Service Analytics Research Group

Service Operations Management

Lecture and exercise course for master students

Prof. Dr. Catherine Cleophas (lecture)
Lena Hörsting (exercise course)



The service sector plays a significant role in the economy and comes with its own operational challenges. In this course, participants consider the application of methods of predictive analytics and optimisation to this domain. They get to know the challenges of intangible products and customer-centric service design and find out about the variety of industries that rely on services, ranging from repair shops to the health care sector.


The course will consider relevant topics of service operations management, such as
  • New service development
  • Managing service experiences: Analyzing processes
  • Service quality management and sentiment analysis
  • Predictive service analytics
  • Location and district problems
  • Resource allocation and workforce scheduling for services
  • Workforce planning
  • Waiting time management and customer scheduling
  • Yield and inventory management
  • Service Operations Management Case Studies


Participants gain theoretical background knowledge in the following areas:
  • Predicting the demand for services
  • Analysing service processes
  • Optimising the allocation of resources and workforce for services
  • Analysing waiting times and customer schedules
  • Planning the service market interface through yield management
They also gain hands-on experience in applying these concepts to a case study and presenting the results.
  • None



  • Haksever, C., & Render, B. (2017). Service and Operations Management. World Scientific Publishing Company.
  • Fitzsimmons, J. A., Fitzsimmons, M. J., & Bordoloi, S. (2008). Service management: Operations, strategy, information technology (p. 4). New York, NY: McGraw-Hill.
  • Further literature is announced during the module


Further information:


Detailed information, room numbers, times and dates of lectures, tutorials and exams will be published on UnivIS and OpenOLAT.