Syllabus

Title
2239 Location Analytics in Supply Chain Management 1 (LA 1)
Instructors
Assoz.Prof PD Dr. Vera Hemmelmayr, Ass.Prof. Mag.Dr. Petra Staufer-Steinnocher
Contact details
petra.staufer-steinnocher@wu.ac.at; vera.hemmelmayr@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/07/20 to 09/07/20
Registration via LPIS
Notes to the course
This class is only offered in winter semesters.
Subject(s) Master Programs
Course I - Location Analytics and Geospatial Data
Dates
Day Date Time Room
Tuesday 10/06/20 09:00 AM - 12:30 PM Virtueller PC-Raum S
Thursday 10/15/20 09:00 AM - 12:30 PM Virtueller PC-Raum M
Tuesday 10/20/20 09:00 AM - 12:30 PM Virtueller PC-Raum S
Tuesday 10/27/20 09:00 AM - 12:30 PM Virtueller PC-Raum S
Tuesday 11/03/20 09:00 AM - 12:30 PM Virtueller PC-Raum S
Tuesday 11/10/20 09:00 AM - 12:30 PM Virtueller PC-Raum S
Tuesday 11/17/20 10:00 AM - 02:30 PM Online-Einheit

On this page:

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Procedure for the course when limited activity on campus

The class in Location Analytics in Supply Chain Management will be held in full distance mode. Even in the remote format, the sessions will take place within the announced time slots via MS Teams and virtual PC Labs, including software tutorials and online coaching.

The defined assignments will still be held in the regular format (also see syllabus section "Assessment").  The conditions of the final exam will be the same for every class participant and, hence, in distance mode an online exam.

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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.

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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
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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.

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

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Readings
1 Author: Laporte, Gilbert, Nickel, Stefan, Saldanha da Gama, Francisco
Title:

Location Science

Publisher: Springer
Edition: 2
Year: 2019
Content relevant for class examination: Yes
2 Author: Eiselt, H.A., Marianov, Vladimir
Title:

Foundations of Location Analysis

Publisher: Springer
Year: 2011
Content relevant for class examination: Yes
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Prerequisites for participation and waiting lists

For incoming exchange students: 10 ECTS in Supply Chain Planning, Global Supply Chain Design, Transport/Logistics (Network) Management/Analysis/Planning, Operations Research, or Geographic Information Systems and Analysis, Spatial Business Intelligence/Location Analytics.

Register together with course no 2240 Location Analytics in Supply Chain Management 2 (LA 2)

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Recommended previous knowledge and skills

Participation is restricted to MSc SCM students.

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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

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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.
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Unit details
Unit Date Contents
1 Unit 1

    Exploratory geospatial analysis in SCM (1)

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

    Download learning materials
        2 Unit 2

        Exploratory spatial data analysis (2)

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

        Download learning materials
        3 Unit 3

         Location-Allocation-Models in SCM

        • Introduction to Location Models and Solution Methods
        • Basic Introduction to Python
        • Discussion of case study

        Download learning materials
        4 Unit 4

        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

        Download learning materials
        5 Unit 5

         Location-Allocation-Models in SCM (2)

        • Discussion of advanced concepts in LA Models
        • Python exercises
        • Implementation of basic solution methods

        Download learning materials
        6 Unit 6

        Location-Allocation-Models in SCM (3)

        • Presentation of case studies
        • Discussion and Q&A

        Download learning materials
        7 Unit 7

        Written exam: models and methods
        Last edited: 2020-10-05



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