E-Commerce

Across Millions of SKUs, demand forecasts are generated daily across portfolios of millions of SKUs using AI/ML models that go beyond historical sales data — incorporating web analytics, click rates, conversion signals, social media spending, influencer events, competitor pricing, promotions, and special events to detect and respond to demand shifts before they hit availability. The forecasting engine runs a tournament across 20+ world-class methods to select the best fit per SKU, handling everything from erratic products to seasonal spikes and new item introductions — making it particularly well-suited for DTC and e-commerce brands where a single influencer campaign or paid social spike can reshape demand overnight.

Inventory targets are set and continuously optimized across fulfillment centers and the full supply network using MEIO — balancing service level, cash flow, and inventory cost simultaneously rather than optimizing each node in isolation. When demand spikes unexpectedly in a region, the platform triggers automated network rebalancing recommendations that redirect inventory across nodes to where demand is materializing — maximizing inventory productivity and protecting availability without waiting for the next planning cycle to catch up.

Replenishment recommendations and purchase orders are generated automatically using demand-driven AI models that factor in lead times, safety stock targets, supplier constraints, shelf-life, and operational constraints — with direct ERP integration to eliminate manual PO entry and reduce human error.

Slow-moving and excess inventory is identified automatically through diagnostic alerts that trigger price action recommendations — optimizing markdown timing and depth by factoring in remaining shelf-life, competitor pricing actions, and budget targets to recover margin and free up working capital before carrying costs or spoilage compound. Markdown recommendations are designed to minimize write-offs by identifying the optimal discount window per SKU before shelf-life thresholds are breached, while competitor pricing signals ensure recommendations are commercially calibrated rather than applied in isolation from market reality.

Promotions are planned and evaluated using predictive models that forecast lift, cannibalization, and inventory impact at the SKU level — giving e-commerce and DTC planners the visibility to size buys correctly, avoid stockouts during peak promotional windows, and measure true incrementality after the event. Predictive analytics are provided for the large volume of promotions that characterize high-velocity digital commerce environments, where manual promotion management quickly becomes unscalable.

Assortment decisions are supported by AI-driven portfolio analysis that classifies items by velocity, margin contribution, and substitutability — enabling e-commerce, DTC, and quick-commerce operators to identify which SKUs to keep, phase out, or substitute without manually reviewing thousands of items. Portfolio analysis capabilities help determine which items should be kept in stock or delisted, keeping the active assortment lean, available, and aligned to demand — a critical discipline in environments where SKU proliferation directly inflates working capital requirements.