Challenge
Unexpected customer demands lead to supply shortages, as the existing stocks have not been optimally distributed among the various warehouses. This usually happens because previous manually performed analyses on a calculated range of individual materials have reached their quantitative limit so that unexpected fluctuations cannot be taken into account.
The approach
With the help of machine learning, a model of customer requirements is created based on historical data. In this way, simulations that optimize the entire inventory system by redistributing the existing stocks in such a way that both OOS situations and overstocks can be avoided. By using exception reporting, situations can be automatically identified that require manual intervention. In addition, all required information and possible solution options can be provided by these reports.
By using the biX AI Tools, the solution approach can be fully integrated into your SAP system.
Your benefit
Due to the optimal distribution of the existing stocks among the various warehouses, additional sales can be realized. At the same time unnecessary costs can be prevented by high stocks of individual materials.
