Syllabus

Title
1397 Location Intelligence in Supply Chains 1 (LI 1)
Instructors
Assoz.Prof PD Dr. Vera Hemmelmayr, Ass.Prof. Mag.Dr. Petra Staufer-Steinnocher
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/27/24 to 09/27/24
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Tuesday 10/01/24 09:00 AM - 12:30 PM D2.0.025 Workstation-Raum
Tuesday 10/08/24 09:00 AM - 12:30 PM D2.0.025 Workstation-Raum
Tuesday 10/15/24 09:00 AM - 12:30 PM D2.0.025 Workstation-Raum
Tuesday 10/22/24 09:00 AM - 12:30 PM D2.0.025 Workstation-Raum
Tuesday 11/05/24 09:00 AM - 12:30 PM D2.0.025 Workstation-Raum
Tuesday 11/12/24 09:00 AM - 12:30 PM D2.0.025 Workstation-Raum
Tuesday 11/19/24 09:00 AM - 11:00 AM EA.6.032
Contents

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.

Learning outcomes

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
Attendance requirements

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

Teaching/learning method(s)
  • Lecture and discussion
  • papers to read
  • lab course tutorials
  • assignments
Assessment

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.

Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Recommended previous knowledge and skills

Participation is restricted to MSc SCM students.

Availability of lecturer(s)

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

Other

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.

Unit details
Unit Date Contents
1 Unit 1

Exploratory geospatial analysis in SCM (1)

  • Introduction to Location Intelligence
  • Geospatial features: characteristics, data structures and transformations
  • Cluster detection and test statistics
  • global and local spatial autocorrelation
  • hands on lab: case studies

 

2 Unit 2

 Location-Allocation-Models in SCM (1)

  • Introduction to Location Models and Solution Methods
  • Basic Introduction to PuLP/Python
  • Discussion of case study
3 Unit 3

Exploratory spatial data analysis (2)

  • global and local spatial autocorrelation
  • hotspots in point patterns, kernel density
  • hands on lab: case studies
4 Unit 4

 Location-Allocation-Models in SCM (2)

  • Discussion of advanced concepts in LA Models
  • Python/PuLP exercises
  • Implementation of basic solution methods
5 Unit 5

Location Analytics in the Cloud

  • Distributed geospatial data, software and users: open source and on premise
  • Geospatial big data: data generation in the age of (M)IoT, sensor networks and citizen/customer screening
  • Sharing/publishing in SC business environments
  • hands on lab
6 Unit 6

Location-Allocation-Models in SCM (3)

  • Presentation of case studies
  • Discussion and Q&A
7 Unit 7

Written exam: models and methods

Last edited: 2024-07-08



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