Supply Chain Demand Planning
This application area focuses on using advanced data-driven models to forecast demand, plan inventory, and orchestrate supply chain decisions across merchandising, assortment, allocation, and replenishment. Instead of relying on spreadsheets, simple heuristics, or generic forecasting tools, companies build planning systems that ingest rich internal and external signals—such as historical sales, seasonality, promotions, prices, and macro events—to generate more accurate forecasts and recommended inventory actions by product, channel, and location. It matters because consumer and retail businesses are highly sensitive to demand volatility and supply disruptions. Poor planning leads directly to stockouts, overstocks, markdowns, excess working capital, and firefighting costs. By continuously predicting demand, identifying risks, and recommending or automating responses, supply chain demand planning applications improve service levels, reduce inventory imbalances, and increase resilience—while still keeping human planners in control for exceptions and strategic decisions.
The Problem
“Forecast SKU-store demand and recommend inventory actions using signal-rich ML”
Organizations face these key challenges:
Stockouts during promos and peak periods despite high overall inventory
Overbuying slow movers due to spreadsheet-driven safety stock assumptions
Forecast bias from manual overrides with no measurable impact tracking