"algo-sc-newsvendor"

Newsvendor Model

Overview

The newsvendor model determines optimal order quantity for a single selling period with uncertain demand. Balances overage cost (Co = cost - salvage) against underage cost (Cu = price - cost). Optimal Q* satisfies: P(D ≤ Q*) = Cu / (Cu + Co). Known as the critical ratio solution.

When to Use

Trigger conditions:

• One-time or seasonal purchasing decisions (fashion, holiday goods, event tickets)
• Perishable products with no restocking opportunity
• Setting initial stocking levels before demand is observed

When NOT to use:

• For continuous replenishment with stable demand (use EOQ)
• When backorders are acceptable and demand carries over (multi-period models)

Algorithm

IRON LAW: The Critical Ratio Determines Optimal Service Level
Q* = F⁻¹(Cu / (Cu + Co)) where F⁻¹ is the inverse demand CDF.
If margin is high relative to cost (Cu >> Co), order MORE (high service level).
If margin is low relative to excess cost (Co >> Cu), order LESS (low service level).
The optimal solution almost NEVER equals expected demand.

Phase 1: Input Validation

Define: unit cost (c), selling price (p), salvage value (v), demand distribution (mean μ, std σ). Compute: Cu = p - c, Co = c - v. Gate: p > c > v (profitable with positive overage cost), demand distribution estimated.

Phase 2: Core Algorithm

1. Critical ratio: CR = Cu / (Cu + Co) = (p - c) / (p - v)
2. If demand ~ Normal(μ, σ): Q* = μ + z(CR) × σ where z(CR) = inverse normal CDF at CR
3. Expected profit = Cu × E[min(Q,D)] - Co × E[max(Q-D, 0)]
4. Expected units sold = μ - σ × L(z) where L(z) is the standard loss function

Phase 3: Verification

Check: Q* > 0, CR between 0 and 1, Q* is above or below μ depending on whether CR > or < 0.5. Gate: Q* directionally correct relative to mean demand.

Phase 4: Output

Return optimal order quantity with profit analysis.

Output Format

{
  "optimal_quantity": 130,
  "critical_ratio": 0.71,
  "expected_profit": 2800,
  "expected_leftover": 15,
  "expected_stockout_probability": 0.29,
  "metadata": {"price": 50, "cost": 20, "salvage": 5, "demand_mean": 100, "demand_std": 30}
}