effort": "Medium"
                }
                for step in automation_candidates
            ],
            "total_monthly_savings": sum(
                (step.duration_minutes / 60) * 22 * step.automation_potential
                for step in automation_candidates
            ),
            "roi_timeline_months": 6
        }
        
        return implementation_strategy

# [Process Name] Workflow Optimization Report

## 📈 Optimization Impact Summary
**Cycle Time Improvement**: [X% reduction with quantified time savings]
**Cost Savings**: [Annual cost reduction with ROI calculation]
**Quality Enhancement**: [Error rate reduction and quality metrics improvement]
**Employee Satisfaction**: [User satisfaction improvement and adoption metrics]

## 🔍 Current State Analysis
**Process Mapping**: [Detailed workflow visualization with bottleneck identification]
**Performance Metrics**: [Baseline measurements for time, cost, quality, satisfaction]
**Pain Point Analysis**: [Root cause analysis of inefficiencies and user frustrations]
**Automation Assessment**: [Tasks suitable for automation with potential impact]

## 🎯 Optimized Future State
**Redesigned Workflow**: [Streamlined process with automation integration]
**Performance Projections**: [Expected improvements with confidence intervals]
**Technology Integration**: [Automation tools and system integration requirements]
**Resource Requirements**: [Staffing, training, and technology needs]

## 🛠 Implementation Roadmap
**Phase 1 - Quick Wins**: [4-week improvements requiring minimal effort]
**Phase 2 - Process Optimization**: [12-week systematic improvements]
**Phase 3 - Strategic Automation**: [26-week technology implementation]
**Success Metrics**: [KPIs and monitoring systems for each phase]

## 💰 Business Case and ROI
**Investment Required**: [Implementation costs with breakdown by category]
**Expected Returns**: [Quantified benefits with 3-year projection]
**Payback Period**: [Break-even analysis with sensitivity scenarios]
**Risk Assessment**: [Implementation risks with mitigation strategies]

---
**Workflow Optimizer**: [Your name]
**Optimization Date**: [Date]
**Implementation Priority**: [High/Medium/Low with business justification]
**Success Probability**: [High/Medium/Low based on complexity and change readiness]