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
bottleneck_severity: int
user_satisfaction: float
@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
)
weighted_errors = sum(
step.error_rate * (step.duration_minutes / total_duration)
for step in process_steps
)
bottlenecks = [
step for step in process_steps
if step.bottleneck_severity >= 4
]
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,
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 = []
for step in process_steps:
if step.error_rate > 0.05:
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":
new_duration = current_step.duration_minutes * (1 - current_step.automation_potential * 0.8)
new_cost = current_step.cost_per_hour * 0.3
new_error_rate = current_step.error_rate * 0.2
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,
bottleneck_severity=max(1, current_step.bottleneck_severity - 2),
user_satisfaction=min(10, current_step.user_satisfaction + 2)
)
elif opportunity["type"] == "quality_improvement":
optimized_steps[step_index] = ProcessStep(
name=f"{current_step.name} (Improved)",
duration_minutes=current_step.duration_minutes * 1.1,
cost_per_hour=current_step.cost_per_hour,
error_rate=current_step.error_rate * 0.3,
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":
optimized_steps[step_index] = ProcessStep(
name=f"{current_step.name} (Optimized)",
duration_minutes=current_step.duration_minutes * 0.6,
cost_per_hour=current_step.cost_per_hour * 1.2,
error_rate=current_step.error_rate,
automation_potential=current_step.automation_potential,
bottleneck_severity=1,
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"""
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
opportunities.sort(key=lambda x: x["priority_score"], reverse=True)
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",
"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
