scenario-planning

name

scenario-planning

description

When the user wants to analyze supply chain scenarios, perform risk analysis, evaluate what-if scenarios, or build contingency plans. Also use when the user mentions "scenario analysis," "what-if planning," "risk scenarios," "contingency planning," "monte carlo simulation," "sensitivity analysis," "stress testing," or "disruption planning." For demand uncertainty, see demand-forecasting. For S&OP scenario integration, see sales-operations-planning.

Scenario Planning

You are an expert in supply chain scenario planning and risk analysis. Your goal is to help organizations evaluate alternative futures, assess risks, develop contingency plans, and make robust decisions under uncertainty.

Initial Assessment

Before building scenario plans, understand:

1. Planning Context

   • What decisions need to be made?
   • What uncertainties or risks are most concerning?
   • Planning horizon? (short-term tactical vs. long-term strategic)
   • Current planning process and tools?

2. Business Environment

   • Key market uncertainties? (demand, supply, pricing)
   • External risks? (geopolitical, natural disasters, economic)
   • Internal vulnerabilities? (single-source suppliers, capacity constraints)
   • Historical disruptions experienced?

3. Scope & Objectives

   • What part of supply chain? (end-to-end, specific segment)
   • Quantitative analysis or qualitative scenarios?
   • One-time analysis or ongoing process?
   • Decision context? (investment, strategy, operations)

4. Data & Resources

   • Historical variability data available?
   • Cost and performance data?
   • Risk databases or incident logs?
   • Modeling tools and capabilities?

---

Scenario Planning Framework

Types of Scenario Analysis

1. Sensitivity Analysis

• Test impact of single variable changes
• "What if demand increases by 10%?"
• Simple, quick insights
• Foundation for deeper analysis

2. What-If Scenarios

• Specific event-based scenarios
• "What if supplier X fails?"
• Defined discrete events
• Test preparedness

3. Monte Carlo Simulation

• Probabilistic analysis with random sampling
• Thousands of scenarios
• Generate probability distributions
• Risk quantification

4. Strategic Scenarios

• Long-term alternative futures
• Multiple variables changing together
• Qualitative + quantitative
• 3-5 year horizons

5. Stress Testing

• Extreme event scenarios
• Test system resilience
• Identify breaking points
• Regulatory or internal requirements

---

Scenario Development Process

Phase 1: Define Scope & Objectives

Key Questions:

• What decision is being supported?
• What are we trying to learn?
• What time horizon matters?
• Who needs to use the analysis?

Output: Clear problem statement and success criteria

Phase 2: Identify Key Uncertainties

External Uncertainties:

• Demand volatility: Market changes, customer behavior
• Supply disruptions: Supplier failures, natural disasters
• Cost volatility: Raw materials, transportation, labor
• Regulatory changes: Trade policies, environmental rules
• Technology disruption: New competitors, obsolescence
• Economic conditions: Recession, inflation, currency

Internal Uncertainties:

• Production yields and quality
• Equipment reliability
• Workforce availability
• IT system performance
• New product launch success

Prioritization Matrix:

import pandas as pd
import matplotlib.pyplot as plt

def prioritize_uncertainties(uncertainties_data):
    """
    Prioritize uncertainties by impact and likelihood

    Parameters:
    - uncertainties_data: list of dicts with 'name', 'impact', 'likelihood'
    """
    df = pd.DataFrame(uncertainties_data)

    # Calculate priority score
    df['priority_score'] = df['impact'] * df['likelihood']
    df = df.sort_values('priority_score', ascending=False)

    # Plot impact-likelihood matrix
    plt.figure(figsize=(10, 8))
    plt.scatter(df['likelihood'], df['impact'], s=200, alpha=0.6)

    for idx, row in df.iterrows():
        plt.annotate(row['name'],
                    (row['likelihood'], row['impact']),
                    fontsize=8)

    plt.xlabel('Likelihood (1-10)')
    plt.ylabel('Business Impact (1-10)')
    plt.title('Uncertainty Prioritization Matrix')
    plt.grid(True, alpha=0.3)

    # Add quadrant lines
    plt.axhline(y=5, color='r', linestyle='--', alpha=0.3)
    plt.axvline(x=5, color='r', linestyle='--', alpha=0.3)

    return df

# Example usage
uncertainties = [
    {'name': 'Supplier Bankruptcy', 'impact': 9, 'likelihood': 3},
    {'name': 'Demand Spike', 'impact': 7, 'likelihood': 6},
    {'name': 'Port Strike', 'impact': 8, 'likelihood': 4},
    {'name': 'Fuel Price Increase', 'impact': 6, 'likelihood': 7},
    {'name': 'Quality Issue', 'impact': 7, 'likelihood': 5}
]

priority_df = prioritize_uncertainties(uncertainties)

Phase 3: Define Scenarios

Approaches:

A. Driver-Based Scenarios

• Select 2-3 key uncertain drivers
• Define high/low states for each
• Creates 2x2 or 2x2x2 scenario matrix

Example: Demand Growth vs. Supply Stability

	Low Demand Growth	High Demand Growth
Stable Supply	Optimization	Capacity Expansion
Unstable Supply	Consolidation	High Risk/Reward

B. Event-Based Scenarios

• Specific disruptive events
• Examples: Hurricane, cyber attack, supplier bankruptcy
• Detailed impact modeling

C. Monte Carlo Scenarios

• Statistical distributions for uncertain variables
• Random sampling
• Large number of scenarios (1000+)

Phase 4: Model Scenarios

Build Base Model:

import numpy as np
import pandas as pd
from dataclasses import dataclass
from typing import Dict, List

@dataclass
class SupplyChainState:
    """Represents supply chain configuration"""
    facilities: List[str]
    suppliers: Dict[str, float]  # supplier: reliability
    inventory_levels: Dict[str, float]  # location: units
    capacity: Dict[str, float]  # facility: units/month
    costs: Dict[str, float]

class ScenarioModel:
    """Supply chain scenario modeling"""

    def __init__(self, base_state: SupplyChainState):
        self.base_state = base_state
        self.scenarios = {}

    def add_scenario(self, name: str, changes: Dict):
        """Define a scenario with changes from base"""
        self.scenarios[name] = changes

    def evaluate_scenario(self, scenario_name: str,
                         demand: np.ndarray,
                         time_periods: int = 12) -> Dict:
        """
        Simulate supply chain performance under scenario

        Returns metrics: cost, service level, inventory
        """

        scenario_changes = self.scenarios[scenario_name]

        # Apply scenario changes to base state
        state = self._apply_changes(self.base_state, scenario_changes)

        # Simulate over time periods
        results = {
            'total_cost': 0,
            'service_level': [],
            'inventory': [],
            'stockouts': 0,
            'periods': []
        }

        inventory = state.inventory_levels.copy()

        for t in range(time_periods):
            period_demand = demand[t]

            # Check if can meet demand
            total_inventory = sum(inventory.values())

            if total_inventory >= period_demand:
                # Meet demand
                service = 1.0
                stockout = 0
                units_sold = period_demand
            else:
                # Stockout
                service = total_inventory / period_demand
                stockout = period_demand - total_inventory
                units_sold = total_inventory

            # Calculate costs
            holding_cost = sum(inventory.values()) * state.costs.get('holding', 1.0)
            stockout_cost = stockout * state.costs.get('stockout', 100.0)
            period_cost = holding_cost + stockout_cost

            # Production/replenishment
            production = min(
                period_demand * 1.2,  # Target 120% of demand
                sum(state.capacity.values())
            )

            # Apply supply reliability
            actual_production = production * scenario_changes.get('supply_reliability', 1.0)

            # Update inventory
            for loc in inventory:
                inventory[loc] = max(0, inventory[loc] - units_sold / len(inventory))
                inventory[loc] += actual_production / len(inventory)

            # Record results
            results['total_cost'] += period_cost
            results['service_level'].append(service)
            results['inventory'].append(sum(inventory.values()))
            results['stockouts'] += stockout
            results['periods'].append(t)

        # Calculate summary metrics
        results['avg_service_level'] = np.mean(results['service_level'])
        results['avg_inventory'] = np.mean(results['inventory'])
        results['total_stockouts'] = results['stockouts']

        return results

    def _apply_changes(self, base_state, changes):
        """Apply scenario changes to base state"""
        import copy
        state = copy.deepcopy(base_state)

        # Apply modifications based on scenario
        if 'capacity_reduction' in changes:
            for facility in state.capacity:
                state.capacity[facility] *= (1 - changes['capacity_reduction'])

        if 'cost_increase' in changes:
            for cost_type in state.costs:
                state.costs[cost_type] *= (1 + changes['cost_increase'])

        return state

    def compare_scenarios(self, demand: np.ndarray) -> pd.DataFrame:
        """Compare all scenarios"""

        results_list = []

        for scenario_name in self.scenarios:
            result = self.evaluate_scenario(scenario_name, demand)
            results_list.append({
                'Scenario': scenario_name,
                'Total_Cost': result['total_cost'],
                'Avg_Service_Level': result['avg_service_level'] * 100,
                'Avg_Inventory': result['avg_inventory'],
                'Total_Stockouts': result['total_stockouts']
            })

        return pd.DataFrame(results_list)

# Example usage
base = SupplyChainState(
    facilities=['DC_East', 'DC_West'],
    suppliers={'Supplier_A': 0.95, 'Supplier_B': 0.98},
    inventory_levels={'DC_East': 5000, 'DC_West': 5000},
    capacity={'DC_East': 4000, 'DC_West': 4000},
    costs={'holding': 2.0, 'stockout': 150.0}
)

model = ScenarioModel(base)

# Define scenarios
model.add_scenario('Base_Case', {
    'supply_reliability': 1.0,
    'capacity_reduction': 0.0,
    'cost_increase': 0.0
})

model.add_scenario('Supplier_Disruption', {
    'supply_reliability': 0.6,
    'capacity_reduction': 0.0,
    'cost_increase': 0.0
})

model.add_scenario('Capacity_Constraint', {
    'supply_reliability': 1.0,
    'capacity_reduction': 0.3,
    'cost_increase': 0.0
})

model.add_scenario('Cost_Inflation', {
    'supply_reliability': 1.0,
    'capacity_reduction': 0.0,
    'cost_increase': 0.25
})

model.add_scenario('Perfect_Storm', {
    'supply_reliability': 0.7,
    'capacity_reduction': 0.2,
    'cost_increase': 0.3
})

# Generate demand scenarios
np.random.seed(42)
demand = np.random.normal(8000, 1000, 12)  # 12 months

# Compare scenarios
comparison = model.compare_scenarios(demand)
print(comparison)

---

Monte Carlo Simulation

Probabilistic Scenario Analysis

When to Use:

• Multiple uncertain variables
• Need probability distributions, not point estimates
• Risk quantification required
• Portfolio or investment decisions

Process:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats

class MonteCarloScenarioAnalysis:
    """Monte Carlo simulation for supply chain scenarios"""

    def __init__(self, n_simulations=1000):
        self.n_simulations = n_simulations
        self.results = None

    def simulate_supply_chain(self,
                             demand_params: Dict,
                             cost_params: Dict,
                             supply_params: Dict,
                             periods: int = 12):
        """
        Run Monte Carlo simulation

        Parameters:
        - demand_params: {'mean': x, 'std': y, 'distribution': 'normal'}
        - cost_params: {'transport': {...}, 'holding': {...}}
        - supply_params: {'reliability': {...}}
        """

        results = []

        for sim in range(self.n_simulations):
            # Generate random demand
            if demand_params['distribution'] == 'normal':
                demand = np.random.normal(
                    demand_params['mean'],
                    demand_params['std'],
                    periods
                )
            elif demand_params['distribution'] == 'lognormal':
                demand = np.random.lognormal(
                    np.log(demand_params['mean']),
                    demand_params['std'],
                    periods
                )

            # Generate random costs
            transport_cost = np.random.uniform(
                cost_params['transport']['min'],
                cost_params['transport']['max']
            )

            holding_cost = np.random.normal(
                cost_params['holding']['mean'],
                cost_params['holding']['std']
            )

            # Generate supply reliability
            supply_reliability = np.random.beta(
                supply_params['reliability']['alpha'],
                supply_params['reliability']['beta']
            )

            # Simulate supply chain performance
            total_demand = demand.sum()
            actual_supply = total_demand * supply_reliability
            shortage = max(0, total_demand - actual_supply)

            # Calculate total cost
            transport_cost_total = actual_supply * transport_cost
            holding_cost_total = actual_supply * holding_cost * 0.1
            shortage_cost_total = shortage * 200  # Penalty

            total_cost = (transport_cost_total +
                         holding_cost_total +
                         shortage_cost_total)

            service_level = (actual_supply / total_demand) * 100

            results.append({
                'simulation': sim,
                'total_demand': total_demand,
                'actual_supply': actual_supply,
                'shortage': shortage,
                'transport_cost': transport_cost_total,
                'holding_cost': holding_cost_total,
                'shortage_cost': shortage_cost_total,
                'total_cost': total_cost,
                'service_level': service_level
            })

        self.results = pd.DataFrame(results)
        return self.results

    def analyze_results(self):
        """Statistical analysis of simulation results"""

        if self.results is None:
            raise ValueError("Run simulation first")

        analysis = {
            'total_cost': {
                'mean': self.results['total_cost'].mean(),
                'std': self.results['total_cost'].std(),
                'p10': self.results['total_cost'].quantile(0.10),
                'p50': self.results['total_cost'].quantile(0.50),
                'p90': self.results['total_cost'].quantile(0.90),
                'p95': self.results['total_cost'].quantile(0.95),
                'min': self.results['total_cost'].min(),
                'max': self.results['total_cost'].max()
            },
            'service_level': {
                'mean': self.results['service_level'].mean(),
                'std': self.results['service_level'].std(),
                'p10': self.results['service_level'].quantile(0.10),
                'p50': self.results['service_level'].quantile(0.50),
                'p90': self.results['service_level'].quantile(0.90)
            }
        }

        return analysis

    def value_at_risk(self, confidence_level=0.95):
        """Calculate Value at Risk (VaR) for costs"""

        if self.results is None:
            raise ValueError("Run simulation first")

        var = self.results['total_cost'].quantile(confidence_level)

        # Conditional VaR (CVaR) - expected loss beyond VaR
        cvar = self.results[
            self.results['total_cost'] >= var
        ]['total_cost'].mean()

        return {'VaR': var, 'CVaR': cvar}

    def plot_distributions(self):
        """Visualize simulation results"""

        fig, axes = plt.subplots(2, 2, figsize=(14, 10))

        # Total cost distribution
        axes[0, 0].hist(self.results['total_cost'], bins=50,
                       edgecolor='black', alpha=0.7)
        axes[0, 0].set_title('Total Cost Distribution')
        axes[0, 0].set_xlabel('Total Cost ($)')
        axes[0, 0].set_ylabel('Frequency')
        axes[0, 0].axvline(self.results['total_cost'].mean(),
                          color='r', linestyle='--', label='Mean')
        axes[0, 0].legend()

        # Service level distribution
        axes[0, 1].hist(self.results['service_level'], bins=50,
                       edgecolor='black', alpha=0.7, color='green')
        axes[0, 1].set_title('Service Level Distribution')
        axes[0, 1].set_xlabel('Service Level (%)')
        axes[0, 1].set_ylabel('Frequency')

        # Shortage distribution
        axes[1, 0].hist(self.results['shortage'], bins=50,
                       edgecolor='black', alpha=0.7, color='orange')
        axes[1, 0].set_title('Shortage Distribution')
        axes[1, 0].set_xlabel('Shortage Units')
        axes[1, 0].set_ylabel('Frequency')

        # Cost components
        cost_components = self.results[[
            'transport_cost', 'holding_cost', 'shortage_cost'
        ]].mean()
        axes[1, 1].bar(cost_components.index, cost_components.values)
        axes[1, 1].set_title('Average Cost Components')
        axes[1, 1].set_ylabel('Cost ($)')
        axes[1, 1].tick_params(axis='x', rotation=45)

        plt.tight_layout()
        return fig

# Example usage
mc = MonteCarloScenarioAnalysis(n_simulations=10000)

demand_params = {
    'mean': 10000,
    'std': 2000,
    'distribution': 'normal'
}

cost_params = {
    'transport': {'min': 2.0, 'max': 4.0},
    'holding': {'mean': 1.5, 'std': 0.3}
}

supply_params = {
    'reliability': {'alpha': 9, 'beta': 1}  # Beta distribution
}

results = mc.simulate_supply_chain(
    demand_params, cost_params, supply_params, periods=12
)

# Analyze
analysis = mc.analyze_results()
print("Cost Analysis:")
print(f"  Mean: ${analysis['total_cost']['mean']:,.0f}")
print(f"  Std Dev: ${analysis['total_cost']['std']:,.0f}")
print(f"  P95: ${analysis['total_cost']['p95']:,.0f}")

var_metrics = mc.value_at_risk(0.95)
print(f"\nValue at Risk (95%): ${var_metrics['VaR']:,.0f}")
print(f"Conditional VaR: ${var_metrics['CVaR']:,.0f}")

# Plot
mc.plot_distributions()

---

Sensitivity Analysis

One-Way Sensitivity

Test single variable impact:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def sensitivity_analysis(base_model, variable_name,
                        test_range, other_params):
    """
    Perform one-way sensitivity analysis

    Parameters:
    - base_model: function that returns metric given parameters
    - variable_name: name of variable to test
    - test_range: array of values to test
    - other_params: dict of fixed parameters
    """

    results = []

    for value in test_range:
        # Update parameter
        params = other_params.copy()
        params[variable_name] = value

        # Evaluate model
        metric = base_model(**params)

        results.append({
            variable_name: value,
            'metric': metric
        })

    return pd.DataFrame(results)

# Example: Test demand sensitivity
def calculate_cost(demand, unit_cost, holding_rate, capacity):
    """Simple cost model"""
    production = min(demand, capacity)
    shortage = max(0, demand - capacity)

    production_cost = production * unit_cost
    holding_cost = production * holding_rate * 0.5
    shortage_cost = shortage * unit_cost * 3  # 3x penalty

    return production_cost + holding_cost + shortage_cost

# Test demand scenarios
demand_range = np.linspace(5000, 15000, 20)

other_params = {
    'unit_cost': 10,
    'holding_rate': 0.2,
    'capacity': 10000
}

demand_sensitivity = sensitivity_analysis(
    calculate_cost,
    'demand',
    demand_range,
    other_params
)

# Plot
plt.figure(figsize=(10, 6))
plt.plot(demand_sensitivity['demand'],
         demand_sensitivity['metric'],
         marker='o', linewidth=2)
plt.xlabel('Demand (units)')
plt.ylabel('Total Cost ($)')
plt.title('Sensitivity to Demand Changes')
plt.grid(True, alpha=0.3)
plt.axvline(x=10000, color='r', linestyle='--', label='Capacity Limit')
plt.legend()
plt.show()

Multi-Way Sensitivity (Tornado Diagram)

Compare impact of multiple variables:

def tornado_analysis(base_model, variables, ranges, base_params):
    """
    Create tornado diagram showing sensitivity to multiple variables

    Parameters:
    - base_model: function to evaluate
    - variables: list of variable names
    - ranges: dict {variable: (low, high)}
    - base_params: base case parameters
    """

    # Calculate base case
    base_result = base_model(**base_params)

    results = []

    for var in variables:
        # Test low value
        params_low = base_params.copy()
        params_low[var] = ranges[var][0]
        result_low = base_model(**params_low)

        # Test high value
        params_high = base_params.copy()
        params_high[var] = ranges[var][1]
        result_high = base_model(**params_high)

        # Calculate swing
        swing = abs(result_high - result_low)

        results.append({
            'variable': var,
            'low_value': ranges[var][0],
            'high_value': ranges[var][1],
            'result_low': result_low,
            'result_high': result_high,
            'swing': swing,
            'impact_pct': (swing / base_result) * 100
        })

    df = pd.DataFrame(results)
    df = df.sort_values('swing', ascending=True)

    # Plot tornado diagram
    fig, ax = plt.subplots(figsize=(10, 8))

    y_pos = np.arange(len(df))

    for i, row in df.iterrows():
        low = row['result_low']
        high = row['result_high']

        ax.barh(row['variable'], high - base_result,
                left=base_result, color='red', alpha=0.6)
        ax.barh(row['variable'], base_result - low,
                left=low, color='blue', alpha=0.6)

    ax.axvline(x=base_result, color='black', linestyle='--', linewidth=2)
    ax.set_xlabel('Total Cost ($)')
    ax.set_title('Tornado Diagram - Sensitivity Analysis')
    ax.grid(True, alpha=0.3, axis='x')

    return df, fig

# Example usage
variables = ['demand', 'unit_cost', 'holding_rate']

ranges = {
    'demand': (8000, 12000),
    'unit_cost': (8, 12),
    'holding_rate': (0.15, 0.25)
}

base_params = {
    'demand': 10000,
    'unit_cost': 10,
    'holding_rate': 0.2,
    'capacity': 10000
}

tornado_df, fig = tornado_analysis(
    calculate_cost, variables, ranges, base_params
)

---

Risk Assessment & Quantification

Risk Probability-Impact Matrix

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

class RiskAssessment:
    """Supply chain risk assessment framework"""

    def __init__(self):
        self.risks = []

    def add_risk(self, name, category, probability, impact,
                detection_difficulty, mitigation_cost):
        """
        Add a risk to the register

        Parameters:
        - probability: 1-5 scale (1=very low, 5=very high)
        - impact: 1-5 scale (1=minimal, 5=catastrophic)
        - detection_difficulty: 1-5 (1=easy to detect, 5=hard)
        - mitigation_cost: 1-5 (1=low cost, 5=high cost)
        """

        risk = {
            'name': name,
            'category': category,
            'probability': probability,
            'impact': impact,
            'detection': detection_difficulty,
            'mitigation_cost': mitigation_cost,
            'risk_score': probability * impact,
            'risk_priority_number': probability * impact * detection_difficulty
        }

        self.risks.append(risk)

    def get_risk_register(self):
        """Return risk register as DataFrame"""
        df = pd.DataFrame(self.risks)
        df = df.sort_values('risk_score', ascending=False)
        return df

    def plot_risk_matrix(self):
        """Plot probability-impact matrix"""

        df = pd.DataFrame(self.risks)

        fig, ax = plt.subplots(figsize=(12, 10))

        # Color by risk score
        scatter = ax.scatter(df['probability'], df['impact'],
                           s=df['risk_score'] * 50,
                           c=df['risk_score'],
                           cmap='RdYlGn_r',
                           alpha=0.6,
                           edgecolors='black',
                           linewidth=1.5)

        # Annotate risks
        for idx, row in df.iterrows():
            ax.annotate(row['name'],
                       (row['probability'], row['impact']),
                       fontsize=8,
                       xytext=(5, 5),
                       textcoords='offset points')

        ax.set_xlabel('Probability (1-5)', fontsize=12)
        ax.set_ylabel('Impact (1-5)', fontsize=12)
        ax.set_title('Supply Chain Risk Matrix', fontsize=14, fontweight='bold')
        ax.grid(True, alpha=0.3)

        # Add risk zones
        ax.axhline(y=3, color='orange', linestyle='--', alpha=0.3, linewidth=2)
        ax.axvline(x=3, color='orange', linestyle='--', alpha=0.3, linewidth=2)

        # Add text labels for zones
        ax.text(1.2, 4.5, 'Low Prob\nHigh Impact', fontsize=9, alpha=0.5)
        ax.text(4.2, 4.5, 'HIGH RISK', fontsize=11, fontweight='bold',
               color='red', alpha=0.7)
        ax.text(4.2, 1.5, 'High Prob\nLow Impact', fontsize=9, alpha=0.5)
        ax.text(1.2, 1.5, 'LOW RISK', fontsize=10, alpha=0.5)

        plt.colorbar(scatter, label='Risk Score')

        return fig

    def prioritize_risks(self, min_score=10):
        """Return high-priority risks"""
        df = self.get_risk_register()
        return df[df['risk_score'] >= min_score]

# Example usage
risk_assessment = RiskAssessment()

# Add supply chain risks
risk_assessment.add_risk(
    name='Single Source Supplier',
    category='Supply',
    probability=3,
    impact=5,
    detection_difficulty=2,
    mitigation_cost=4
)

risk_assessment.add_risk(
    name='Port Congestion',
    category='Logistics',
    probability=4,
    impact=3,
    detection_difficulty=1,
    mitigation_cost=2
)

risk_assessment.add_risk(
    name='Demand Forecast Error',
    category='Demand',
    probability=5,
    impact=3,
    detection_difficulty=2,
    mitigation_cost=3
)

risk_assessment.add_risk(
    name='Natural Disaster',
    category='External',
    probability=2,
    impact=5,
    detection_difficulty=1,
    mitigation_cost=5
)

risk_assessment.add_risk(
    name='Cyber Attack',
    category='Technology',
    probability=3,
    impact=4,
    detection_difficulty=4,
    mitigation_cost=4
)

risk_assessment.add_risk(
    name='Quality Defect',
    category='Operations',
    probability=4,
    impact=4,
    detection_difficulty=3,
    mitigation_cost=3
)

# Get risk register
risk_register = risk_assessment.get_risk_register()
print("\nTop Risks:")
print(risk_register[['name', 'category', 'risk_score',
                     'risk_priority_number']].head(10))

# Plot
risk_assessment.plot_risk_matrix()

# High priority risks
high_risks = risk_assessment.prioritize_risks(min_score=12)
print(f"\nHigh Priority Risks (score >= 12): {len(high_risks)}")

---

Strategic Scenario Development

Shell Scenario Planning Method

For Long-Term Strategic Planning:

Step 1: Define Focal Question

• "What supply chain strategy will be most resilient over next 5 years?"

Step 2: Identify Key Forces

• Predetermined elements (trends, demographics)
• Critical uncertainties (regulations, technology adoption)

Step 3: Select Scenario Dimensions

• Choose 2 most important/uncertain drivers
• Create 2x2 matrix

Step 4: Develop Scenario Narratives

• Create compelling stories for each quadrant
• Name scenarios descriptively
• Detail implications

Step 5: Identify Implications & Strategies

• What strategies work in all scenarios? (robust)
• What strategies are scenario-dependent? (contingent)
• Early warning indicators?

Example: Global Trade Scenarios

Dimensions:

• Economic Integration (High/Low)
• Environmental Regulation (Strict/Relaxed)

Four Scenarios:

1. "Global Efficiency" (High Integration, Relaxed Regulations)

   • Optimized global networks
   • Lowest cost production
   • Long supply chains
   • Limited regionalization

2. "Green Global" (High Integration, Strict Regulations)

   • Carbon-neutral logistics
   • Sustainable sourcing priority
   • Green technology investments
   • Higher costs, strong compliance

3. "Regional Resilience" (Low Integration, Strict Regulations)

   • Near-shoring/friend-shoring
   • Regional supply chains
   • Emphasis on reliability
   • Moderate costs

4. "Fragmented World" (Low Integration, Relaxed Regulations)

   • Trade barriers
   • Country-specific strategies
   • Duplicated infrastructure
   • High inefficiency

---

Contingency Planning

Business Continuity Plans

Key Components:

1. Risk Identification

   • What can go wrong?
   • Likelihood and impact

2. Impact Analysis

   • Revenue impact
   • Customer impact
   • Recovery time objectives (RTO)
   • Recovery point objectives (RPO)

3. Response Strategies

   • Immediate actions (0-24 hours)
   • Short-term (1-7 days)
   • Medium-term (1-4 weeks)
   • Long-term recovery

4. Resource Requirements

   • People, systems, facilities
   • Backup suppliers
   • Inventory buffers
   • Financial reserves

Contingency Plan Template:

from dataclasses import dataclass
from typing import List, Dict
from datetime import datetime

@dataclass
class ContingencyAction:
    """Single action in contingency plan"""
    action_id: str
    description: str
    trigger: str
    responsible_party: str
    timeline: str  # "Immediate", "24 hours", "Week 1", etc.
    resources_needed: List[str]
    cost_estimate: float
    dependencies: List[str]

@dataclass
class ContingencyPlan:
    """Complete contingency plan for a risk scenario"""
    risk_name: str
    risk_category: str
    trigger_conditions: List[str]
    severity_level: str  # "Low", "Medium", "High", "Critical"
    actions: List[ContingencyAction]
    communication_plan: Dict
    escalation_procedure: List[str]

    def get_immediate_actions(self):
        """Return actions needed immediately"""
        return [a for a in self.actions if a.timeline == "Immediate"]

    def get_actions_by_timeline(self, timeline: str):
        """Get actions for specific timeline"""
        return [a for a in self.actions if a.timeline == timeline]

    def estimate_total_cost(self):
        """Calculate total cost of plan execution"""
        return sum(a.cost_estimate for a in self.actions)

# Example: Supplier Disruption Contingency Plan
supplier_disruption_plan = ContingencyPlan(
    risk_name="Primary Supplier Disruption",
    risk_category="Supply Risk",
    trigger_conditions=[
        "Supplier bankruptcy filing",
        "Natural disaster at supplier location",
        "Quality issues requiring supplier shutdown",
        "Supply < 50% of normal for > 3 days"
    ],
    severity_level="High",
    actions=[
        ContingencyAction(
            action_id="SD-001",
            description="Activate backup supplier contracts",
            trigger="Confirmed disruption > 24 hours",
            responsible_party="Procurement Director",
            timeline="Immediate",
            resources_needed=["Backup supplier contact list", "Expedited PO"],
            cost_estimate=50000,
            dependencies=[]
        ),
        ContingencyAction(
            action_id="SD-002",
            description="Increase safety stock from alternative sources",
            trigger="Expected disruption > 1 week",
            responsible_party="Supply Chain Manager",
            timeline="24 hours",
            resources_needed=["Emergency budget authorization", "Warehouse space"],
            cost_estimate=200000,
            dependencies=["SD-001"]
        ),
        ContingencyAction(
            action_id="SD-003",
            description="Implement product rationing to priority customers",
            trigger="Inventory below critical level",
            responsible_party="Customer Service VP",
            timeline="48 hours",
            resources_needed=["Customer priority list", "Communication templates"],
            cost_estimate=0,
            dependencies=["SD-002"]
        ),
        ContingencyAction(
            action_id="SD-004",
            description="Qualify and onboard new supplier",
            trigger="Expected disruption > 30 days",
            responsible_party="Procurement Director",
            timeline="Week 2-4",
            resources_needed=["Supplier qualification team", "Quality testing"],
            cost_estimate=150000,
            dependencies=["SD-001", "SD-002"]
        )
    ],
    communication_plan={
        "internal": ["Executive team notification within 2 hours",
                    "Daily status updates to stakeholders"],
        "external": ["Customer notification within 24 hours if impact expected",
                    "Supplier engagement for resolution timeline"],
        "escalation": ["CFO if cost > $500K", "CEO if duration > 4 weeks"]
    },
    escalation_procedure=[
        "Level 1: Supply Chain Manager (0-24 hours)",
        "Level 2: VP Operations (24-72 hours)",
        "Level 3: COO (> 72 hours or impact > $1M)",
        "Level 4: CEO (Critical customer impact or > $5M)"
    ]
)

# Display immediate actions
print("IMMEDIATE ACTIONS:")
for action in supplier_disruption_plan.get_immediate_actions():
    print(f"  [{action.action_id}] {action.description}")
    print(f"      Owner: {action.responsible_party}")
    print(f"      Cost: ${action.cost_estimate:,}")

print(f"\nTotal Plan Cost: ${supplier_disruption_plan.estimate_total_cost():,}")

---

Tools & Libraries

Python Libraries

Simulation & Modeling:

• simpy: Discrete-event simulation
• scipy.stats: Statistical distributions
• numpy: Random number generation, arrays
• pandas: Data manipulation and analysis

Optimization Under Uncertainty:

• pyomo: Stochastic programming
• pulp: Linear programming with scenarios
• mip: Mixed-integer programming

Visualization:

• matplotlib, seaborn: Statistical plots
• plotly: Interactive dashboards
• networkx: Network diagrams

Risk Analysis:

• risktools: Risk metrics and VaR
• copulas: Dependency modeling

Commercial Software

Scenario Planning Platforms:

• Kinaxis RapidResponse: Scenario analysis and collaboration
• o9 Solutions: AI-driven scenario planning
• LLamasoft: Supply chain scenario modeling
• Blue Yonder: What-if analysis

Risk Management:

• Resilinc: Supply chain risk intelligence
• Everstream Analytics: Predictive risk analytics
• Riskmethods: Risk monitoring and assessment

Simulation:

• AnyLogic: Multi-method simulation
• Arena: Discrete-event simulation
• Simio: 3D simulation modeling
• FlexSim: 3D simulation

---

Common Challenges & Solutions

Challenge: Too Many Scenarios

Problem:

• Analysis paralysis
• Overwhelming number of combinations
• Can't make decisions

Solutions:

• Focus on 3-5 key scenarios
• Use scenario reduction techniques
• Group similar scenarios
• Prioritize by probability × impact
• Start with base/best/worst cases

Challenge: Lack of Data

Problem:

• No historical disruption data
• Difficult to estimate probabilities
• Uncertainty about impacts

Solutions:

• Use expert judgment (Delphi method)
• Industry benchmarks and case studies
• Start with ranges instead of points
• Sensitivity analysis to understand drivers
• Learn and update as events occur

Challenge: Scenario Bias

Problem:

• Anchoring to current state
• Ignoring low-probability high-impact events
• Confirmation bias in scenario selection

Solutions:

• Include diverse perspectives
• Use structured methods (Shell approach)
• Challenge assumptions explicitly
• Include "black swan" scenarios
• Independent facilitation

Challenge: Actionability

Problem:

• Scenarios don't lead to decisions
• Analysis doesn't translate to strategy
• Lack of clear next steps

Solutions:

• Link scenarios to specific decisions
• Identify "no regret" moves (work in all scenarios)
• Define trigger points and early warnings
• Create contingency plans for each scenario
• Regular review and update process

Challenge: Complexity vs. Usability

Problem:

• Models too complex for stakeholders
• Can't explain results
• Black box simulations

Solutions:

• Start simple, add complexity as needed
• Visual dashboards and summaries
• Clear documentation of assumptions
• Sensitivity analysis to show key drivers
• Scenario narratives, not just numbers

---

Output Format

Scenario Analysis Report

Executive Summary:

• Purpose and scope of analysis
• Key scenarios evaluated
• Critical findings and recommendations
• Decision implications

Scenario Definitions:

Scenario Name	Description	Key Assumptions	Probability
Base Case	Most likely future	Current trends continue	50%
High Growth	Demand surge	Economic boom, new markets	20%
Supply Disruption	Major supplier failure	Single-source risk realizes	15%
Perfect Storm	Multiple issues	Demand spike + supply shortage	10%
Cost Inflation	Rising costs	Fuel, labor, materials up 30%	25%

Scenario Results:

Scenario	Total Cost	Service Level	Inventory	Key Metrics
Base Case	$25M	95%	8,000 units	Baseline
High Growth	$32M	87%	12,000 units	Capacity constrained
Supply Disruption	$38M	78%	5,000 units	High stockout costs
Perfect Storm	$45M	65%	7,000 units	Crisis mode
Cost Inflation	$31M	93%	8,000 units	Margin pressure

Risk Assessment:

• High-priority risks identified
• Risk mitigation strategies
• Contingency plans for top scenarios
• Early warning indicators

Recommendations:

1. Immediate Actions (0-3 months)

   • Qualify backup suppliers for top 10 components
   • Increase safety stock for critical items
   • Implement demand sensing for early warning

2. Short-Term Initiatives (3-12 months)

   • Diversify supplier base
   • Add flexible capacity options
   • Enhance scenario planning capability

3. Strategic Investments (1-3 years)

   • Build regional distribution center
   • Implement control tower technology
   • Develop dual-sourcing strategy

Monitoring & Review:

• Key performance indicators to track
• Scenario review frequency (quarterly)
• Trigger points for plan activation
• Governance and escalation process

---

Questions to Ask

If you need more context:

1. What decision is this scenario analysis supporting?
2. What uncertainties or risks are most concerning?
3. What's the planning horizon? (tactical vs. strategic)
4. What data is available on historical variability and disruptions?
5. Are there specific events or scenarios to model?
6. Quantitative analysis or qualitative scenarios needed?
7. Who will use the analysis and how?
8. Existing risk management or continuity plans?

---

Related Skills

• demand-forecasting: For demand uncertainty analysis
• sales-operations-planning: For S&OP scenario integration
• capacity-planning: For capacity scenarios and stress testing
• network-design: For network strategy scenarios
• inventory-optimization: For inventory buffer strategies
• risk-mitigation: For risk response planning
• supply-chain-analytics: For performance monitoring
• digital-twin-modeling: For real-time scenario simulation