algo-sc-bullwhiplisted

# Bullwhip Effect Analysis ## Overview The bullwhip effect describes how small fluctuations in consumer demand amplify progressively at each upstream stage of the supply chain. A 5% retail demand increase can become a 40% order spike at the manufacturer. Caused by demand signal processing, order batching, price fluctuations, and rationing/shortage gaming. ## When to Use **Trigger conditions:** - Diagnosing why supplier orders are far more volatile than end-consumer demand - Quantifying demand amplification across supply chain tiers - Designing strategies to reduce order variability **When NOT to use:** - When demand is genuinely volatile (not amplified) — the issue is demand forecasting - For single-echelon inventory optimization (use EOQ or safety stock) ## Algorithm ``` IRON LAW: Demand Variability Amplifies at EACH Upstream Stage Bullwhip ratio = Var(orders) / Var(demand). A ratio > 1 at any stage confirms the bullwhip effect. The four root causes (Lee et al., 1997): 1. Demand signal processing (forecasting with moving averages) 2. Order batching (periodic review, MOQs) 3. Price fluctuations (forward buying during promotions) 4. Rationing and shortage gaming (inflating orders during scarcity) ``` ### Phase 1: Input Validation Collect: end-consumer demand time series AND order time series at each supply chain stage (retailer → distributor → manufacturer → supplier). **Gate:** At least 2 tiers of order data, minimum 26 periods. ### Phase 2: Core Algorithm 1. Compute