Modulbeschreibungen

With the increasing complexity of supply chains and the massive growth of data, analyzing these internal and external (big) data is becoming more important for successfully managing supply chains. The course prepares students to use business intelligence and analytics for understanding and improving supply chains.

2h Seminar, 2h Hands-On Exercises in PC Lab (twice with split groups)

• Introduction to general analytics methods


• Operational (ERP) vs. Analytical (BI&A) Systems


• Data querying


• Data modeling


• Data visualization


• Business intelligence for supply chains and operations management


• Multidimensional analyzes, OLAP, and data warehousing


• Descriptive, predictive and prescriptive analytics


• Predictive analytics for demand forecasting, churn prediction, campaign planning, etc.


• Predictive maintenance


• Industry 4.0 and internet of things


• Sensor-based data (e.g. RFID)


• Text and Social Media Mining


• Real-time analytics and process monitoring


• Process mining of supply chain processes


• Big Data analysis


• Development of use cases and business models for big data

Basic literature

Turban, E., Sharda, R., & Delen, D. (2015). Business Intelligence and Analytics: Systems for Decision Support (10th ed.). Upper Saddle River, N.J.: Pearson Education Limited.

Liebowitz, J. (2013). Big Data and Business Analytics. CRC Press, London.

Wolfram, S. (2017). An Elementary Introduction to the Wolfram Language. Wolfram Media, Champaign.

Additional literature

Foreman, J. W. (2014). Data smart using data science to transform information into insight. Indianapolis, IN : John Wiley & Sons.

Drake, M. J. (2013). The Applied Business Analytics Casebook: Applications in Supply Chain Management, Operations Management, and Operations Research. New Jersey: Financial Times Prentice Hall.

Nagurney, A., Yu, M., Masoumi, A. H., & Nagurney, L. S. (2013). Networks Against Time: Supply Chain Analytics for Perishable Products. Dordrecht: Springer.

Sanders, N. R. (2014). Big data driven supply chain management a framework for implementing analytics and turning information into intelligence. Upper Saddle River, NJ: Pearson Education.

Somani, A.K.; Deka, G.C. (2017). Big Data Analytics: Tools and Technology for Effective Planning. CRC Press, London.

Watson, M., Lewis, S., Cacioppi, P., & Jayaraman, J. (2012). Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain (1st ed.). Upper Saddle River, N.J: Financial Times Prent.

Zielesny, A. (2016). From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence, 2^nd ed. Springer

Basic knowledge in Global Supply Chain Projects (at least 10 ECTS in bachelor or master courses about logistics, production, operations management, supply chain management)

Seminar/Exercises (Contact time 64 hrs), face-to-face (day and evening courses)

After successful completion of the module, students will have acquired the following competencies:

Content oriented learning outcomes:

ILO 1: The students will have an overview of the state-of-the-art of data analytics.

ILO 2: They have gained an in-depth understanding of the conceptual foundations of business intelligence and analytics for managing supply chains.

ILO 3: They can critically analyze methods and concepts with respect to their applicability in practice and transfer the solving methods to new problems.

Skill oriented learning outcomes:

ILO 4: be able to choose appropriate business intelligence and analytics systems for different problems

ILO 5: have gained hands-on experience with different business intelligence and analytics techniques and applications by solving practical cases in teams

ILO 6: present their finding orally in front of a large group (presentation)

Preparation for the Master Thesis

Programs: Global Supply Chain and Operations Management Master (GSCOM), DFS

Type of course unit: compulsory.

Verification of competence acquisition: combined examination (respect and attend to the diversity of stu­dents and their needs, enabling flexible lear­ning paths whilst evaluating both individual as well as standardised learning outcomes).

The exam consists of two parts. The assessment is based on a project report (2.000 words per student; counts 50%) plus a presentation (counts 50%) on a real life problem, thus it is a combined examination.

Both parts test several areas of knowledge and areas of competence.

In the presentation, the following is in particular tested

• Ability to work independently (ILO 4, ILO 5)


• Ability to prioritize information (ILO 4, ILO 5)


• Ability to apply theoretical concepts (knowledge acquired in class) to ”real” problems (ILO 4, ILO 5)


• Ability to formulate hypotheses and conclude on these (ILO 6, ILO 7)


• Ability to present own results (ILO 6)

The project report tests different areas of knowledge and competencies.

• Knowledge questions require students to describe definitions, concepts or facts. Thus, knowledge about and correct understanding of these is examined. (ILO 1)


• Problems containing calculations test the understanding of the given problem statement and the ability to solve it analytically with an adequate method. (ILO 1, ILO 2)


• Other problems require students to interpret, summarize or compare facts, statements or previously found solutions. Such problems test the ability to critically analyze methods, concepts and solutions regarding their applicability in real life. (ILO 3)

Retake examinations will be held as combined examinations (CE) or oral examinations (OE).