Ingredient-Level Demand Forecasting for a Meal-Kit Company's Weekly Menus
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Problem
Weekly menus, guessed demand
A meal-kit subscription company running 10 fulfilment hubs across the US published new weekly menus while committing ingredient orders two weeks ahead from planner spreadsheets. Recipe popularity was estimated from experience, so hubs routinely over-ordered perishables that ended up as waste while under-ordering others, forcing last-minute substitutions that drove subscriber complaints and credits.
Solution
Ingredient-level forecasting engine
We developed an ingredient-level demand forecasting platform that predicts recipe selections per hub from menu attributes, subscriber preference history, seasonality, and past swap behaviour. Forecasts roll up into automated purchase-order recommendations per supplier and hub, planners review only flagged exceptions in a dashboard, and re-forecasts run nightly as subscribers lock in their weekly choices.
Measurable Impact
What changed after launch
Perishable ingredient waste reduced by 23% across all 10 hubs within two quarters
Recipe-level forecast error at order commitment fell from 31% to 14% (weighted absolute percentage error)
Last-minute ingredient substitutions down 41% year on year, cutting subscriber credit payouts
Planner time per weekly menu cycle cut from 3 days to under 1 across the network
Tech & Tools Used
What powered the build
Ready to Build your Food & Meal-Kit E-Commerce Business with Predictive Modeling & Forecasting
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