## TL;DR
AI inventory management ensures the right products are in the right place at the right time -- predicting demand at day-level, automatically reordering before stockouts, and optimizing safety stock across millions of SKUs. The result: 30% fewer out-of-stocks, 20% lower inventory costs.

## Core Explanation
Inventory AI: (1) Demand forecasting -- predict how much of each SKU will sell at each location each day. Features: historical sales, price, promotions, holidays, weather, events. Models: DeepAR (probabilistic LSTM), TFT (Temporal Fusion Transformer), N-BEATS; (2) Safety stock -- buffer inventory to handle demand variability. ML estimates demand distribution (not just point forecast) to set optimal safety stock levels given service level target (e.g., 98% in-stock); (3) Replenishment -- given forecast + current stock + lead time + order constraints (MOQ, case pack), determine optimal reorder quantity and timing; (4) Allocation -- distribute limited inventory across locations to maximize sales (each unit goes where demand probability is highest).

## Detailed Analysis
DeepAR (Amazon, 2020): autoregressive RNN predicting probability distribution parameters (negative binomial for count data, Gaussian for continuous). Enables cold-start forecasting for new products via item embeddings. Amazon SCOT: production system processing billions of time series across global fulfillment network. TFT: attention-based model with variable selection networks for multi-horizon forecasting, interpretable importance weights showing which features drive predictions. Learned Reorder Point (RL): treat inventory as MDP -- state (inventory level, on-order, demand history), action (order quantity), reward (sales - holding cost - stockout penalty). RL learns dynamic reorder points adapting to demand patterns. Omnichannel allocation: ship-from-store (fulfill online orders from nearby stores). AI balances store inventory (serving in-store customers) vs fulfillment capacity. Key challenge: the bullwhip effect -- demand variability amplifies up the supply chain. AI reduces amplification through better forecasting and information sharing.