Supply Chain Analytics
Winter term 21/22: The
number of participants is limited to 16. The registration process has
already been completed for this semester.
Business Analytics is divided into three areas: (1) Descriptive (2) Predictive and (3) Prescriptive Analytics. Descriptive analytics goes beyond the aggregation and aggregation of data, which is often mistakenly understood as such. The aim of Descriptive Analytics is to gain an understanding of fundamental phenomena, processes, interrelationships or causal causes that have led to past corporate events and results from data. Predictive analytics aims to predict an as yet unknown variables. These may be unrealized future variables or variables that are not available to a company. A distinction can also be made in the prediction of continuous numerical values or categories. Finally, Prescriptive Analytics aims to provide recommendations for action to achieve a desired goal. All three areas should support decisions through their results.
Content of the course
The aim of the course
is, on the one hand, the application-oriented teaching of
methodological competence with regard to quantitative analyses using
the example of problems in logistics. In the course, methods are
taught on the basis of problems with required decision support, the
critical handling of the results and their uncertainty are focused.
The basics of the quantitative methods enable the students to acquire
further knowledge themselves and to critically evaluate the use of the
methods in everyday professional life. The course is an
application-oriented course designed to enable students to apply the
methods to problems. In-depth theoretical basics of statistics,
optimization or machine learning are not covered. On the other hand,
the course is intended to impart management competence with regard to
the use of quantitative methods. Students should understand when the
use of which method is meaningful and expedient and recognize the
limits of the methods. Within projects with the goal of decision
support, the students understand the prerequisites for analytics, the
necessary linking of methods to solve problems and tasks and the
subsequent steps to bring about the desired decision support.
The course covers five topics:
Introduction to Business Analytics: deals with the definition, goals, methods, capability areas and barriers of Business Analytics and introduces Supply Chain Analytics.
Key figures and metrics: Deals with the topics of the goals of key figures as well as metrics, key figures and key figure systems in logistics. The topic area introduces key figures as indicators of problem areas that can be investigated and, if necessary, corrected with Supply Chain Analytics.
Predictive Analytics: Treats the methods of time series analysis and regression to predict values, such as demand or prices.
Prescriptive Analytics: deals with the method of optimization using the examples of Network Design, Sales & Operations Planning (S&OP) and Revenue Management.
Descriptive Analytics: Treats segmentation (e.g. of customers, products or suppliers) using the clustering method.
The analysis methods are introduced and taught using Excel and then deepened using R-programming to analyze large amounts of data. An introduction to R-Programming is part of the course. The methods are prepared using a gamification concept.
The course requires a computer to complete the tasks with an installed version of MS Excel (free for TUB students) or LibreOffice (free) and R (the additional installation of R-Studio is strongly recommended) (free). R is used with R notebooks (equivalent to Jupyter notebooks in Python) to improve the learning experience. As far as possible, students are encouraged to bring their own computer. The event room (H 9107) is limited to 12 computer workstations.
No explicit modules are required. However, prior enrollment in Statistics I, Operations Research I, Introduction to Computer Science I and Fundamentals of Logistics or equivalent courses is recommended.
6 ECTS points will be awarded for the successful completion of the event.
|Course number||73 110 L
|Time||1. Event 27.10.2021,
Gerlach, M. Sc.
Workload in hours
postprocessing||Preparation of case
homework deals with one methodical problem to be solved in R and 2-3
text problems. These will probably be solved in groups of 2. The tasks
will be submitted in writing.
The case study comprises a comprehensive project to be solved in groups of 4 and then presented.
- Master Wi-Ing: Optional module in the integration area
- Diploma students and students of other courses of study: free choice
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AnsprechpartnerBenno Gerlach, M.Sc.
Sprechstunde: Nach Vereinbarung
Room H 9104
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