Information on individual educational components (ECTS-Course descriptions) per semester

Participating students must have an interest in scientific processes and research in general. In addition, the students have to work independently, especially when defining research questions relevant to them, to active participation in the elaboration of the content that contributes to solving the research questions.

Prerequisites for the participation are, beforehand, an application to the coach of the research project and an accompanying discussion before the beginning of the course in order to identify possible topics and interests. For the AI topic basic programming knowledge is required.

This research project relates to the current BI research projects JIDEP, Data Sharing Framework for SME als well as Josef Ressel Centre for Robust Decision Making and also refers to the completed research projects iCargo and EURIDICE.

Background

Numerous initiatives, (research) projects and teaching programs at the international, EU and regional levels are currently dealing with the so-called Digital Transformation, i.e. the change from traditional business activities, processes and competencies to fully or partially digitalized models and forms of organization in which significant innovations can be achieved in the short term and, from a strategic perspective, also in the longer term. Digital transformation is a motor for a wide range of processes in society - including industry - and is ultimately aimed at moving away from traditional, rigid ways of thinking in order to shape the future strategically in a targeted manner according to predefined goals with the help of ICT (information and communication technologies).

Topic

The research project deals with topics from Digital Transformation, such as automated data exchange between business partners, intelligent ICT-based production methods, Internet of Things (IoT) or ICT-based optimization of workstations (VR, RFID, intelligent flow charts, etc.). The AI topic area investigates how these applications lead to new innovations through the use of neural networks. In addition, methods and current research focuses on the self-control of production and logistics processes are considered. For self-control real objects (vehicles, containers, material) need information about their states and have to derive decisions for subsequent steps in the process chain. The embedding of such self-control processes in enterprise resource planning systems (ERP), such as SAP or the open source alternative Odoo, rounds off the research project.

As a superordinate research approach, Industry 4.0 uses the digital twin of a product, often also called "product avatar", as the basis for modeling machine and product data models. For the topic AI, approaches of supervised learning or reinforcement learning are used to derive decisions from the models. The explainability of these decisions is of interest.

Cross-cutting issues

The research project deals with current cross-cutting topics from production (from unicufactured production to intelligent assembly of machines), logistics (digital logistics marketplaces, logistics services and multimodal transport chains), ICT (IoT, VR, ERP, big data, data modeling, AI, ML) , Dealing with real-time data) and business models (servitization).

The learning results of this module are partitioned.

On the one hand, students will learn to apply scientific (applied) research questions. This includes the formulation of research questions, selection of the research method - from qualitative to quantitative methods to the use of specific, relevant IT tools -, presentation of the research contents in presentations or publications and the execution of a research process (theses, falsification, experiments, etc.).

On the other hand, students will learn about the content of current research projects at the Research Centre Business Informatics (BI). Topical overlaps with other research centres are quite possible. The module also provides students with an international environment for science. On the one hand by a diverse team in the BI, on the other hand by the inclusion of a large number of cooperation partners within the consortium projects of the BI in the (EU) foreign country.

The detailed, scientific learning results are documented individually with the students before the start of the course as part of the formulation of the research questions.

Activation: The students take responsibility for the research and learning process by introducing their own perspectives in the jointly discussed research map (context) and raising questions. They should be able to incorporate their own ideas into the research question.

Action orientation: In the introduction to the topic, the students recognize the discrepancy between their current knowledge and the target state. At the same time, the focus is on the methods and skills necessary for dealing with the question, so that a targeted acquisition of the necessary procedures can be carried out by the students.

Will-based implementation: Agreement of framework conditions and practices that make the company possible and promising. These include common rules and expectations, milestones, dealing with failure and a new beginning, the agreement, for which the students assume responsibility and when they give account when.

Embedding: Identify contact persons and achievements in the research team and, in particular, mentor mentors on the part of the university. Conditions under which a sufficient contact with the students is ensured during the work process, and which enable exchanges to be maintained and to provide ongoing feedback. In particular, students who have not performed the self-study performance should have the opportunity to recognize that they are losing the connection.

The module is a research project within the framework of the module library - see also description of the context study format.

The module is characterized by the individual support of the students within the framework of a scientific project, which results in the following characteristics:

__Strong independent component

High self-organization

__Institutes of the university are learning assistants instead of lecturers
__Open (scientific) output

__Themes are suitable for continuing in a master thesis

__According to challenging research activities in (a) supported projects or (b) projects pursuing an internal competence development target in the research area

__Good pre-exercise for all those who intend to do further scientific work (master's thesis, dissertation)

The criteria for the assessment will be clarified individually with the students in a preliminary interview and defined within the scientific context.

For the evaluation, the project results and quality of the scientific approach are used. Any scientific publication or in-house presentations (overlapping with other Research Centres or within the Research Centre) are included in the assessment.

An assessment of the final presentation of the project results rounds off the assessment criteria.

This module constitutes the second cycle of the course "Research Project: Business Informatics" which has been started under the same title in the precedent summer term.

In addition, the research project also offers the possibility of a scientific publication (conference or journal) in cooperation with the research centre Business Informatics (BI).

The course is also an introduction to (applied) research and the opportunity for further scientific work (master's thesis, dissertation).

Another special feature is the close contact of the research centre BI with research and industry partners. Within the framework of the BI projects, students can make contacts with research partners and industry at events.

If a scientific conference is attended, traveling expenses and / or participation fees may apply.

* O. Ganschar, et al., „Produktionsarbeit der Zukunft-Industrie 4.0“. D. Spath (Ed.). Stuttgart: Fraunhofer Verlag (2013).

* European Communities. Europe 2020 – “A strategy for smart, sustainable and inclusive growth”, Communication from the European Commission. Brussels, Belgium. Available online at http://ec.europa.eu/europe2020

* M. Huschebeck, et al., “Intelligent Cargo Systems Study (ICSS): Impact assessment study on the introduction of intelligent cargo systems in transport logistics industry”, European Communities (2009)

* M. Dobler and J. Schumacher, “Towards a Pan-European Ecosystem for Intelligent Cargo”, Proceedings of 9th ITS European Congress 2013, Dublin, Ireland (2013)

* B. Holtkamp, S. Steinbuss, and Heiko Gsell, “Towards a Logistics Cloud”, Proceedings of the Sixth International Conference on Semantics Knowledge and Grid (SKG) 2010 (2010). 

* M. Huschebeck, et al., “Intelligent Cargo Systems Study (ICSS): Impact assessment study on the introduction of intelligent cargo systems in transport logistics industry,” European Communities (2009)

* K. Ashton, “That Internet of Things Thing: In the real world, things matter more than ideas”, RFID Journal.

* D. Evans, “The Internet of Everything: How More Relevant and Valuable Connections Will Change the World” (2012), available online at http://www.cisco.com/web/about/ac79/docs/innov/IoE.pdf

* G. Halbritter, T. Fleischer, and C. Kupsch, "Strategien für Verkehrsinnovationen. Umsetzungsbedingungen – Verkehrstelematik – internationale Erfahrungen” (2008), Edition Sigma, Berlin.

* P. Ballon, et al., “Future Internet Public Private Partnership: Towards FI-PPP Innovation and Business Ecosystems”, available at: http://www.fi-ppp.eu/white-paper-published-on-fi-ppp-innovation-and-business-ecosystems/

Introductory Literature for AI:

* S. Russel and P. Norvig, "Artificial Intelligence: A Modern Approach", Pearson, 2020, ISBN 978-9-13-461099-3

* F. Provost and T. Fawcett, "Data Science for Business", O'Reilly, 2013, ISBN 978-1-44-93-6132-7

* I. Goodfellow et al., "Deep Learning", MIT Press, 2017, ISBN 978-0-262-03561-3

* C. Monar, "Interpretable Machine Learning", Leanpub, 2018

* R. S. Sutton and A. G. Barto, "Reinforcement Learning: An Introduction", MIT Press, 2018, ISBN 978-0-262-35270-3

* G. James et al., "An Introduction to Statistical Learning, Springer 2013, ISBN 978-1-4614-7137-0

The topic is split into didactic sections.

In a first step, the lecturers will address key issues and the results of the research and present them in impulse lectures. Afterwards, the topic will be presented within a workshop and the research questions and economic intentions involved will be worked out jointly. The aim is, on the one hand, to understand the thematic motives of the research project and, on the other hand, to be able to independently interpret and evaluate independently applied applied research, especially from business and innovation-driven points of view.

In a second step, the students are encouraged to develop certain areas independently. The sub-areas are derived from the research questions elaborated in the first step. The independent elaboration is carried out in close consultation with a coach (lecturer). The development may also take place at the Research Centre BI. The aim is to provide the students with the opportunity to take part in research (action, testing, failure, etc.) while at the same time allowing them to become more involved in research.

Finally, the research topic is to be presented within the course of the course and possibly other research projects of the context study.

In addition, there is also the possibility to pursue a scientific publication (conference or journal) in collaboration with the Research Centre BI within the scope of this research project.