import numpy as np
from dataclasses import dataclass
from typing import Optional

@dataclass
class InventoryParameters:
    annual_demand: float       # Annual demand quantity
    order_cost: float          # Cost per order
    holding_cost_rate: float   # Inventory holding cost rate (percentage of unit price)
    unit_price: float          # Unit price
    lead_time_days: int        # Procurement lead time (days)
    demand_std_dev: float      # Demand standard deviation
    service_level: float       # Service level (e.g., 0.95 for 95%)

class InventoryManager:
    def __init__(self, params: InventoryParameters):
        self.params = params

    def calculate_eoq(self) -> float:
        """
        Calculate Economic Order Quantity (EOQ)
        EOQ = sqrt(2 * D * S / H)
        """
        d = self.params.annual_demand
        s = self.params.order_cost
        h = self.params.unit_price * self.params.holding_cost_rate
        eoq = np.sqrt(2 * d * s / h)
        return round(eoq)

    def calculate_safety_stock(self) -> float:
        """
        Calculate safety stock
        SS = Z * sigma_dLT
        Z: Z-value corresponding to the service level
        sigma_dLT: Standard deviation of demand during lead time
        """
        from scipy.stats import norm
        z = norm.ppf(self.params.service_level)
        lead_time_factor = np.sqrt(self.params.lead_time_days / 365)
        sigma_dlt = self.params.demand_std_dev * lead_time_factor
        safety_stock = z * sigma_dlt
        return round(safety_stock)

    def calculate_reorder_point(self) -> float:
        """
        Calculate Reorder Point (ROP)
        ROP = daily demand x lead time + safety stock
        """
        daily_demand = self.params.annual_demand / 365
        rop = daily_demand * self.params.lead_time_days + self.calculate_safety_stock()
        return round(rop)

    def analyze_dead_stock(self, inventory_df):
        """
        Dead stock analysis and disposition recommendations
        """
        dead_stock = inventory_df[
            (inventory_df['last_movement_days'] > 180) |
            (inventory_df['turnover_rate'] < 1.0)
        ]

        recommendations = []
        for _, item in dead_stock.iterrows():
            if item['last_movement_days'] > 365:
                action = 'Recommend write-off or discounted disposal'
                urgency = 'High'
            elif item['last_movement_days'] > 270:
                action = 'Contact supplier for return or exchange'
                urgency = 'Medium'
            else:
                action = 'Markdown sale or internal transfer to consume'
                urgency = 'Low'

            recommendations.append({
                'sku': item['sku'],
                'quantity': item['quantity'],
                'value': item['quantity'] * item['unit_price'],       # Inventory value
                'idle_days': item['last_movement_days'],              # Days idle
                'action': action,                                      # Recommended action
                'urgency': urgency                                     # Urgency level
            })

        return recommendations

    def inventory_strategy_report(self):
        """
        Generate inventory strategy report
        """
        eoq = self.calculate_eoq()
        safety_stock = self.calculate_safety_stock()
        rop = self.calculate_reorder_point()
        annual_orders = round(self.params.annual_demand / eoq)
        total_cost = (
            self.params.annual_demand * self.params.unit_price +                    # Procurement cost
            annual_orders * self.params.order_cost +                                 # Ordering cost
            (eoq / 2 + safety_stock) * self.params.unit_price *
            self.params.holding_cost_rate                                             # Holding cost
        )

        return {
            'eoq': eoq,                           # Economic Order Quantity
            'safety_stock': safety_stock,          # Safety stock
            'reorder_point': rop,                  # Reorder point
            'annual_orders': annual_orders,        # Orders per year
            'total_annual_cost': round(total_cost, 2),  # Total annual cost
            'avg_inventory': round(eoq / 2 + safety_stock),  # Average inventory level
            'inventory_turns': round(self.params.annual_demand / (eoq / 2 + safety_stock), 1)  # Inventory turnover
        }

class SupplyChainDigitalization:
    """
    Supply chain digital maturity assessment and roadmap planning
    """

    # Comparison of major ERP systems in China
    ERP_SYSTEMS = {
        'SAP': {
            'target': 'Large conglomerates / foreign-invested enterprises',
            'modules': ['MM (Materials Management)', 'PP (Production Planning)', 'SD (Sales & Distribution)', 'WM (Warehouse Management)'],
            'cost': 'Starting from millions of RMB',
            'implementation': '6-18 months',
            'strength': 'Comprehensive functionality, rich industry best practices',
            'weakness': 'High implementation cost, complex customization'
        },
        'Yonyou U8+ / YonBIP': {
            'target': 'Mid-to-large private enterprises',
            'modules': ['Procurement Management', 'Inventory Management', 'Supply Chain Collaboration', 'Smart Manufacturing'],
            'cost': 'Hundreds of thousands to millions of RMB',
            'implementation': '3-9 months',
            'strength': 'Strong localization, excellent tax system integration',
            'weakness': 'Less experience with large-scale projects'
        },
        'Kingdee Cloud Galaxy / Cosmic': {
            'target': 'Mid-size growth companies',
            'modules': ['Procurement Management', 'Warehousing & Logistics', 'Supply Chain Collaboration', 'Quality Management'],
            'cost': 'Hundreds of thousands to millions of RMB',
            'implementation': '2-6 months',
            'strength': 'Fast SaaS deployment, excellent mobile experience',
            'weakness': 'Limited deep customization capability'
        }
    }

    # SRM procurement management systems
    SRM_PLATFORMS = {
        'ZhenYun (甄云科技)': 'Full-process digital procurement, ideal for manufacturing',
        'QiQiTong (企企通)': 'Supplier collaboration platform, focused on SMEs',
        'ZhuJiCai (筑集采)': 'Specialized procurement platform for the construction industry',
        'Yonyou Procurement Cloud (用友采购云)': 'Deep integration with Yonyou ERP',
        'SAP Ariba': 'Global procurement network, ideal for multinational enterprises'
    }

    def assess_digital_maturity(self, company_profile: dict) -> dict:
        """
        Assess enterprise supply chain digital maturity (Level 1-5)
        """
        dimensions = {
            'procurement_digitalization': self._assess_procurement(company_profile),
            'inventory_visibility': self._assess_inventory(company_profile),
            'supplier_collaboration': self._assess_supplier_collab(company_profile),
            'logistics_tracking': self._assess_logistics(company_profile),
            'data_analytics': self._assess_analytics(company_profile)
        }

        avg_score = sum(dimensions.values()) / len(dimensions)

        roadmap = []
        if avg_score < 2:
            roadmap = ['Deploy ERP base modules first', 'Establish master data standards', 'Implement electronic approval workflows']
        elif avg_score < 3:
            roadmap = ['Deploy SRM system', 'Integrate ERP and SRM data', 'Build supplier portal']
        elif avg_score < 4:
            roadmap = ['Supply chain visibility dashboard', 'Intelligent replenishment alerts', 'Supplier collaboration platform']
        else:
            roadmap = ['AI demand forecasting', 'Supply chain digital twin', 'Automated procurement decisions']

        return {
            'dimensions': dimensions,
            'overall_score': round(avg_score, 1),
            'maturity_level': self._get_level_name(avg_score),
            'roadmap': roadmap
        }

    def _get_level_name(self, score):
        if score < 1.5: return 'L1 - Manual Stage'
        elif score < 2.5: return 'L2 - Informatization Stage'
        elif score < 3.5: return 'L3 - Digitalization Stage'
        elif score < 4.5: return 'L4 - Intelligent Stage'
        else: return 'L5 - Autonomous Stage'

## Cost Reduction Strategy Matrix

### Short-Term Savings (0-3 months to realize)
- **Commercial negotiation**: Leverage competitive quotes for price reduction, negotiate payment term improvements (e.g., Net 30 → Net 60)
- **Consolidated purchasing**: Aggregate similar requirements to leverage volume discounts (typically 5-15% savings)
- **Payment term optimization**: Early payment discounts (2/10 net 30), or extended terms to improve cash flow

### Mid-Term Savings (3-12 months to realize)
- **VA/VE (Value Analysis / Value Engineering)**: Analyze product function vs. cost, optimize design without compromising functionality
- **Material substitution**: Find lower-cost alternative materials with equivalent performance (e.g., engineering plastics replacing metal parts)
- **Process optimization**: Jointly improve manufacturing processes with suppliers to increase yield and reduce processing costs
- **Supplier consolidation**: Reduce supplier count, concentrate volume with top suppliers in exchange for better pricing

### Long-Term Savings (12+ months to realize)
- **Vertical integration**: Make-or-buy decisions for critical components
- **Supply chain restructuring**: Shift production to lower-cost regions, optimize logistics networks
- **Joint development**: Co-develop new products/processes with suppliers, sharing cost reduction benefits
- **Digital procurement**: Reduce transaction costs and manual overhead through electronic procurement processes

class SupplyChainRiskManager:
    """
    Supply chain risk identification, assessment, and response
    """

    RISK_CATEGORIES = {
        'supply_disruption_risk': {
            'indicators': ['Supplier concentration', 'Single-source material ratio', 'Supplier financial health'],
            'mitigation': ['Multi-source procurement strategy', 'Safety stock reserves', 'Alternative supplier development']
        },
        'quality_risk': {
            'indicators': ['Incoming defect rate trend', 'Customer complaint rate', 'Quality system certification status'],
            'mitigation': ['Strengthen incoming inspection', 'Supplier quality improvement plan', 'Quality traceability system']
        },
        'price_volatility_risk': {
            'indicators': ['Commodity price index', 'Currency fluctuation range', 'Supplier price increase warnings'],
            'mitigation': ['Long-term price-lock contracts', 'Futures/options hedging', 'Alternative material reserves']
        },
        'geopolitical_risk': {
            'indicators': ['Trade policy changes', 'Tariff adjustments', 'Export control lists'],
            'mitigation': ['Supply chain diversification', 'Nearshoring/friendshoring', 'Domestic substitution plans (国产替代)']
        },
        'logistics_risk': {
            'indicators': ['Capacity tightness index', 'Port congestion level', 'Extreme weather warnings'],
            'mitigation': ['Multimodal transport solutions', 'Advance stocking', 'Regional warehousing strategy']
        }
    }

    def risk_assessment(self, supplier_data: dict) -> dict:
        """
        Comprehensive supplier risk assessment
        """
        risk_scores = {}

        # Supply concentration risk
        if supplier_data.get('spend_share', 0) > 0.3:
            risk_scores['concentration_risk'] = 'High'
        elif supplier_data.get('spend_share', 0) > 0.15:
            risk_scores['concentration_risk'] = 'Medium'
        else:
            risk_scores['concentration_risk'] = 'Low'

        # Single-source risk
        if supplier_data.get('alternative_suppliers', 0) == 0:
            risk_scores['single_source_risk'] = 'High'
        elif supplier_data.get('alternative_suppliers', 0) == 1:
            risk_scores['single_source_risk'] = 'Medium'
        else:
            risk_scores['single_source_risk'] = 'Low'

        # Financial health risk
        credit_score = supplier_data.get('credit_score', 50)
        if credit_score < 40:
            risk_scores['financial_risk'] = 'High'
        elif credit_score < 60:
            risk_scores['financial_risk'] = 'Medium'
        else:
            risk_scores['financial_risk'] = 'Low'

        # Overall risk level
        high_count = list(risk_scores.values()).count('High')
        if high_count >= 2:
            overall = 'Red Alert - Immediate contingency plan required'
        elif high_count == 1:
            overall = 'Orange Watch - Improvement plan needed'
        else:
            overall = 'Green Normal - Continue routine monitoring'

        return {
            'detail_scores': risk_scores,
            'overall_risk': overall,
            'recommended_actions': self._get_actions(risk_scores)
        }

    def _get_actions(self, scores):
        actions = []
        if scores.get('concentration_risk') == 'High':
            actions.append('Immediately begin alternative supplier development — target qualification within 3 months')
        if scores.get('single_source_risk') == 'High':
            actions.append('Single-source materials must have at least 1 alternative supplier developed within 6 months')
        if scores.get('financial_risk') == 'High':
            actions.append('Shorten payment terms to prepayment or cash-on-delivery, increase incoming inspection frequency')
        return actions

# Review existing supplier roster and procurement spend analysis
# Assess supply chain risk hotspots and bottleneck stages
# Audit inventory health and dead stock levels

# [Period] Supply Chain Management Report

## Summary

### Core Operating Metrics
**Total procurement spend**: ¥[amount] (YoY: [+/-]%, Budget variance: [+/-]%)
**Supplier count**: [count] (New: [count], Phased out: [count])
**Incoming quality pass rate**: [%] (Target: [%], Trend: [up/down])
**On-time delivery rate**: [%] (Target: [%], Trend: [up/down])

### Inventory Health
**Total inventory value**: ¥[amount] (Days of inventory: [days], Target: [days])
**Dead stock**: ¥[amount] (Share: [%], Disposition progress: [%])
**Shortage alerts**: [count] (Production orders affected: [count])

### Cost Reduction Results
**Cumulative savings**: ¥[amount] (Target completion rate: [%])
**Cost reduction projects**: [completed/in progress/planned]
**Primary savings drivers**: [Commercial negotiation / Material substitution / Process optimization / Consolidated purchasing]

### Risk Alerts
**High-risk suppliers**: [count] (with detailed list and response plans)
**Raw material price trends**: [Key material price movements and hedging strategies]
**Supply disruption events**: [count] (Impact assessment and resolution status)

## Action Items
1. **Urgent**: [Action, impact, and timeline]
2. **Short-term**: [Improvement initiatives within 30 days]
3. **Strategic**: [Long-term supply chain optimization directions]

**Supply Chain Strategist**: [Name]
**Report date**: [Date]
**Coverage period**: [Period]
**Next review**: [Planned review date]