Prescriptive Analytics for Recommendation-Based Business Process Optimization

Continuously improved business processes are a central success factor for companies. Yet, existing data analytics do not fully exploit the data generated during process execution. Particularly, they miss prescriptive techniques to transform analysis results into improvement actions. In this paper, we present the data-mining-driven concept of recommendation-based business process optimization on top of a holistic process warehouse. It prescriptively generates action recommendations during process execution to avoid a predicted metric deviation. We discuss data mining techniques and data structures for real-time prediction and recommendation generation and present a proof of concept based on a prototypical implementation in manufacturing.