Implementing Innovation Analytics: Code Examples and Production Deployment

from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
import asyncio
import logging

class InnovationStage(Enum):
    IDEATION = "ideation"
    VALIDATION = "validation"
    DEVELOPMENT = "development"
    TESTING = "testing"
    LAUNCH = "launch"
    SCALE = "scale"
    MATURITY = "maturity"

@dataclass
class InnovationProject:
    project_id: str
    name: str
    current_stage: InnovationStage
    start_date: datetime
    target_launch_date: datetime
    investment_to_date: float
    strategic_priority: str
    risk_category: str
    market_potential: float
    technical_feasibility: float
    competitive_advantage: float
    resource_requirements: Dict[str, float]
    kpis: Dict[str, float] = field(default_factory=dict)
    stage_history: List[Tuple[InnovationStage, datetime]] = field(default_factory=list)

@dataclass
class PortfolioMetrics:
    total_investment: float
    active_projects: int
    portfolio_risk_score: float
    strategic_alignment_score: float
    expected_portfolio_value: float
    time_to_market_average: float
    success_rate: float
    competitive_advantage_score: float

class InnovationMetricsEngine:
    """Production-ready innovation metrics calculation engine."""

    def __init__(self, data_sources: Dict[str, Any], config: Dict[str, Any]):
        self.data_sources = data_sources
        self.config = config
        self.logger = logging.getLogger(__name__)
        self.prediction_models = {}
        self.metric_cache = {}
        self._initialize_prediction_models()

    async def calculate_project_metrics(self, project: InnovationProject) -> Dict[str, Any]:
        """Calculate comprehensive metrics for individual innovation project."""

        try:
            # Performance metrics
            performance_metrics = await self._calculate_performance_metrics(project)

            # Progress metrics
            progress_metrics = await self._calculate_progress_metrics(project)

            # Value metrics
            value_metrics = await self._calculate_value_metrics(project)

            # Risk metrics
            risk_metrics = await self._calculate_risk_metrics(project)

            # Predictive metrics
            predictive_metrics = await self._calculate_predictive_metrics(project)

            # Market impact metrics
            market_metrics = await self._calculate_market_metrics(project)

            project_metrics = {
                "project_id": project.project_id,
                "calculation_timestamp": datetime.utcnow().isoformat(),
                "performance": performance_metrics,
                "progress": progress_metrics,
                "value": value_metrics,
                "risk": risk_metrics,
                "predictions": predictive_metrics,
                "market_impact": market_metrics,
                "overall_health_score": self._calculate_overall_health_score([
                    performance_metrics, progress_metrics, value_metrics,
                    risk_metrics, market_metrics
                ])
            }

            # Cache results
            await self._cache_project_metrics(project.project_id, project_metrics)

            return project_metrics

        except Exception as e:
            self.logger.error(f"Error calculating metrics for project {project.project_id}: {str(e)}")
            return self._create_error_metrics(project.project_id, str(e))

    async def _calculate_performance_metrics(self, project: InnovationProject) -> Dict[str, float]:
        """Calculate project performance metrics."""

        metrics = {}

        # Time performance
        days_since_start = (datetime.utcnow() - project.start_date).days
        planned_duration = (project.target_launch_date - project.start_date).days

        if planned_duration > 0:
            metrics["schedule_performance"] = min(1.0, days_since_start / planned_duration)
        else:
            metrics["schedule_performance"] = 0.0

        # Budget performance
        planned_investment = self._get_planned_investment(project, days_since_start)
        if planned_investment > 0:
            metrics["budget_performance"] = min(1.0, project.investment_to_date / planned_investment)
        else:
            metrics["budget_performance"] = 0.0

        # Stage progression velocity
        stage_velocity = self._calculate_stage_velocity(project)
        metrics["stage_velocity"] = stage_velocity

        # Milestone completion rate
        milestone_completion = await self._get_milestone_completion_rate(project.project_id)
        metrics["milestone_completion_rate"] = milestone_completion

        # Quality metrics
        quality_score = await self._calculate_quality_score(project.project_id)
        metrics["quality_score"] = quality_score

        # Team productivity
        productivity_score = await self._calculate_team_productivity(project.project_id)
        metrics["team_productivity"] = productivity_score

        return metrics

    async def _calculate_value_metrics(self, project: InnovationProject) -> Dict[str, float]:
        """Calculate value creation and ROI metrics."""

        metrics = {}

        # Expected ROI calculation
        expected_revenue = self._calculate_expected_revenue(project)
        total_investment = project.investment_to_date + self._estimate_remaining_investment(project)

        if total_investment > 0:
            metrics["expected_roi"] = (expected_revenue - total_investment) / total_investment
        else:
            metrics["expected_roi"] = 0.0

        # Strategic value score
        strategic_value = self._calculate_strategic_value(project)
        metrics["strategic_value_score"] = strategic_value

        # Market opportunity size
        market_opportunity = await self._assess_market_opportunity(project)
        metrics["market_opportunity_score"] = market_opportunity

        # Competitive advantage potential
        competitive_advantage = self._assess_competitive_advantage(project)
        metrics["competitive_advantage_score"] = competitive_advantage

        # Customer value creation potential
        customer_value = await self._assess_customer_value_potential(project)
        metrics["customer_value_score"] = customer_value

        # Innovation index
        innovation_index = self._calculate_innovation_index(project)
        metrics["innovation_index"] = innovation_index

        return metrics

    async def _calculate_predictive_metrics(self, project: InnovationProject) -> Dict[str, Any]:
        """Calculate predictive metrics using machine learning models."""

        features = self._extract_project_features(project)

        predictions = {}

        # Success probability prediction
        if "success_prediction" in self.prediction_models:
            success_prob = self.prediction_models["success_prediction"].predict_proba([features])[0][1]
            predictions["success_probability"] = float(success_prob)

        # Time to market prediction
        if "time_to_market" in self.prediction_models:
            ttm_prediction = self.prediction_models["time_to_market"].predict([features])[0]
            predictions["predicted_time_to_market_days"] = float(ttm_prediction)

        # Investment requirement prediction
        if "investment_prediction" in self.prediction_models:
            investment_pred = self.prediction_models["investment_prediction"].predict([features])[0]
            predictions["predicted_total_investment"] = float(investment_pred)

        # Market share prediction
        if "market_share" in self.prediction_models:
            market_share_pred = self.prediction_models["market_share"].predict([features])[0]
            predictions["predicted_market_share"] = float(market_share_pred)

        # Risk assessment
        risk_factors = self._assess_risk_factors(project)
        predictions["risk_factors"] = risk_factors

        # Scenario analysis
        scenarios = self._generate_scenario_analysis(project, predictions)
        predictions["scenarios"] = scenarios

        return predictions

    async def calculate_portfolio_metrics(self, projects: List[InnovationProject]) -> PortfolioMetrics:
        """Calculate comprehensive portfolio-level metrics."""

        # Basic portfolio statistics
        total_investment = sum(p.investment_to_date for p in projects)
        active_projects = len([p for p in projects if p.current_stage != InnovationStage.MATURITY])

        # Portfolio risk calculation
        portfolio_risk = self._calculate_portfolio_risk(projects)

        # Strategic alignment assessment
        strategic_alignment = self._assess_portfolio_strategic_alignment(projects)

        # Expected portfolio value
        expected_value = await self._calculate_expected_portfolio_value(projects)

        # Average time to market
        ttm_average = self._calculate_average_time_to_market(projects)

        # Historical success rate
        success_rate = await self._calculate_historical_success_rate(projects)

        # Competitive advantage assessment
        competitive_advantage = self._assess_portfolio_competitive_advantage(projects)

        return PortfolioMetrics(
            total_investment=total_investment,
            active_projects=active_projects,
            portfolio_risk_score=portfolio_risk,
            strategic_alignment_score=strategic_alignment,
            expected_portfolio_value=expected_value,
            time_to_market_average=ttm_average,
            success_rate=success_rate,
            competitive_advantage_score=competitive_advantage
        )

    def _calculate_portfolio_risk(self, projects: List[InnovationProject]) -> float:
        """Calculate portfolio-wide risk score considering correlation effects."""

        if not projects:
            return 0.0

        # Individual project risks
        project_risks = [self._calculate_individual_risk(p) for p in projects]

        # Portfolio diversification benefit
        diversification_benefit = self._calculate_diversification_benefit(projects)

        # Risk concentration penalty
        concentration_penalty = self._calculate_concentration_penalty(projects)

        # Portfolio risk = weighted average - diversification + concentration
        portfolio_weights = [p.investment_to_date for p in projects]
        total_investment = sum(portfolio_weights)

        if total_investment == 0:
            return 0.0

        weighted_risk = sum(
            risk * weight / total_investment
            for risk, weight in zip(project_risks, portfolio_weights)
        )

        portfolio_risk = weighted_risk - diversification_benefit + concentration_penalty

        return max(0.0, min(1.0, portfolio_risk))

    async def _calculate_expected_portfolio_value(self, projects: List[InnovationProject]) -> float:
        """Calculate expected value of entire innovation portfolio."""

        total_expected_value = 0.0

        for project in projects:
            # Get project-specific predictions
            project_features = self._extract_project_features(project)

            # Calculate expected value for this project
            success_prob = 0.5  # Default if no model available
            if "success_prediction" in self.prediction_models:
                success_prob = self.prediction_models["success_prediction"].predict_proba([project_features])[0][1]

            # Expected revenue if successful
            expected_revenue = self._calculate_expected_revenue(project)

            # Expected value = probability of success * expected revenue
            project_expected_value = success_prob * expected_revenue

            total_expected_value += project_expected_value

        return total_expected_value

import asyncio
from kafka import KafkaConsumer, KafkaProducer
import redis
import json
from typing import Dict, List, Any
from datetime import datetime, timedelta
import logging

class RealTimeInnovationAnalytics:
    """Real-time analytics pipeline for innovation metrics."""

    def __init__(self, kafka_config: Dict, redis_config: Dict, metrics_engine):
        self.kafka_consumer = KafkaConsumer(
            'innovation-events',
            bootstrap_servers=kafka_config['bootstrap_servers'],
            value_deserializer=lambda x: json.loads(x.decode('utf-8')),
            consumer_timeout_ms=1000
        )

        self.kafka_producer = KafkaProducer(
            bootstrap_servers=kafka_config['bootstrap_servers'],
            value_serializer=lambda x: json.dumps(x).encode('utf-8')
        )

        self.redis_client = redis.Redis(**redis_config)
        self.metrics_engine = metrics_engine
        self.logger = logging.getLogger(__name__)

        # Aggregation windows for real-time metrics
        self.aggregation_windows = {
            'hourly': timedelta(hours=1),
            'daily': timedelta(days=1),
            'weekly': timedelta(weeks=1)
        }

    async def start_real_time_processing(self):
        """Start real-time analytics processing pipeline."""

        self.logger.info("Starting real-time innovation analytics pipeline")

        # Start multiple concurrent processing tasks
        tasks = [
            asyncio.create_task(self._process_event_stream()),
            asyncio.create_task(self._calculate_rolling_metrics()),
            asyncio.create_task(self._detect_anomalies()),
            asyncio.create_task(self._generate_real_time_insights()),
            asyncio.create_task(self._update_dashboards())
        ]

        try:
            await asyncio.gather(*tasks)
        except Exception as e:
            self.logger.error(f"Error in real-time processing pipeline: {str(e)}")
            raise

    async def _process_event_stream(self):
        """Process incoming innovation events from Kafka stream."""

        for message in self.kafka_consumer:
            try:
                event = message.value
                event_type = event.get('event_type')

                # Route event to appropriate handler
                if event_type == 'project_update':
                    await self._handle_project_update(event)
                elif event_type == 'milestone_reached':
                    await self._handle_milestone_event(event)
                elif event_type == 'resource_allocation':
                    await self._handle_resource_event(event)
                elif event_type == 'market_feedback':
                    await self._handle_market_feedback(event)
                elif event_type == 'competitive_intelligence':
                    await self._handle_competitive_intelligence(event)

                # Update real-time aggregations
                await self._update_real_time_aggregations(event)

                # Trigger immediate metric recalculation if needed
                if self._requires_immediate_recalculation(event):
                    await self._trigger_metric_recalculation(event)

            except Exception as e:
                self.logger.error(f"Error processing event: {str(e)}")
                continue

    async def _handle_project_update(self, event: Dict[str, Any]):
        """Handle project update events."""

        project_id = event['project_id']
        update_type = event['update_type']

        # Store event in Redis for quick access
        event_key = f"project_events:{project_id}"
        self.redis_client.lpush(event_key, json.dumps(event))
        self.redis_client.ltrim(event_key, 0, 100)  # Keep last 100 events

        # Update project metrics cache
        if update_type in ['stage_change', 'budget_update', 'timeline_change']:
            # Invalidate cached metrics for this project
            metrics_key = f"project_metrics:{project_id}"
            self.redis_client.delete(metrics_key)

            # Trigger metric recalculation
            recalc_event = {
                'event_type': 'recalculate_metrics',
                'project_id': project_id,
                'trigger_event': event,
                'timestamp': datetime.utcnow().isoformat()
            }

            self.kafka_producer.send('innovation-metrics-recalc', recalc_event)

    async def _calculate_rolling_metrics(self):
        """Calculate rolling window metrics for real-time dashboards."""

        while True:
            try:
                current_time = datetime.utcnow()

                for window_name, window_size in self.aggregation_windows.items():
                    window_start = current_time - window_size

                    # Get events in this time window
                    events = await self._get_events_in_window(window_start, current_time)

                    # Calculate rolling metrics
                    rolling_metrics = self._calculate_window_metrics(events, window_name)

                    # Store rolling metrics in Redis
                    metrics_key = f"rolling_metrics:{window_name}"
                    self.redis_client.setex(
                        metrics_key,
                        int(window_size.total_seconds()),
                        json.dumps(rolling_metrics)
                    )

                    # Publish metrics update
                    metrics_update = {
                        'event_type': 'rolling_metrics_update',
                        'window': window_name,
                        'metrics': rolling_metrics,
                        'timestamp': current_time.isoformat()
                    }

                    self.kafka_producer.send('innovation-metrics-updates', metrics_update)

                # Wait before next calculation cycle
                await asyncio.sleep(60)  # Calculate every minute

            except Exception as e:
                self.logger.error(f"Error calculating rolling metrics: {str(e)}")
                await asyncio.sleep(10)

    async def _detect_anomalies(self):
        """Detect anomalies in innovation metrics using statistical methods."""

        while True:
            try:
                # Get current metrics
                current_metrics = await self._get_current_portfolio_metrics()

                # Get historical baseline
                historical_metrics = await self._get_historical_metrics_baseline()

                # Detect anomalies
                anomalies = self._identify_metric_anomalies(
                    current_metrics, historical_metrics
                )

                if anomalies:
                    # Generate anomaly alerts
                    for anomaly in anomalies:
                        alert = {
                            'event_type': 'anomaly_detected',
                            'anomaly_type': anomaly['type'],
                            'metric_name': anomaly['metric'],
                            'current_value': anomaly['current_value'],
                            'expected_value': anomaly['expected_value'],
                            'deviation_score': anomaly['deviation_score'],
                            'severity': anomaly['severity'],
                            'timestamp': datetime.utcnow().isoformat()
                        }

                        self.kafka_producer.send('innovation-alerts', alert)

                await asyncio.sleep(300)  # Check every 5 minutes

            except Exception as e:
                self.logger.error(f"Error in anomaly detection: {str(e)}")
                await asyncio.sleep(30)

    def _calculate_window_metrics(self, events: List[Dict], window_name: str) -> Dict[str, Any]:
        """Calculate metrics for a specific time window."""

        metrics = {
            'window': window_name,
            'event_count': len(events),
            'project_activity': {},
            'portfolio_metrics': {},
            'trends': {}
        }

        if not events:
            return metrics

        # Group events by project
        project_events = {}
        for event in events:
            project_id = event.get('project_id')
            if project_id:
                if project_id not in project_events:
                    project_events[project_id] = []
                project_events[project_id].append(event)

        # Calculate project-level activity metrics
        for project_id, proj_events in project_events.items():
            metrics['project_activity'][project_id] = {
                'event_count': len(proj_events),
                'last_activity': max(e['timestamp'] for e in proj_events),
                'activity_types': list(set(e['event_type'] for e in proj_events))
            }

        # Calculate portfolio-level metrics
        milestone_events = [e for e in events if e['event_type'] == 'milestone_reached']
        metrics['portfolio_metrics']['milestones_reached'] = len(milestone_events)

        budget_events = [e for e in events if e['event_type'] == 'resource_allocation']
        total_allocated = sum(e.get('amount', 0) for e in budget_events)
        metrics['portfolio_metrics']['total_budget_allocated'] = total_allocated

        # Calculate trends
        event_timestamps = [datetime.fromisoformat(e['timestamp'].replace('Z', '+00:00')) for e in events]
        if len(event_timestamps) > 1:
            # Simple linear trend calculation
            time_diffs = [(ts - min(event_timestamps)).total_seconds() for ts in event_timestamps]
            activity_trend = np.polyfit(time_diffs, range(len(event_timestamps)), 1)[0]
            metrics['trends']['activity_trend'] = float(activity_trend)

        return metrics

from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import classification_report, mean_squared_error
import joblib
from datetime import datetime
import numpy as np
import pandas as pd

class AdvancedInnovationAnalytics:
    """Advanced analytics for innovation performance prediction and optimization."""

    def __init__(self, data_sources: Dict, model_config: Dict):
        self.data_sources = data_sources
        self.model_config = model_config
        self.models = {}
        self.feature_importance = {}
        self.model_performance = {}

    async def train_success_prediction_model(self, training_data: pd.DataFrame) -> Dict[str, Any]:
        """Train machine learning model to predict innovation success probability."""

        # Feature engineering
        features = self._engineer_features(training_data)
        target = training_data['success'].astype(int)

        # Time series cross-validation
        tscv = TimeSeriesSplit(n_splits=5)
        cv_scores = []

        best_model = None
        best_score = 0

        for train_idx, val_idx in tscv.split(features):
            X_train, X_val = features.iloc[train_idx], features.iloc[val_idx]
            y_train, y_val = target.iloc[train_idx], target.iloc[val_idx]

            # Train Random Forest model
            rf_model = RandomForestClassifier(
                n_estimators=self.model_config['n_estimators'],
                max_depth=self.model_config['max_depth'],
                random_state=42
            )

            rf_model.fit(X_train, y_train)
            val_score = rf_model.score(X_val, y_val)
            cv_scores.append(val_score)

            if val_score > best_score:
                best_score = val_score
                best_model = rf_model

        # Store best model
        self.models['success_prediction'] = best_model

        # Calculate feature importance
        feature_importance = dict(zip(features.columns, best_model.feature_importances_))
        self.feature_importance['success_prediction'] = feature_importance

        # Model performance metrics
        y_pred = best_model.predict(features)
        performance_report = classification_report(target, y_pred, output_dict=True)
        self.model_performance['success_prediction'] = performance_report

        # Save model
        model_path = f"models/success_prediction_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pkl"
        joblib.dump(best_model, model_path)

        return {
            'model_path': model_path,
            'cv_scores': cv_scores,
            'mean_cv_score': np.mean(cv_scores),
            'feature_importance': feature_importance,
            'performance_report': performance_report
        }

    async def train_time_to_market_model(self, training_data: pd.DataFrame) -> Dict[str, Any]:
        """Train model to predict time to market for innovation projects."""

        # Feature engineering
        features = self._engineer_features(training_data)
        target = training_data['actual_time_to_market_days']

        # Remove outliers
        q99 = target.quantile(0.99)
        valid_idx = target <= q99
        features = features[valid_idx]
        target = target[valid_idx]

        # Time series cross-validation
        tscv = TimeSeriesSplit(n_splits=5)
        cv_scores = []

        best_model = None
        best_score = float('inf')

        for train_idx, val_idx in tscv.split(features):
            X_train, X_val = features.iloc[train_idx], features.iloc[val_idx]
            y_train, y_val = target.iloc[train_idx], target.iloc[val_idx]

            # Train Gradient Boosting model
            gb_model = GradientBoostingRegressor(
                n_estimators=self.model_config['n_estimators'],
                learning_rate=self.model_config['learning_rate'],
                max_depth=self.model_config['max_depth'],
                random_state=42
            )

            gb_model.fit(X_train, y_train)
            y_pred = gb_model.predict(X_val)
            val_score = mean_squared_error(y_val, y_pred)
            cv_scores.append(val_score)

            if val_score < best_score:
                best_score = val_score
                best_model = gb_model

        # Store best model
        self.models['time_to_market'] = best_model

        # Calculate feature importance
        feature_importance = dict(zip(features.columns, best_model.feature_importances_))
        self.feature_importance['time_to_market'] = feature_importance

        # Model performance metrics
        y_pred = best_model.predict(features)
        rmse = np.sqrt(mean_squared_error(target, y_pred))
        mae = np.mean(np.abs(target - y_pred))

        self.model_performance['time_to_market'] = {
            'rmse': rmse,
            'mae': mae,
            'r2_score': best_model.score(features, target)
        }

        # Save model
        model_path = f"models/time_to_market_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pkl"
        joblib.dump(best_model, model_path)

        return {
            'model_path': model_path,
            'cv_scores': cv_scores,
            'mean_cv_score': np.mean(cv_scores),
            'feature_importance': feature_importance,
            'performance_metrics': self.model_performance['time_to_market']
        }

    def _engineer_features(self, data: pd.DataFrame) -> pd.DataFrame:
        """Engineer features for machine learning models."""

        features = pd.DataFrame()

        # Basic project features
        features['market_potential'] = data['market_potential']
        features['technical_feasibility'] = data['technical_feasibility']
        features['competitive_advantage'] = data['competitive_advantage']
        features['investment_amount'] = data['investment_to_date']

        # Time-based features
        features['project_duration'] = (
            pd.to_datetime(data['current_date']) - pd.to_datetime(data['start_date'])
        ).dt.days

        # Stage progression features
        features['stages_completed'] = data['current_stage_index']
        features['stage_velocity'] = data['stages_completed'] / (features['project_duration'] + 1)

        # Resource allocation features
        features['team_size'] = data['team_size']
        features['budget_per_person'] = features['investment_amount'] / (features['team_size'] + 1)

        # Market context features
        features['market_size'] = data['total_addressable_market']
        features['competition_intensity'] = data['competition_intensity_score']

        # Strategic features
        features['strategic_priority'] = pd.Categorical(
            data['strategic_priority']
        ).codes
        features['risk_category'] = pd.Categorical(
            data['risk_category']
        ).codes

        # Interaction features
        features['market_tech_fit'] = features['market_potential'] * features['technical_feasibility']
        features['investment_efficiency'] = features['competitive_advantage'] / (features['investment_amount'] + 1)

        return features

    async def generate_optimization_recommendations(self, portfolio_data: pd.DataFrame) -> List[Dict[str, Any]]:
        """Generate portfolio optimization recommendations using analytics models."""

        recommendations = []

        # Resource reallocation recommendations
        resource_recommendations = self._analyze_resource_allocation_optimization(portfolio_data)
        recommendations.extend(resource_recommendations)

        # Timeline optimization recommendations
        timeline_recommendations = self._analyze_timeline_optimization(portfolio_data)
        recommendations.extend(timeline_recommendations)

        # Portfolio balance recommendations
        balance_recommendations = self._analyze_portfolio_balance(portfolio_data)
        recommendations.extend(balance_recommendations)

        # Risk mitigation recommendations
        risk_recommendations = self._analyze_risk_mitigation_opportunities(portfolio_data)
        recommendations.extend(risk_recommendations)

        # Prioritize recommendations by impact and feasibility
        prioritized_recommendations = self._prioritize_recommendations(recommendations)

        return prioritized_recommendations

    def _analyze_resource_allocation_optimization(self, portfolio_data: pd.DataFrame) -> List[Dict[str, Any]]:
        """Analyze resource allocation for optimization opportunities."""

        recommendations = []

        # Identify under-performing projects with high resource allocation
        portfolio_data['roi_efficiency'] = (
            portfolio_data['expected_roi'] / portfolio_data['investment_to_date']
        )

        low_efficiency_projects = portfolio_data[
            portfolio_data['roi_efficiency'] < portfolio_data['roi_efficiency'].quantile(0.25)
        ]

        for _, project in low_efficiency_projects.iterrows():
            recommendation = {
                'type': 'resource_reallocation',
                'project_id': project['project_id'],
                'current_allocation': project['investment_to_date'],
                'recommended_action': 'reduce_allocation',
                'potential_savings': project['investment_to_date'] * 0.2,
                'impact_score': self._calculate_recommendation_impact(project, 'resource_reduction'),
                'feasibility_score': 0.8,
                'rationale': f"Project shows low ROI efficiency ({project['roi_efficiency']:.2f})"
            }
            recommendations.append(recommendation)

        return recommendations