Delays and problems during sales order fulfillment processes are caused by a multitude of factors. Oftentimes issues causing the delay only become obvious when the process is already interrupted. To improve the handling of these issues, one needs to analyze and extract the factors commonly responsible for delays.
Enterprise systems contain often data of millions of past sales orders. These can be utilized to draw conclusions for current and upcoming orders. Based on real world data of historic sales order fulfillment processes, the software applies machine learning techniques that allows for the prediction of issues that may interrupt the process for a currently open order. This enables the user to display details of the most pressing sales orders and review the reasons why a particular sales order is considered to become delayed. To avoid such delays even before occurring, the software uses a mathematical model to derive, and propose the best action to mitigate the risk for delayed sales orders.