Base Knowledge
No prior knowledge is required.
Teaching Methodologies
The teaching methodology promotes the development of practical and research skills, in specific areas of Business Intelligence, which the student perceives as relevant for their training, for the organization where they are inserted or the area where they intend to develop their future career, always applied nature.
A “Problem-Based Learning” approach is applied and students will be able to use the projects developed as part of their portfolio of works that confirm skills in various areas of Data Analytics applied to logistics.
To be successful, you will have to carry out work on using a data set, ensure data quality and use a tool for data visualization, and this work is aimed at applying the acquired knowledge to the elaboration of a set of visualization elements that allow decision making based on this data.
The practical nature of this teaching option is in line with the professional nature of this Master’s Degree.
Learning Results
GOALS
The objectives of the Business Intelligence in Logistics UC are the following:
– identify key concepts and trends associated with BI and BI in Logistics;
– know the components of a BI system, methodologies for its implementation, use and management in particular in the area of Logistics;
– Know and use Self Service BI tools to visualize data in logistics;
– understand the relevance of using data warehouses in logistics;
– Anticipate trends in BI in logistics and allow the connection between this UC and the following Curricular Units.
SKILLS
It is intended that students, at the end of this UC, have acquired the following skills:
– Know how to evaluate the new BI trends in Logistics and their application to companies and organizations
– master the components of a BI system, methodologies for its implementation, use and management in particular in the area of Logistics;
– efficiently use, proposing solutions adapted to the reality of an organization/company, the Power BI tool applied to the visualization of data in logistics;
– be able to suggest application contexts for data warehouses in logistics;
– Identify new trends in BI in logistics, in addition to what was studied in this UC, so that they can apply this knowledge in future contexts.
Program
P1 – Business Intelligence and Logistics Integration
P2 – Components of a Business Intelligence System
P3 – Identification of patterns in logistic data and reporting of trends
P4 – Data Warehouse Applications in Logistics
P5 – Methodologies for implementing a Business Intelligence project
P6 – New BI trends in Logistics
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Main references
- Tipi, N. (2021). Supply Chain Analytics and Modelling: Quantitative Tools and Applications. Kogan Page.
- Vandeput, N. (2021). Data Science for Supply Chain Forecasting, 2nd Edition. De Gruyter.
- Few, S.(2019).The Data Loom: Weaving Understanding by Thinking Critically and Scientifically with Data, AnalyticsPress.
Complementary references
- Pochiraju, B., & Seshadri, S. (Eds.).(2019).Essentials of Business Analytics: An Introduction to the Methodology and its applications (Vol. 264). Springer.
- Stefanovic, N., & Stefanovic, D.(2009).Supply chain business intelligence: technologies, issues, and trends. In Artificial intelligence an international perspective (pp. 217-245). Springer, Berlin, Heidelberg.
- Chae, B., & Olson, D. L.(2013).Business analytics for supply chain: A dynamic-capabilities framework. International, Journal of Information Technology & Decision Making, 12(01), 9-26.
- Grabińska, A., & Ziora, L.(2019).The application of Business Intelligence systems in logistics. review of selected practical examples. System Safety: Human-Technical Facility-Environment, 1(1).