kishorkukreja/awesome-supply-chain/strategic-sourcing

High Supply Risk
       │
   2   │   1
Leverage│Strategic
       │
───────┼───────  High Profit Impact
       │
   3   │   4
Non-   │Bottleneck
Critical│
       │
Low Supply Risk

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
class KraljicAnalysis:
    """Kraljic portfolio positioning for categories"""
    def __init__(self, categories_df):
        """
        categories_df: DataFrame with category, spend, supply_risk, profit_impact
        """
        self.categories = categories_df
    def classify_category(self, supply_risk, profit_impact):
        """Classify category into Kraljic quadrant"""
        if supply_risk >= 50 and profit_impact >= 50:
            return 'Strategic'
        elif supply_risk < 50 and profit_impact >= 50:
            return 'Leverage'
        elif supply_risk < 50 and profit_impact < 50:
            return 'Non-Critical'
        else:  # supply_risk >= 50 and profit_impact < 50
            return 'Bottleneck'
    def add_classifications(self):
        """Add Kraljic classification to categories"""
        self.categories['kraljic_quadrant'] = self.categories.apply(
            lambda row: self.classify_category(
                row['supply_risk'],
                row['profit_impact']
            ),
            axis=1
        )
        return self.categories
    def plot_portfolio(self):
        """Create Kraljic portfolio matrix visualization"""
        fig, ax = plt.subplots(figsize=(12, 8))
        # Color by quadrant
        colors = {
            'Strategic': 'red',
            'Leverage': 'green',
            'Bottleneck': 'orange',
            'Non-Critical': 'blue'
        }
        for quadrant in colors:
            data = self.categories[self.categories['kraljic_quadrant'] == quadrant]
            ax.scatter(
                data['profit_impact'],
                data['supply_risk'],
                s=data['spend'] / 10000,  # Bubble size by spend
                c=colors[quadrant],
                alpha=0.6,
                label=quadrant
            )
            # Add category labels
            for _, row in data.iterrows():
                ax.annotate(
                    row['category'],
                    (row['profit_impact'], row['supply_risk']),
                    fontsize=8,
                    ha='center'
                )
        # Quadrant lines
        ax.axvline(50, color='gray', linestyle='--', alpha=0.5)
        ax.axhline(50, color='gray', linestyle='--', alpha=0.5)
        # Labels
        ax.set_xlabel('Profit Impact (Spend, Cost Criticality)', fontsize=12)
        ax.set_ylabel('Supply Risk (Availability, Complexity)', fontsize=12)
        ax.set_title('Kraljic Portfolio Matrix', fontsize=14, weight='bold')
        ax.set_xlim(0, 100)
        ax.set_ylim(0, 100)
        ax.legend()
        ax.grid(True, alpha=0.3)
        # Quadrant labels
        ax.text(75, 75, 'Strategic', ha='center', fontsize=11, weight='bold')
        ax.text(25, 75, 'Bottleneck', ha='center', fontsize=11, weight='bold')
        ax.text(75, 25, 'Leverage', ha='center', fontsize=11, weight='bold')
        ax.text(25, 25, 'Non-Critical', ha='center', fontsize=11, weight='bold')
        plt.tight_layout()
        return fig
# Example usage
categories = pd.DataFrame({
    'category': ['IT Hardware', 'Office Supplies', 'Specialized Components',
                'Raw Materials', 'Consulting', 'MRO'],
    'spend': [2000000, 500000, 800000, 5000000, 1500000, 300000],
    'supply_risk': [30, 20, 80, 40, 60, 75],  # 0-100
    'profit_impact': [70, 40, 35, 90, 80, 25]  # 0-100
})
kraljic = KraljicAnalysis(categories)
classified = kraljic.add_classifications()
print("\nKraljic Classification:")
print(classified[['category', 'kraljic_quadrant', 'spend']])
# fig = kraljic.plot_portfolio()
# plt.show()

class ValueLevers:
    """Identify and quantify value levers for category"""
    def __init__(self, category, baseline_spend):
        self.category = category
        self.baseline_spend = baseline_spend
        self.levers = []
    def add_price_reduction(self, current_price, target_price, volume,
                           confidence=0.8, timeline_months=6):
        """Add price reduction lever"""
        savings_per_unit = current_price - target_price
        annual_savings = savings_per_unit * volume
        risk_adjusted = annual_savings * confidence
        self.levers.append({
            'lever_type': 'Price Reduction',
            'description': f'Negotiate price from ${current_price} to ${target_price}',
            'gross_savings': annual_savings,
            'confidence_%': confidence * 100,
            'risk_adjusted_savings': risk_adjusted,
            'implementation_timeline': timeline_months,
            'implementation_effort': 'Medium'
        })
    def add_demand_reduction(self, reduction_pct, rationale,
                            confidence=0.6, timeline_months=12):
        """Add demand management lever"""
        annual_savings = self.baseline_spend * reduction_pct
        risk_adjusted = annual_savings * confidence
        self.levers.append({
            'lever_type': 'Demand Management',
            'description': rationale,
            'gross_savings': annual_savings,
            'confidence_%': confidence * 100,
            'risk_adjusted_savings': risk_adjusted,
            'implementation_timeline': timeline_months,
            'implementation_effort': 'High'
        })
    def add_specification_change(self, savings_amount, description,
                                confidence=0.7, timeline_months=9):
        """Add specification optimization lever"""
        risk_adjusted = savings_amount * confidence
        self.levers.append({
            'lever_type': 'Specification Change',
            'description': description,
            'gross_savings': savings_amount,
            'confidence_%': confidence * 100,
            'risk_adjusted_savings': risk_adjusted,
            'implementation_timeline': timeline_months,
            'implementation_effort': 'High'
        })
    def add_process_improvement(self, savings_amount, description,
                               confidence=0.9, timeline_months=3):
        """Add process efficiency lever"""
        risk_adjusted = savings_amount * confidence
        self.levers.append({
            'lever_type': 'Process Improvement',
            'description': description,
            'gross_savings': savings_amount,
            'confidence_%': confidence * 100,
            'risk_adjusted_savings': risk_adjusted,
            'implementation_timeline': timeline_months,
            'implementation_effort': 'Medium'
        })
    def add_supplier_consolidation(self, expected_discount, description,
                                  confidence=0.75, timeline_months=6):
        """Add supplier consolidation lever"""
        annual_savings = self.baseline_spend * expected_discount
        risk_adjusted = annual_savings * confidence
        self.levers.append({
            'lever_type': 'Supplier Consolidation',
            'description': description,
            'gross_savings': annual_savings,
            'confidence_%': confidence * 100,
            'risk_adjusted_savings': risk_adjusted,
            'implementation_timeline': timeline_months,
            'implementation_effort': 'High'
        })
    def get_value_lever_summary(self):
        """Get prioritized value lever summary"""
        if not self.levers:
            return None
        df = pd.DataFrame(self.levers)
        df = df.sort_values('risk_adjusted_savings', ascending=False)
        # Add cumulative savings
        df['cumulative_savings'] = df['risk_adjusted_savings'].cumsum()
        total_gross = df['gross_savings'].sum()
        total_risk_adjusted = df['risk_adjusted_savings'].sum()