27% less waste with per-flavor forecasting.
An ice cream manufacturer replaced a one-size-fits-all forecasting approach with model selection for each flavor, reducing waste and saving planning time.
Less production waste. Less manual planning.
Reduction in production waste
Reduction in production waste reported after introducing per-flavor forecasting.
Manual planning time saved monthly
Manual production planning time recovered each month.
About these results
These outcomes are reported in the published Software Sushi case study. The published account does not include the baseline dataset or measurement methodology.
- Reduction in production waste: Published case study · Results
- Manual planning time saved monthly: Published case study · Results
One forecast could not serve every flavor.
- Business
- Ice cream manufacturer
- Data
- Historical production and sales
- Work delivered
- Per-flavor forecasting and backtesting
The client was overproducing and underproducing across their flavor lineup, leading to significant waste and lost revenue. A single forecasting model couldn't capture the distinct seasonality and demand curves of each flavor.
A single forecasting approach missed the distinct demand patterns of each flavor, leading to overproduction and underproduction.
Each flavor used its best-performing model, selected by backtesting against historical data.
Select a forecasting model for each flavor.
The approach
Select models at the flavor level. Historical production and sales data provided the basis for backtesting five candidate architectures per flavor, rather than applying one model to different demand patterns.
Production planning tailored to each flavor’s seasonality and demand.
Conceptual workflow · based on the published case studyModel by flavor
Built a dedicated forecasting model for each individual flavor
Compare candidates
Evaluated 5 different model architectures per flavor
Backtest
Backtested all models against historical production and sales data
Select
Selected the best-performing model per flavor — no one-size-fits-all
Does demand vary across your product range?
Let’s discuss your forecasting process, historical data and production-planning challenges.