Syllabus

Title
1693 Advanced Methods and Tools in Supply Chain Analytics (AMT)
Instructors
Assoz.Prof PD Lena Silbermayr, Ph.D., Univ.Prof.i.R. Dipl.-Ing.Dr. Werner Jammernegg, Dr. Martin Waitz
Type
PI
Weekly hours
4
Language of instruction
Englisch
Registration
09/27/24 to 09/27/24
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Wednesday 10/02/24 01:30 PM - 05:00 PM D2.0.030
Wednesday 10/09/24 01:30 PM - 05:00 PM D2.0.382
Thursday 10/17/24 09:00 AM - 12:30 PM LC.2.064 PC Raum
Wednesday 10/23/24 09:00 AM - 12:30 PM D2.0.030
Wednesday 10/30/24 09:00 AM - 12:30 PM TC.4.14
Wednesday 11/06/24 01:00 PM - 04:30 PM D2.0.030
Wednesday 11/13/24 01:00 PM - 04:30 PM D2.0.030
Wednesday 11/20/24 09:00 AM - 12:30 PM D2.0.038
Wednesday 11/27/24 09:00 AM - 12:30 PM D2.0.038
Wednesday 12/04/24 01:00 PM - 04:30 PM D2.0.038
Wednesday 12/11/24 09:00 AM - 12:30 PM D2.0.038
Wednesday 12/18/24 01:30 PM - 05:00 PM D2.0.038
Wednesday 01/08/25 01:30 PM - 05:00 PM D2.0.038
Wednesday 01/15/25 01:30 PM - 05:00 PM D2.0.038
Contents

The course provides the basics of behavioral operation with applications to different supply chain management problems. The topics to be covered include:

  • cognitive biases in decision making and incorporation of biases into supply chain models
  • descriptive vs. prescriptive decisions: newsvendor model with behavioral factors; newsvendor models with risk preferences (expected utility, Value-at-Risk  (VaR), Conditional VaR (CVaR))
  • designing, conducting and analyzing laboratory experiments
  • supplier-buyer interactions: classcial supply chain contracts and coordination (standard game theory)
  • behavioral game theory and social preferences like trust, fairness and reciprocity
  • advanced topics
Learning outcomes

Students will become familiar with cognitive biases influencing decision making and behavioral operation management models that are able to predict those biases and supply chain management problems with multiple actors (behavioral game theory).Further, students get familiar with controlled laboraory experiments for testing analytical models. 

Attendance requirements

Attendance is required in all sessions. Absence in one session is tolerated if a reasonable excuse is given before the session. 

Teaching/learning method(s)
  • Lectures with discussions
  • Literature review and case studies
  • Cognitive tests and experiments
Assessment

InClass Assignents (40 %)

InClass Presentations and Discussion (20 %)

Assignments (40%)

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%

Readings

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Last edited: 2024-05-14



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