A dealer of construction machinery spare parts would like to forecast the demand quantities for its products in the next few months at various locations in order to stock its warehouses according to demand.
Relevant predictive indicators were identified from internal data (e.g. historical demand quantities, product master data, master data on sales locations, ...) and external data sources (weather and economic data). With the help of a machine learning algorithm, the demand for spare parts at all locations for the next 12 months can be predicted more accurately than was previously possible.
The increased forecasting accuracy means that retailers can manage their warehouses more efficiently. The added business value is reflected in key figures such as parts availability (service level), stock turnover and reduction of lost sales by avoiding empty warehouses.
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