Business Analytics Systems in Logistics

Bachelor 2022/2023

Type: Elective course (Supply Chain Management and Business Analytics)

Area of studies: Management

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 4 year, 1 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Yulia A. Kuznetsova, Gleb Zakhodiakin

Language: English

ECTS credits: 4

Contact hours: 40

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Abstract

The discipline is focused on the study and practical skills of working with modern information technologies used by managers to support decision-making in logistics controlling, management and design of supply chains. The discipline introduces specific methods of data analysis of logistics business processes, data storage and processing, specific tasks and methods of operational and data mining, scenario planning and modeling methods. Practice sessions are met using modern data analysis and analysis systems, solution finders using a low-code approach, and industrial planning model development tools.

Learning Objectives

The course is aimed at building practical skills of working with modern information technologies used by managers to support decision-making in logistics controlling, management and design of supply chains probability.

Expected Learning Outcomes

A student understands the use of data analytical tools to support problem solving at various levels of management of the logistics system of the company.
A student selects and uses data collection and storage tools.
A student selects and uses tools for building analytical reporting.
A student analyzes and compares the functionality of Business Intelligence systems.
A student is able to visualize and to analyze spacial data using specialized software.
A student chooses methods of analyzing economic data depending on the specifics of the problem being solved.
A student is able to create and interpret data processing and analytical models using specialized software.
A student forms the concept of information and analytical support for managerial decision-making in the organization.

Course Contents

Introduction to Business Analytics
Data collection and storage technologies
Analytical reporting and dashboards
Business Intelligence systems
Geoinformation systems and tools for visualization and analysis of spatial data
Data mining and knowledge extraction

Assessment Elements

Participation
Solving in-class assignments. Participating discussions.
Practice
Group practical assignments.
Presentation
A 7-10 minutes presentation on the topic of the course.
Test
Electronic test in LMS for 10 minutes.
Project
The aim of the project is to develop a prototype analytical solution for a logistics and supply chain management problem using data processing and business intelligence tools covered in the course: Loginom, database management systems, some business intelligence tools. Public defense of the project will take place on the scheduled exam date. All members of the project team must be present at the defense. The format of the exam is face-to-face, as scheduled (except for students who have been officially approved for distance learning mode).

Interim Assessment

2022/2023 1st module
0.1 * Participation + 0.1 * Test + 0.4 * Project + 0.15 * Presentation + 0.25 * Practice

Bibliography

Recommended Core Bibliography

Greg Deckler. (2019). Learn Power BI : A Beginner’s Guide to Developing Interactive Business Intelligence Solutions Using Microsoft Power BI. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2252653
Joshua N. Milligan. (2019). Learning Tableau 2019 : Tools for Business Intelligence, Data Prep, and Visual Analytics, 3rd Edition. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2092866
Kantardzic, M., & Recorded Books, I. (2019). Data Mining : Concepts, Models, Methods, and Algorithms (Vol. Third edition). [Place of publication not identified]: Wiley-IEEE Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2282578
Murphy, T., & SAS Institute. (2018). Infographics Powered by SAS : Data Visualization Techniques for Business Reporting / C Travis Murphy. Cary, NC: SAS Institute. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1805001
Nabavi, M., & Olson, D. L. (2019). Introduction to Business Analytics. New York: Business Expert Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1922612
S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.

Recommended Additional Bibliography

Jukic, N., Vrbsky, S., & Nestorov, S. (2017). Database Systems : Introduction to Databases and Data Warehouses. Burlington, Virginia: Prospect Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1562389
Konecny, G. (2014). Geoinformation : Remote Sensing, Photogrammetry and Geographic Information Systems, Second Edition (Vol. Second edition). Boca Raton, FL: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1631976
Барсегян А., Куприянов М., Степаненко В., Холод И. - Технологии анализа данных: Data Mining, Text Mining, Visual Mining, OLAP. 2 изд. - 5-94157-991-8 - Санкт-Петербург: БХВ-Петербург - 2008 - 335156 - https://ibooks.ru/bookshelf/335156/reading - iBOOKS

Authors

MOROZOVA YULIYA ALEKSANDROVNA

Course Syllabus