Fresh food forecasting

Improve on-shelf availability and reduce food wastage with DSLab's fresh food forecasting

With fresh food, retailers have always faced a trade-off: order too little, and you lose sales; order too much, and you get excess stock and food wastage.

DSLab machine learning technology allows grocers to predict demand for items with a shelf life of 1-7 days more accurately and thus increase on-shelf availability and reduce write-offs. Keep your perishables fresh with DSLab's daily demand forecasting for fresh food.

Always fresh, always in stock, with minimum spoilage

Focus on fresh
Fresh food is extremely difficult to forecast and requires different forecasting approaches. Taking into account all characteristics of perishable goods, we've designed a solution that focuses exclusively on fresh food.
Machine learning
Bayesian methods of machine learning at the core of our solution deliver highly-accurate forecasts allowing you to increase on-shelf availability without keeping excess inventory.
Daily forecast
We deliver daily forecasts for each perishable item on a store level.
Business metrics
Instead of focusing on accuracy only, we optimize for business metrics such as write-offs and out-of-stocks.
Interval estimations
Instead of averages, we utilize interval estimations that reveal more information on future demand. It allows you to assess the potential influence of out-of-stocks and write-offs on your business and to make optimal allocation decisions.
Fewer amount of data
Our approach requires fewer - months rather than years - amount of data to train the predictive model. Forecast demand for new products and recently opened stores when few data is available.
Automation
Our solution allows you to eliminate highly intensive routine tasks, avoid human errors, and fully automate forecasting and replenishment processes.
Easy integration
Our solution for fresh food is easily integrated with standard data sources such as Hadoop or SQL-based data storage, as well as replenishment systems.