Tez Data was asked to help large e-grocery company to optimize its operational costs. We collected the data on client's competitors and defined pickers management as the main source of improvement. Our solutions were targeted at 2 goals.
First, we wanted to better predict the amount of work (basically, the amount of orders) that was required at a given hour.
Second, we tried to make the work more efficient.
First target was measured in standard bias and variance terms. Specifically, we picked MAE and WAPE metrics for that. Second target was measured as an average time to collect the order. To better predict the amount of orders we changed the existing approach in spreadsheets to more advanced Machine Learning based model. This allowed us to account for special occasions like holidays and to faster react to changed conditions. After many iterations our client were able to predict the amount of orders 20% better than it was done before.
