For large convenience store chains, management of perishable goods can prove to be challenging. Many encounter problems with the availability of goods, freshness, etc.
Considering various important features like the location of the store, availability of the goods, sales of the goods, temporal features etc.., we have built a hybrid forecasting model. This model uses machine learning techniques to predict the demand for goods.
Incorporating this model into daily use by convenience store chains has ensured a more optimal use of supplies. We have seen reduced goods damage and wastage as well as provided a smooth flow of goods across DFW metroplex pilot stores.