TU Berlin

Bereich LogistikSupply Chain Analytics

Logoschriftzug des Bereichs Logistik

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Supply Chain Analytics

Winter term 22/23: The number of participants is limited to 24. The registration process takes place via the ISIS course.

The current increasing complexity of global value creation networks and logistics activities as well as the new associated, intelligent and automated technologies increase the availability and use of data and the resulting strengthening of data-driven or evidence-driven management. This development is accompanied by increasingly powerful quantitative and data-based methods, which are primarily derived from statistics and machine learning. Moreover, the use of these methods is always increased in terms of user-friendliness, which is due to the growth in data processing tools and their continuous improvement. In this way, the application of statistics and machine learning in business management is always promoted. The use of quantitative and data-based methods to find and solve problems in an economic context and for corporate tasks is called business analytics. Applied to logistics, this is referred to as supply chain analytics. The introduction of Supply Chain Analytics has already made a significant contribution in some companies to increasing the performance of companies and further high expectations are placed in it.

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 details

Course details
Course number
73 110 L 9546
Wednesdays: 12-14
1st event 26.10.2022
H 9107
Benno Gerlach, M. Sc.

Lecturer of the event


The event will be held by Benno Gerlach, M.Sc.

occupancy opportunities

  • Master Wi-Ing: Optional module in the integration area
  • Diploma students and students of other courses of study: free choice


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