Enterprise Innovation Measurement: Strategic Frameworks and Competitive Advantage

from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from enum import Enum
import numpy as np
from datetime import datetime, timedelta

class StrategicObjective(Enum):
    MARKET_EXPANSION = "market_expansion"
    COMPETITIVE_DIFFERENTIATION = "competitive_differentiation"
    OPERATIONAL_EXCELLENCE = "operational_excellence"
    ECOSYSTEM_DEVELOPMENT = "ecosystem_development"
    PLATFORM_CREATION = "platform_creation"

@dataclass
class StrategicInnovationMetrics:
    objective_alignment_score: float
    competitive_advantage_duration: float
    market_position_improvement: float
    ecosystem_value_creation: float
    platform_effect_multiplier: float
    strategic_option_value: float

class StrategicInnovationMeasurement:
    """Enterprise-level strategic innovation measurement framework."""

    def __init__(self, strategic_framework, competitive_intelligence, market_analytics):
        self.strategic_framework = strategic_framework
        self.competitive_intelligence = competitive_intelligence
        self.market_analytics = market_analytics
        self.measurement_models = self._initialize_strategic_models()

    async def assess_strategic_innovation_impact(
        self,
        innovation_portfolio: List[Dict[str, Any]],
        strategic_context: Dict[str, Any]
    ) -> StrategicInnovationMetrics:
        """Assess strategic impact of innovation portfolio on competitive positioning."""

        # Strategic objective alignment analysis
        alignment_score = await self._calculate_strategic_alignment(
            innovation_portfolio, strategic_context
        )

        # Competitive advantage sustainability assessment
        advantage_duration = await self._assess_competitive_advantage_duration(
            innovation_portfolio
        )

        # Market position improvement measurement
        market_improvement = await self._measure_market_position_improvement(
            innovation_portfolio, strategic_context
        )

        # Ecosystem value creation analysis
        ecosystem_value = await self._analyze_ecosystem_value_creation(
            innovation_portfolio
        )

        # Platform effect multiplier calculation
        platform_multiplier = await self._calculate_platform_effects(
            innovation_portfolio
        )

        # Strategic option value assessment
        option_value = await self._assess_strategic_option_value(
            innovation_portfolio, strategic_context
        )

        return StrategicInnovationMetrics(
            objective_alignment_score=alignment_score,
            competitive_advantage_duration=advantage_duration,
            market_position_improvement=market_improvement,
            ecosystem_value_creation=ecosystem_value,
            platform_effect_multiplier=platform_multiplier,
            strategic_option_value=option_value
        )

    async def _calculate_strategic_alignment(
        self,
        portfolio: List[Dict[str, Any]],
        context: Dict[str, Any]
    ) -> float:
        """Calculate alignment between innovation portfolio and strategic objectives."""

        strategic_objectives = context.get('strategic_objectives', [])
        objective_weights = context.get('objective_weights', {})

        alignment_scores = []

        for objective in strategic_objectives:
            # Get innovations aligned to this objective
            aligned_innovations = [
                innovation for innovation in portfolio
                if objective in innovation.get('strategic_objectives', [])
            ]

            if not aligned_innovations:
                alignment_scores.append(0.0)
                continue

            # Calculate resource allocation alignment
            objective_investment = sum(
                innovation.get('investment', 0) for innovation in aligned_innovations
            )
            total_investment = sum(
                innovation.get('investment', 0) for innovation in portfolio
            )

            resource_alignment = objective_investment / total_investment if total_investment > 0 else 0

            # Calculate expected impact alignment
            objective_impact = sum(
                innovation.get('expected_impact', 0) for innovation in aligned_innovations
            )
            total_impact = sum(
                innovation.get('expected_impact', 0) for innovation in portfolio
            )

            impact_alignment = objective_impact / total_impact if total_impact > 0 else 0

            # Weighted alignment score for this objective
            objective_weight = objective_weights.get(objective, 1.0)
            objective_alignment = (resource_alignment + impact_alignment) / 2 * objective_weight

            alignment_scores.append(objective_alignment)

        return np.mean(alignment_scores) if alignment_scores else 0.0

    async def _assess_competitive_advantage_duration(
        self, portfolio: List[Dict[str, Any]]
    ) -> float:
        """Assess sustainability of competitive advantages created by innovation portfolio."""

        advantage_factors = []

        for innovation in portfolio:
            # Technical barriers to replication
            technical_barrier = innovation.get('technical_complexity', 0.5)

            # Network effects potential
            network_effects = innovation.get('network_effects_potential', 0.0)

            # Intellectual property protection
            ip_protection = innovation.get('ip_protection_strength', 0.0)

            # First-mover advantage
            first_mover = innovation.get('first_mover_advantage', 0.0)

            # Ecosystem lock-in potential
            ecosystem_lockin = innovation.get('ecosystem_lockin_potential', 0.0)

            # Combined advantage duration score
            innovation_advantage = (
                technical_barrier * 0.2 +
                network_effects * 0.25 +
                ip_protection * 0.2 +
                first_mover * 0.15 +
                ecosystem_lockin * 0.2
            )

            # Weight by innovation impact
            impact_weight = innovation.get('expected_impact', 1.0)
            advantage_factors.append(innovation_advantage * impact_weight)

        total_impact = sum(innovation.get('expected_impact', 1.0) for innovation in portfolio)
        weighted_advantage = sum(advantage_factors) / total_impact if total_impact > 0 else 0

        return weighted_advantage

class EcosystemInnovationMeasurement:
    """Measurement framework for ecosystem-level innovation value creation."""

    def __init__(self, ecosystem_analytics, partner_integration, market_intelligence):
        self.ecosystem_analytics = ecosystem_analytics
        self.partner_integration = partner_integration
        self.market_intelligence = market_intelligence

    async def measure_ecosystem_innovation_value(
        self, ecosystem_data: Dict[str, Any]
    ) -> Dict[str, float]:
        """Measure value creation from ecosystem-level innovation activities."""

        # Partner innovation contribution analysis
        partner_contribution = await self._analyze_partner_innovation_contribution(
            ecosystem_data
        )

        # Network effects amplification measurement
        network_amplification = await self._measure_network_effects_amplification(
            ecosystem_data
        )

        # Ecosystem innovation velocity assessment
        ecosystem_velocity = await self._assess_ecosystem_innovation_velocity(
            ecosystem_data
        )

        # Cross-pollination value measurement
        cross_pollination_value = await self._measure_cross_pollination_effects(
            ecosystem_data
        )

        # Platform value creation analysis
        platform_value = await self._analyze_platform_value_creation(
            ecosystem_data
        )

        return {
            'partner_contribution_score': partner_contribution,
            'network_amplification_factor': network_amplification,
            'ecosystem_velocity_multiplier': ecosystem_velocity,
            'cross_pollination_value': cross_pollination_value,
            'platform_value_creation': platform_value,
            'total_ecosystem_value': self._calculate_total_ecosystem_value([
                partner_contribution, network_amplification, ecosystem_velocity,
                cross_pollination_value, platform_value
            ])
        }

    async def _analyze_partner_innovation_contribution(
        self, ecosystem_data: Dict[str, Any]
    ) -> float:
        """Analyze innovation contributions from ecosystem partners."""

        partner_innovations = ecosystem_data.get('partner_innovations', [])
        total_ecosystem_value = ecosystem_data.get('total_innovation_value', 0)

        if not partner_innovations or total_ecosystem_value == 0:
            return 0.0

        # Calculate direct partner contributions
        partner_direct_value = sum(
            innovation.get('value_contribution', 0)
            for innovation in partner_innovations
        )

        # Calculate indirect amplification effects
        partner_amplification = sum(
            innovation.get('amplification_effect', 0)
            for innovation in partner_innovations
        )

        # Total partner contribution score
        total_partner_contribution = partner_direct_value + partner_amplification

        return min(1.0, total_partner_contribution / total_ecosystem_value)

    async def _measure_network_effects_amplification(
        self, ecosystem_data: Dict[str, Any]
    ) -> float:
        """Measure network effects amplification in innovation value."""

        network_size = ecosystem_data.get('network_size', 0)
        innovation_adoption_rate = ecosystem_data.get('innovation_adoption_rate', 0)
        value_per_connection = ecosystem_data.get('value_per_connection', 0)

        if network_size == 0:
            return 1.0  # No network effects

        # Metcalfe's law approximation for network value
        network_value = network_size * (network_size - 1) * value_per_connection

        # Innovation-specific network amplification
        innovation_network_value = network_value * innovation_adoption_rate

        # Baseline innovation value without network effects
        baseline_value = ecosystem_data.get('baseline_innovation_value', 1)

        amplification_factor = innovation_network_value / baseline_value if baseline_value > 0 else 1.0

        return max(1.0, amplification_factor)  # Minimum 1x (no negative effects)

import numpy as np
from sklearn.ensemble import RandomForestRegressor, GradientBoostingClassifier
from sklearn.neural_network import MLPRegressor
from scipy.optimize import minimize
import pandas as pd

class PredictiveInnovationAnalytics:
    """Advanced predictive analytics for enterprise innovation strategy."""

    def __init__(self, historical_data, market_intelligence, competitive_data):
        self.historical_data = historical_data
        self.market_intelligence = market_intelligence
        self.competitive_data = competitive_data
        self.prediction_models = {}
        self.scenario_models = {}
        self._train_prediction_models()

    async def generate_innovation_predictions(
        self, innovation_portfolio: List[Dict[str, Any]], prediction_horizon: int = 24
    ) -> Dict[str, Any]:
        """Generate comprehensive predictions for innovation portfolio outcomes."""

        # Market opportunity predictions
        market_predictions = await self._predict_market_opportunities(
            innovation_portfolio, prediction_horizon
        )

        # Competitive response predictions
        competitive_predictions = await self._predict_competitive_responses(
            innovation_portfolio, prediction_horizon
        )

        # Technology evolution predictions
        technology_predictions = await self._predict_technology_evolution(
            innovation_portfolio, prediction_horizon
        )

        # Resource requirement predictions
        resource_predictions = await self._predict_resource_requirements(
            innovation_portfolio, prediction_horizon
        )

        # Portfolio optimization predictions
        optimization_predictions = await self._predict_portfolio_optimization(
            innovation_portfolio, prediction_horizon
        )

        # Scenario analysis
        scenario_analysis = await self._generate_scenario_analysis(
            innovation_portfolio, prediction_horizon
        )

        return {
            'prediction_horizon_months': prediction_horizon,
            'market_opportunities': market_predictions,
            'competitive_responses': competitive_predictions,
            'technology_evolution': technology_predictions,
            'resource_requirements': resource_predictions,
            'portfolio_optimization': optimization_predictions,
            'scenario_analysis': scenario_analysis,
            'confidence_intervals': self._calculate_prediction_confidence(
                market_predictions, competitive_predictions, technology_predictions
            )
        }

    async def _predict_market_opportunities(
        self, portfolio: List[Dict[str, Any]], horizon: int
    ) -> Dict[str, Any]:
        """Predict market opportunity evolution for innovation portfolio."""

        market_features = self._extract_market_features(portfolio)

        # Market size predictions
        market_size_model = self.prediction_models.get('market_size')
        if market_size_model:
            predicted_market_sizes = market_size_model.predict(market_features)
        else:
            predicted_market_sizes = np.ones(len(portfolio))

        # Market share predictions
        market_share_model = self.prediction_models.get('market_share')
        if market_share_model:
            predicted_market_shares = market_share_model.predict(market_features)
        else:
            predicted_market_shares = np.full(len(portfolio), 0.05)  # Default 5%

        # Market timing predictions
        timing_model = self.prediction_models.get('market_timing')
        if timing_model:
            predicted_timing = timing_model.predict(market_features)
        else:
            predicted_timing = np.full(len(portfolio), horizon / 2)

        # Combine predictions
        market_opportunities = []
        for i, innovation in enumerate(portfolio):
            opportunity = {
                'innovation_id': innovation.get('id', f'innovation_{i}'),
                'predicted_market_size': float(predicted_market_sizes[i]),
                'predicted_market_share': float(predicted_market_shares[i]),
                'predicted_time_to_market': float(predicted_timing[i]),
                'expected_revenue': float(
                    predicted_market_sizes[i] * predicted_market_shares[i]
                ),
                'market_readiness_score': self._calculate_market_readiness(
                    innovation, predicted_timing[i], horizon
                )
            }
            market_opportunities.append(opportunity)

        return {
            'individual_opportunities': market_opportunities,
            'total_addressable_market': float(np.sum(predicted_market_sizes)),
            'total_expected_revenue': float(np.sum([
                opp['expected_revenue'] for opp in market_opportunities
            ])),
            'average_time_to_market': float(np.mean(predicted_timing))
        }

    async def optimize_innovation_portfolio(
        self,
        current_portfolio: List[Dict[str, Any]],
        constraints: Dict[str, Any],
        objectives: Dict[str, float]
    ) -> Dict[str, Any]:
        """Optimize innovation portfolio allocation using predictive analytics."""

        # Extract portfolio features
        portfolio_features = self._extract_portfolio_features(current_portfolio)

        # Define optimization objective function
        def portfolio_objective(allocation_weights):
            return self._calculate_portfolio_objective(
                allocation_weights, portfolio_features, objectives
            )

        # Define constraints
        constraint_functions = self._build_constraint_functions(
            constraints, portfolio_features
        )

        # Initial allocation (current portfolio)
        initial_allocation = np.array([
            innovation.get('resource_allocation', 1.0)
            for innovation in current_portfolio
        ])
        initial_allocation = initial_allocation / np.sum(initial_allocation)

        # Optimization bounds (0-100% allocation per innovation)
        bounds = [(0.0, 1.0) for _ in current_portfolio]

        # Portfolio optimization
        optimization_result = minimize(
            portfolio_objective,
            initial_allocation,
            method='SLSQP',
            bounds=bounds,
            constraints=constraint_functions,
            options={'ftol': 1e-9, 'disp': True}
        )

        if optimization_result.success:
            optimal_allocation = optimization_result.x

            # Calculate optimization impact
            current_objective_value = portfolio_objective(initial_allocation)
            optimal_objective_value = optimization_result.fun
            improvement = (current_objective_value - optimal_objective_value) / current_objective_value

            # Generate reallocation recommendations
            reallocation_recommendations = []
            for i, innovation in enumerate(current_portfolio):
                current_allocation = initial_allocation[i]
                optimal_allocation_value = optimal_allocation[i]
                allocation_change = optimal_allocation_value - current_allocation

                if abs(allocation_change) > 0.05:  # Significant change threshold
                    recommendation = {
                        'innovation_id': innovation.get('id', f'innovation_{i}'),
                        'current_allocation': float(current_allocation),
                        'recommended_allocation': float(optimal_allocation_value),
                        'allocation_change': float(allocation_change),
                        'rationale': self._generate_allocation_rationale(
                            innovation, allocation_change, portfolio_features[i]
                        )
                    }
                    reallocation_recommendations.append(recommendation)

            return {
                'optimization_successful': True,
                'objective_improvement': float(improvement),
                'optimal_allocation': optimal_allocation.tolist(),
                'reallocation_recommendations': reallocation_recommendations,
                'portfolio_metrics': self._calculate_optimized_portfolio_metrics(
                    optimal_allocation, portfolio_features
                )
            }
        else:
            return {
                'optimization_successful': False,
                'error_message': optimization_result.message,
                'current_allocation': initial_allocation.tolist()
            }