Research
The acronym MIS has been standing for „Management Information Systems“ for decades. Research and practice have been focusing on gaining insights into large structured data, for example by online analytical processing (OLAP) with SQL.
Today, artificial intelligence (AI) opens the door to extract knowledge out of “big data” in automated and autonomous ways in order to improve business management. A large variety of methods and models (see the clouds in the figure above) allow advanced descriptive, diagnostic, predictive, and prescriptive analytics for AI-supported decision making. Managers capitalize on various services to cope with the challenges of the new era of data-driven business. That is why we coin the new meaning of MIS: “Management Intelligence Services”.
There are several perspectives of “Management Intelligence Systems”. On the one hand, MIS build on the foundation of advanced analytics, joining tasks, data, and methods. Managers have to decide on or at least comprehend the right methods and the right data to be used for given tasks. On the other hand, the results of advanced analytics provide knowledge about markets, demands, customers, competitors, technologies, and processes, which characterizes a strategic view. Based on deepened and augmented knowledge managers are able to optimize business solutions, e. g., to reduce costs, increase value or improve efficiency. To pave the way to success in application MIS have to ensure acceptance on the managers’ side. One option is to provide transparency and build trust in data, methods, and results.
Selected Research Projects
Production — E-Commerce
Knowledge-based anomaly correction in continuous manufacturing processes
Causal analysis on observational data for campaign planning in e-commerce
Trusted data logistics in supply chains by blockchain technologies
Optimizing energy efficiency, sustainability, and cost of buildings by advanced analytics
Transparency engineering for complex analytical and optimization models by explainable AI
Healthcare — Sports
AI in Healthcare – Increasing trust in data-driven applications
Optimizing hospital stays by machine learning and risk analysis
Competitive intelligence in the healthcare industry
Trusted networks for data sharing between and within healthcare stakeholders
Data governance and coordination processes in future healthcare
Intelligence-driven strategies and tactics in team sports