class CustomWorkflow: def __init__(self, process_type, client_data): self.process_type = process_type self.client_data = client_data self.status = "analyzing" self.optimization_rules = [] self.analytics_enabled = True self.performance_metrics = {} def analyze_current_process(self, workflow_data): """Analyze existing workflow for optimization opportunities""" bottlenecks = [] efficiency_score = 0 for step in workflow_data: if step.processing_time > self.time_threshold: bottlenecks.append(step.name) efficiency_score += step.calculate_efficiency() return { "bottlenecks": bottlenecks, "efficiency": efficiency_score, "recommendations": self.generate_recommendations() } def optimize_workflow(self, data_input): if data_input > self.efficiency_threshold: self.status = "optimizing" self.apply_automation_rules() return "Custom automation successfully deployed!" else: return "Process analysis complete. Optimization ready." def apply_automation_rules(self): """Apply custom business logic and automation rules""" for rule in self.optimization_rules: if rule.condition_met(self.client_data): rule.execute_automation() self.log_performance_gain(rule.impact_metric) def generate_real_time_insights(self): """Provide real-time analytics and decision support""" current_performance = self.calculate_performance() predicted_outcomes = self.run_predictive_analysis() return { "current_metrics": current_performance, "predictions": predicted_outcomes, "optimization_suggestions": self.get_next_improvements() } def get_analytics_status(self): return f"Real-time insights: {self.status} | Performance: {self.performance_metrics}" def deploy_custom_solution(self, target_systems): """Deploy tailored automation to client systems""" deployment_config = self.build_deployment_config() for system in target_systems: integration_result = system.integrate(deployment_config) if integration_result.success: self.monitor_performance(system) else: self.handle_integration_error(integration_result.error) return "Custom workflow deployed across all target systems