MSE Master of Science in Engineering

The Swiss engineering master's degree

Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 
Smart services (CM_SmartSer)

 

Smart Service Design and Engineering - Value Creation:

  • Basics of Smart Service Design (Customer insight, customer journey, value proposition design, use of data insights)
  • Selected topics of Service Science and Service Dominant Logic
  • Service blueprinting as a relevant step in the service engineering process
  • Characteristics of Data Services and Data Products
  • Use of data in the smart service design process and in the services themselves - Smart Data
  • data sources
  • Iterative improvement up to product maturity
  • Discussion of applications in the industrial and the sector
  • Discussion of real-life cases

Smart Business Model Design - Value Capturing:

  • Fundamentals for Engineering Value Flows in Service Ecosystems and Service Business Models
  • From Service Blueprint to Business Model
  • Quantification of service business models
  • Basics Business Model Design and Business Model Canvas
  • Service Ecosystem Design
  • Quantification of the business model
  • Discussion of real-life cases

Data Protection, Data Security, Data Ethics:

  • Fundamentals of data protection and data security
  • Relevant aspects for Data Product Design
  • Legal aspects vs. ethics
  • Discussion of real-life cases

Prerequisites

Prior to joining the module, the students should have an understanding of business process modeling and engineering, e.g., terms like process charts, swimlanes, process models, resources, value chain etc. (see, e.g., the paper of John Krogstie: Introduction to Business Processes and Business Process Modeling, https://link.springer.com/chapter/10.1007/978-3-319-42512-2_1)

Learning Objectives

  • Understand and apply the essential principles of Smart Service Design and Engineering - i.e. the development of intelligent services on the basis of data (comprehensive methods for the development of novel data-driven services, for their operation as well as their improvement in operations).
  • Able to integrate the data specific aspects into their service design.
  • Apply the methods of data-driven service engineering in practical case studies primarily in industrial envi-ronments (B2B), but also in consumer areas (B2C)
  • Know and understand the relevant basics of Service Business Model Design including the types of industrial Service Models.
  • Evaluate these business models quantitatively. To weigh up variants and draw conclusions about the engineering process with the aim of achieving an opera-tionally and economically balanced model.
  • Understand the design of service ecosystems.
  • Able to understand the essential principles of data protection, data security, and data ethics.

Contents of Module

Smart Service Design and Engineering - Value Creation: 40%
Smart Business Model Design - Value Capturing: 40%
Data Protection, Data Security, Data Ethics: 20%

Teaching and Learning Methods

  • Lectures
  • Group work, presentation and discussion of case studies
  • Self study of papers and analysis of business case studies

Literature

  • A. Wierse, T. Riedel: Smart Data Analytics, Walter de Gruyter, 2017.
  • A. Polaine, L. Løvlie, B. Reason, Service Design: From Insight to Implementation, Rosenfeld, 2013.
  • A. Osterwalder, Y. Pigneur et al., Value Proposition Design: How to Create Products and Services Customers Want, Wiley, 2014.
  • E. Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Wiley, 2016.
  • F. Provost, T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking, O'Reilly, 2013.
  • A. Osterwalder, Y. Pigneur, Business Model Generation, Wiley, 2010.
  • C. Kowalkowski, W. Ulaga: Service strategy in action: a practical guide for growing your B2B service and solution business, Service Strategy Press, 2017.
  • O. Gassmann, K. Frankenberger, M. Csik:  Business Model Navigator: 55 Models That Will Revolutionise Your Business, Harlow Pearson, 2014.
  • D. S. Evans, R. Schmalensee, Matchmakers, Matchmakers: The New Economics of Multisided Platforms, Harvard Business Review Press, 2016.
  • W. Stallings, Cryptography and Network Security: Principles and Practice (7th Edition), Pearson, 2016.
  • N. Passadelis et al., Datenschutzrecht, Beraten in Privatwirtschaft und öffentlicher Verwaltung, Basel 2015.

  • Stickdorn, Marc, Markus Edgar Hormess, Adam Lawrence, and Jakob Schneider 2018: This Is Service Design Doing: Applying Service Design Thinking in the Real World. O’Reilly Media, Inc.

Download full module description

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