Current lectures at WU

Business Intelligence in Supply Chains 1 - CO2 Accounting with SAP Sustainability Footprint Manager
 

An introductory Webinar on applications and limitations of AI in economy and society.

Participants are (from left to right):

Prof. Robert Müller-Török, University of Public Administration and Finance, Ludwigsburg
Prof. Hiroko Kudo, Chuo University, Tokyo
Prof. Andras Nemeslaki, University of Technology and Economics, Budapest

Moderation:

Prof. Alexander Prosser, University of Economics and Business, Vienna

The webinar was recorded on Sept. 16, 2021 at the University of Public Service, Budapest within CEEeGov 2021.

This project was supported by Baden-Württemberg Stiftung, here a list of the project partners.

Project coordination, Europazentrum Baden-Württemberg

An introductory webinar with Prof. Robert Müller-Török, HVF Ludwigsburg.

Moderator: Irina Cojocaru, Information Society Development Institute Moldova

This project was supported by Baden-Württemberg Stiftung, here a list of the project partners.

Project coordination, Europazentrum Baden-Württemberg

Content

The entire course will emulate a real-world warehouse implementation project from its early planning stages to final use. The system used will be SAP HANA as well as tools to analyse analogous data.

Learning Outcomes

Students will understand the concept, tools and limitations of in-memory-based business intelligence, which enables analytics far beyond traditional data warehousing. They will also understand how methods of artificial intelligence interact with analytics. Two case studies will be processed:

• Process mining of a process in sales: Students build a system to check a defined process against “real” transaction data, whether business rules are followed or if there are deviations; and if so, whether there are patterns in these deviations.

• Image classification: Tires are to be automatically classified, whether they are suitable for refurbishment or not. Students build a system that learns from pre-classified data what suitable and unsuitable tires look like. A set of unclassified tires is then to be analysed. For each tire a digital passport is available. The result data of the classification (suitable/not suitable) and the passport data is used to build a data warehouse to find patterns indicating (non-)suitability.

Both applications, process mining and image classification – have become standard applications in a manufacturing environment.

Students will also learn how to conceptually plan such a data warehouse with particular reference to unformatted and analogue data sources and their analysis.

For DFM modelling cf. Golfarelli, M., Rizzi, S., Maio, D., The Dimensional Fact Model: A Conceptual Model for Data Warehouses