Master in Management

In this course, we will cover how firms can use performance measurement tools and concepts to manage the implementation of strategies. We cover concepts such as the balanced scorecard, strategy maps, action controls, personnel controls, strategic profitability analysis, etc.  The course will emphasize quantitative approaches (i.e.: use calculations to improve decision-making and strategy execution). 

Lecturer

Prof. Dr. Matthias Mahlendorf

This course focuses on the process of decision making. In contrast to the economic model that assumes that human nature is determined by perfect rationality, this course highlights the irrationality of human decision making. We show that irrational decision making is not random or senseless, but systematic and predictable. The goal of the course is to explain how information processing, expectations, emotions, social norms, and other invisible, seemingly illogical forces skew our reasoning abilities. Understanding irrationality is important to better understand the actions and decisions of consumers, managers, and policy makers. 

This module aims to establish the foundations required to manage information systems in a business environment. Based on knowledge about different types of information technology and information systems, future managers will be able to evaluate potentials of IT from a strategic perspective. Further, they will be able to reflect on and contribute to designing a firm’s IT strategy, IT governance, and IT architecture.

External Lecturer

Prof. Dr. Daniel Beimborn

The module provides students with a sound foundation in the application of the many tools and techniques of management science. Students are expected to learn the tools and the applications of modelling, optimisation, computing and programming in solving practical problems drawn from different functional areas (operations, finance, marketing, and human resources, etc.) in different organisations. 

This course focuses on how firms can maximize value by building and evaluating their operating system. More specifically, the course provides rigorous answers to the question of how firms should tailor their operational competencies, assets, and processes to a specific business strategy? The topics include the historical sources of operations strategy, its link to other strategic decisions, process designs, global networks, strategic procurement, the integration of new technologies, environmental considerations, demand management, and the question of how to manage the implementation of a new strategic initiative. 

Lecturer

Prof. Dr. Jochen Schlapp

The world runs on social relationships and social networks. Relationships are essential to getting work done, and to the way leaders engage in leading and leadership. Effective leaders and followers in any position use relationships to manage up, down and sideways. Work is accomplished through formal and informal relationships – social networks – and participants can be more effective if they understand the strength and weakness of their social relations. As well, in any job it is important to build power and manage relationships with peers and superiors. But how do you do it? In this course, we provide answers to this question by using a social network perspective.  

This module focuses on the design of resilient and sustainable supply chains and emphasizes the strategic role of information and data analytics. Students will learn about important design choices and digital core capabilities (such as traceability, social media monitoring, and artificial intelligence) that firms need to build to increase their resilience towards regular demand and supply uncertainty, and towards major unpredictable supply disruptions caused e.g., by natural disasters, pandemics, supplier bankruptcy, or port strikes. 

Lecturer

Prof. Dr. Mirko Kremer

This module introduces computer simulation as a powerful (yet intuitive) method for modelling complex business environments, analysing their behaviour, and predicting the effects of managerial strategies. Students will learn how to develop representative models, analyse and provide data to adequately parameterise and validate their models, conduct sensitivity analyses, and interpret and communicate results, acquiring the knowledge and tools to conduct simulation-based projects in managerial practice. 

This course covers aspects of firm growth and how business models can be scaled. It focuses specifically on the differences between traditional growth models and growths achievable through digital business models, platform and so called zero marginal costs business models. It provides a unique blend of economic theory and practice, applying elements of recent theoretical advances to study the impact of novel digital technologies and new business model designs on strategic analysis. 

The purpose of this course is to introduce you to the field of persuasion and give you hands-on experience to understand how persuasion “works”. We will discuss theories related to attitudes, persuasion and social influence to illuminate the underlying behavioural and psychological constructs. You will learn the key behavioural and psychological concepts and develop the intellectual ability to apply them in situations where you need to persuade both businesses and individuals. As a related outcome of this course, you will also develop negotiation skills. 

Based on setting a common understanding about the concept of digital innovation and about selected digital technologies, this module is centered around the specifics of digital innovation discovery, implementation, and exploitation. The “academic” content of the course will be complemented by various workshops from corporate partners, a digital innovation challenge, and a hands-on IoT group project. Rounding it off, discussions about ethical aspects will sensitize students for difficulties and dilemmas related with digital innovation.

External Lecturer

Prof. Dr. Daniel Beimborn

This module is a hands-on introduction to state-of-the-art data analytics and machine learning methods. The course covers both supervised learning algorithms (used to make accurate predictions about the future from current data) and unsupervised learning (used to discover unknown structures in given data). Students will understand how to use those algorithms in different business contexts, and how to obtain managerially relevant insights from their analyses. 

The course covers probabilistic and behavioural aspects that underpin resource-allocation strategies. It focuses on how firms can apply strategic foresight to anticipate competitive market dynamics and how such analyses should affect resource allocation strategies. The course also discusses managerial heuristics than can be applied in highly uncertain environments in which traditional forecast approaches are limited. 

Lecturer 

Prof. Dr. Ronald Klingebiel

New for 2021, more information to follow.

Online marketing is quickly gaining in importance and has recently surpassed radio, print and magazine advertising in terms of marketing spending. Moreover, the internet and online marketing are changing business models across many industries; traditional (offline) media is quickly “going digital” through technological advancements such as video-on-demand. The goal of this module is to introduce students to the technical and business aspects of the major online marketing instruments as well as new trends. In particular, we will discuss practical examples of; display and search engine advertising, affiliate marketing, social media and influencer marketing, viral marketing, mobile marketing and the latest trends and opportunities. 

Lecturer

Prof. Dr. Christian Schulze

This module bridges data science techniques with managerial decision-making, by leveraging the powerful combination of data, visuals, and narratives. The module covers basic data visualisation techniques for data of different volume (small and big data), variety (structured and unstructured), and velocity. The module also covers the psychological foundations and principles of data visualisation – how does the human brain process information, and how can we manage the cognitive load and attention of the audience? Students will learn to build compelling stories around their data analysis, in order to effectively communicate their results to decision-makers in important business contexts. 

New for 2021, more information to follow.

Change is a constant, and organisational change is a regular theme for any leader. Leading change is difficult because you need to convince people that they should give up habits, routines, and scripts that perhaps work well and make their lives easier. Leaders that are good at influencing social change processes understand what motivates people and how to get people to commit to new lines of thinking and action through negotiations. Leading change can be top-down, bottom-up and middle-out. It can also be re-active when a crisis or shift takes place in the market or proactive when leaders take steps to change before an interruption calls for change. In this course, we explore some of the challenges and traps that can derail leaders' attempts to change people and organisations.  

Industry 4.0 is predicated on the idea that the real-time connection of physical and digital systems will affect every corner of modern organizations’ value creation process, through the emergence and convergence of new technologies such as additive manufacturing (AM), the internet of things (IoT), blockchain, artificial intelligence (AI), augmented reality (AR), and others. This module includes hands-on workshops to provide a common technology foundation, as well as case studies and business simulations to understand how Industry 4.0 changes the way work is done in various manufacturing and service settings, and how it will fundamentally alter traditional trade-offs among the competitive priorities of cost, flexibility, speed, and quality. 

This hands-on module highlights the crucial role that well-designed business experiments can play in informing important managerial decisions in diverse areas such as pricing, new product development, operations management, and organisational incentive setting. A growing number of organisations recognize the power of experiments, but often fail in the execution phase; for instance, because they are using unsystematic trial-and-error approaches that are not geared towards solid causal inference and learning from robust empirical evidence. The module covers the scientific foundations of experimental design & analysis, including advanced topics such as quasi-experimental designs, natural experiments, and regression discontinuity designs. The module further teaches students to understand and solve practical implementation challenges of business experiments. 

New for 2021, more info to follow.

This course offers a hands-on approach to the study of people, management and organisations. We provide students an opportunity to acquire knowledge and skills that are critical to tackling today's managerial problems. The course emphasises experiential learning coupled with lectures, readings, class discussion/debate, guest speakers, personal and group assignments. The content will reflect the most relevant business topics at the given time such as crisis management, disruptive innovations, etc. 

This module focuses on the challenges and approaches involved with an organization’s digital transformation. Participants will discuss the potentials and limitations of different agile management approaches (Scrum, ScaledAgile, DevOps, Bimodal IT etc.). They will acquire competencies in designing and shaping the enterprise architecture, organizational structure, and culture of a firm which is prepared for the digital future.  The “academic” content of the course will be complemented by workshops from corporate partners and a hands-on agile methodology workshop. Debating on the economic and societal impacts of digitalization will complete the course. 

External Lecturer

Prof. Dr. Daniel Beimborn

The Applied Analytics Challenge presents students with a challenging real-world (big data) business analytics problem. The students will work on solving the problem by using the data analysis techniques that they have learned in previous courses. The course exposes students to the nature of big data projects and will teach them how to address challenging issues in management practice. It also introduces students to the issue of how to manage such projects.