Large enterprises and their information systems produce and collect large amounts of data related to different areas, e.g., manufacturing, finance or human resources. This data can be used to complete tasks more efficiently, automate tasks that are currently executed manually, and also generate insights in order to solve certain challenges.

Nowadays, machine learning techniques are utilized in many fields and use cases. In cooperation with the SAP Innovation Center Network, this bachelor project investigated opportunities to apply machine learning techniques to the problem of order delay prediction. The recent development and in-practice application of in-memory database technology (e.g. SAP HANA) enabled the efficient execution of these techniques on large enterprise datasets.

Enterprise systems contain often data of millions of historic 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, we apply decision trees that estimate which issues may interrupt the process for a currently open order. This flexible machine learning technique enables to display details of the most pressing sales orders on the one hand, and furthermore to review the reasons why a particular sales order is considered to become delayed on the other hand. Therefore, the user receives the relevant information about critical indicators of an open sales order right at the beginning of the fulfillment process.