Retail Demand Forecasting
A demand forecasting model for retail inventory planning, built on historical sales data.
Problem
Overstocking and stockouts both cost retailers money, and both stem from inaccurate demand forecasts.
Solution
A forecasting model over historical sales data that projects demand per SKU, giving planners a data-driven baseline for inventory decisions.
Overview
Retail inventory planning sits between two expensive mistakes: stock out and lose the sale, or overstock and tie up cash in unsold inventory. Both trace back to the same root cause — a bad demand forecast.
How it works
Historical sales data is used to train a forecasting model that projects demand per SKU, accounting for trend and seasonality. The output is designed as a planning input — a data-driven baseline planners can adjust with business context, not a black-box final answer.
Result
A concrete, per-SKU forecast that replaces gut-feel reordering with a number planners can defend and refine.
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