Ford Motor Company sought to predict daily sales accurately to optimize operations and plan for the future. Intricate models built with algorithms and machine learning forecasted sales and surfaced potential challenges, enabling more reliable production and inventory decisions.
The decision behind the question
Ford Motor Company needed accurate daily sales forecasts to plan production and inventory, since monthly aggregates arrived too late to change build schedules.
Data and methods used
Daily sales history was modelled with gradient-boosted forecasting, with calendar effects, promotional periods, and regional variation carried as explicit inputs and a prediction range reported alongside each expected value.
What the analysis found
- •Daily demand was predictable enough to plan against once calendar and promotional effects were separated from underlying trend.
- •Forecast error concentrated around promotional transitions rather than being spread evenly through the year.
- •Regional patterns diverged sufficiently that a single national forecast understated risk in specific markets.
What changed as a result
Production and inventory planning moved onto the daily forecast with its stated range, so buffers were sized to actual uncertainty instead of a flat rule.
Related work is grouped under Market Research, and the methods used here are described in more detail under analytical capabilities.
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