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.
72 flavors. 72 different demand patterns.
An ice cream manufacturer produces for a network of stores across multiple locations. Its lineup covers 72 individual flavors, and each one sells differently through the year.
Production planning relied on a single forecasting approach for the whole lineup. The result was overproduction of some flavors and underproduction of others, which meant waste on one side and lost revenue on the other.
One forecast could not serve every flavor.
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.
The key decision: Select the model at the flavor level.
Select a forecasting model for each flavor.
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.
Model by flavor
Built a dedicated forecasting model for each individual flavor, instead of one model for the whole lineup.
Compare five architectures
Evaluated DNN, XGBoost, LightGBM, random forest and CNN models for every flavor.
Backtest against history
Backtested every candidate against historical production and sales data.
Select and serve automatically
Automated model selection and tuning across all 72 flavors, and delivered the forecasts through a production API.
From historical data to a production forecasting API.
Historical production and sales feed five candidate model types per flavor. Backtesting picks the best performer for each flavor, and a Dockerized API serves the forecasts.
Candidates compared for every flavor
- DNN
- XGBoost
- LightGBM
- Random forest
- CNN
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.
“We’d been guessing for years. Software Sushi built models that actually understand our flavors — strawberry behaves nothing like chocolate chip. We cut waste by 27% in the first season and finally have confidence in our production runs.”
Reported outcomes from the published Software Sushi case study.
What we delivered.
- Model-comparison pipelineFive candidate model types evaluated for every flavor.
- Automated selection and tuningApplied across 72 individual flavors.
- Dockerized forecasting APIDelivered as a production-ready container.
Tools and methods.
- DNN
- XGBoost
- LightGBM
- Random forest
- CNN
- Docker
Let each product choose its model.
Demand is rarely uniform across a product range. Comparing candidates per product, against the company’s own history, beat any single model applied to everything, and automation keeps that choice maintainable at 72 flavors.
Does demand vary across your product range?
Let’s discuss your forecasting process, historical data and production-planning challenges.