Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories

The emerging trend of Prescriptive Analytics tries to prescribe actionable decisions. The goal is to help production experts in automating their decisions in the factory. Decision making processes in factories mostly rely on experts inside the company. Current trends such as demographic shifts and shortages of skilled personnel are challenging organizations to find ways to become less dependent on the ad hoc availability of expert knowledge through digitalization and data analytics. Existing data analytics approaches for smart factories usually focus on description, diagnosis, and prediction. An approach to differentiate between different application areas of Prescriptive Analytics in a smart factory is presented. Focus areas for a useful application and integration of Prescriptive Analytics are derived. Based on these applications, design principles are constructed. They help decision makers to select future lighthouse use cases for development and help smart factory advocates to strategically decide which prescriptive analytics approaches to highlight. The resulting design principles are based on a literature review and validated in a workshop with experts from research. Approaches are differentiated and key characteristics for future Use Case design decisions are derived.