German Tank Problem/ Panzer III Chassis Type Production

# Parameters
n_simulations = 10000
shortage_factors = {
    'manpower': 0.15, # 15% reduction in production due to manpower shortages
    'material': 0.10   # 10% reduction in specific variants due to material shortages
}

# Simulation
results_adjusted = {month: {'tanks': [], 'guns': [], 'carriers': []} for the month in months}
for _ in range(n_simulations):
    for month in months:
        prop_tanks = np.random.uniform(*proportion_tanks_range)
        prop_guns = np.random.uniform(*proportion_guns_range)
        prop_carriers = np.random.uniform(*proportion_carriers_range)

        total_prop = prop_tanks + prop_guns + prop_carriers
        prop_tanks /= total_prop
        prop_guns /= total_prop
        prop_carriers /= total_prop

        # Apply shortage factors
        adjusted_production = monthly_estimates[month] * (1 - shortage_factors['manpower'])
        adjusted_tanks = prop_tanks * adjusted_production
        adjusted_guns = prop_guns * adjusted_production * (1 - shortage_factors['material'])
        adjusted_carriers = prop_carriers * adjusted_production * (1 - shortage_factors['material'])

        results_adjusted[month]['tanks'].append(adjusted_tanks)
        results_adjusted[month]['guns'].append(adjusted_guns)
        results_adjusted[month]['carriers'].append(adjusted_carriers)

# Summary statistics
for month in months:
    print(f"Month: {month}")
    print(f"  Tanks: Mean = {np.mean(results_adjusted[month]['tanks'])}, Std Dev = {np.std(results_adjusted[month]['tanks'])}")
    print(f"  Guns: Mean = {np.mean(results_adjusted[month]['guns'])}, Std Dev = {np.std(results_adjusted[month]['guns'])}")
    print(f"  Carriers: Mean = {np.mean(results_adjusted[month]['carriers'])}, Std Dev = {np.std(results_adjusted[month]['carriers'])}")