This course provides an advanced introduction to state of the art network-based transportation and supply chain modeling with a specific focus on recent developments in

• exploratory geospatial analysis
• location/allocation analysis
• cloud-based services and open-source data infrastructures & tools

We will use real-world data and case studies in various industries for

• Locating facilities, e.g., a new warehouse of a major retail chain, a new hub/spoke in a distribution network

• Allocating, e.g., customers to retail/service outlets, regional warehouses to a central warehouse, resources to production sites

• Evaluating , e.g., service/infrastructure networks and customer potentials, trade area and distribution/production network (re-)design, geospatial risks and sustainability effects

The topics are addressed from a methodological-theoretical as well as an empirical perspective, both with a particular emphasis on spatial aspects. Considerable attention will be paid to gaining hands-on experience in the application of spatial analysis techniques of events that occur on and alongside networks in empirical practice, using spatial analytics methods and tools like ArcGIS Desktop and ArcGIS Online as well as Open-Source Software like GeoDa, CrimeStat, GWR or SANET.

Students learn selected theoretical and empirical methods and get a good understanding of the fundamental questions that are addressed in the context of SCM, the methods with which these are addressed, and the current state of affairs in the literature.

By the end of this course students 

• possess a relevant background and a good mastery of models, methods and techniques used in the domain
• have the ability to select and apply appropriate modeling tools in specific decision making contexts

According to the examination regulation full attendance is intended for a PI. Absence in one unit is tolerated if a proper reason is given.

• Lecture and discussion
• papers to read
• lab course tutorials
• assignments

Tasks (max. achievable points = 100)

• readings and assignments (5+10+15+15+10=55)
• active participation in class discussion (5)
• final exam (40)

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%

Prerequisite for passing the course: minimum performance of 40% in the final examination.

Participation is restricted to MSc SCM students.

Office hours: please, send email to petra.staufer-steinnocher@wu.ac.at and/or vera.hemmelmayr@wu.ac.at to make an appointment

Course material: There is no traditional course text. But the lecture slides and a limited number of readings are provided on the learn@wu platform.