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

MANA – Meaningful Analytics Lab


Publication List

Friedrich-Alexander-Universität Erlangen-Nürnberg