Symbiotic Analytics: Exploring human-machine collaboration in prescriptive analytics for automated anticipative operational decision making.

• Applicant: Professorin Dr. Catherine Cleophas
• Subject Area: Operations Management and Computer Science for Business Administration ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 545267303

Much published analytics research assumes that providing better predictions and prescriptions will increase efficiency and reduce room for human error through more automation. Yet, Implementing operations research and analytics for prescriptive purposes in practice shows that the related information systems are socio-technical, involving a relevant human component. In almost all cases, solutions that automated algorithms compute can be altered or overwritten by human decision makers. The proposed project aims to examine the conditions for success of human-machine-collaboration in this area, focusing on anticipative operational decision making. In that, it considers decision problems that arise frequently as part of the business process and that must rely on predictions about an uncertain future. As representative problem areas, we will consider inventory management, revenue management, and staff scheduling. The proposed project aims to consider the interplay of algorithm, interface, and analyst through simulation-based laboratory experiments and field studies. In that, we will focus on the role of transparency and predictability as design goals that may make the contributions from automated systems more accessible to human decision makers.

• DFG Programme: Research Grants