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A logistics operator cut forecast error by 22% and reduced carrying costs with probabilistic demand models.
Client background
Harborline LogisticsA logistics operator cut forecast error by 22% and reduced carrying costs with probabilistic demand models.
Planning ran on spreadsheets that went stale quickly and broke during promotions and seasonal peaks.
We built Demand Forecasting models on shipment history plus external signals, writing recommended plans back into the planning system.
Probabilistic forecasts at the lane level replaced manual guesses, with scenario planning for peak events. Accuracy improvements built planner trust within a quarter.
Measured outcomes from the deployment. Figures are illustrative mock data.
“Forecasting accuracy jumped in the first quarter. The team trusts the numbers now.”
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