This is a simplified dataset aimed to predict inventory demand based on historical sales data. The objective is to forecast the demand of a product for a given week, at a particular store. The dataset consists of 7 weeks of sales transactions in Mexico.
Every week, there are delivery trucks that deliver products to the vendors. Each transaction consists of sales and returns. Returns are the products that are unsold and expired. The demand for a product in a certain week is defined as the sales this week subtracted by the return next week.
Things to note:
The adjusted demand (Demanda_uni_equil) is always >= 0 since demand should be either 0 or a positive value. The reason that Venta_uni_hoy - Dev_uni_proxima sometimes has negative values is that the returns records sometimes carry over a few weeks. Data fields:
Semana — Week number (From Thursday to Wednesday) Agencia_ID — Sales Depot ID Town — Town of the Agencia_ID State — State of the Agencia_ID CanalID — Sales Channel ID RutaSAK — Route ID (Several routes = Sales Depot) ClienteID — Client ID NombreCliente — Client name ProductoID — Product ID NombreProducto — Product Name Ventaunihoy — Sales unit this week (integer) Ventahoy — Sales this week (unit: pesos) Devuniproxima — Returns unit next week (integer) Devproxima — Returns next week (unit: pesos) Demanda___uni__equil — Adjusted Demand (integer) (This is the target you will predict)
