Modules

First Semester

Big Data Analytics and Forecasting in Logistics & Supply Chain Management




Course Objectives

The purpose of the course is twofold. The first is to introduce students to the notion of Big Data and in the ways that it can help them make better L & SCM decisions. The second purpose of the course is to familiarize students with the basic time series analysis methods that are useful in forecasting customer demand, one of the most important initial stages in almost any L & SMC decision process, in strategic, tactical or operational level.

By completing the course, the students will also have knowledge of how they can make practical use of the capabilities of modern L & SCM Big Data Analytics Tools and to use the models that are suitable for forecasting time series with different characteristics.

Course Description

The course presents what is L&SCM Big Data, and highlights Applications of Big Data Analytics in L & SCM areas such as supply chain network design, procurement management, inventory management etc. The creation and processing of Big Data are examined, together with their analysis through statistical and visualization tools.

In addition, the course presents and examines mathematical models useful for the analysis of time series that differentiate according to the presence or absence of trend and seasonality, as well as with periods with zero demand. The analysis of time series is conducted in order to forecast future customer demand.

During the module there will be application of popular Big Data Analytics Tools such as Tableau and Qlik Sense. Also, the students will have the opportunity to familiarize with time series analysis and forecasting using spreadsheets.

Suggested Textbooks

  • Peter W. Robertson, Supply Chain Analytics: Using Data to Optimise Supply Chain Processes, Routledge, 2020.
  • Iman Rahimi et al. Big Data Analytics in Supply Chain Management: Theory and Applications, CRC Press 2020.
  • Bernard Marr, Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, Wiley 2016.
  • Glenn J. Myatt, Wayne P. Johnson, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining 2nd Edition, Wiley 2014.
  • James R. Evans, Business Analytics: Methods, Models, and Decisions, 3rd edition, Pearson, 2019.
  • Alexander Loth, Visual Analytics with Tableau, Wiley 2019.
  • Joshua N. Milligan, Learning Tableau 2020: Create effective data visualizations, build interactive visual analytics, and transform your organization, 4th Edition, Packt Publishing 2020.
  • Pablo Labbe, et al. Hands-On Business Intelligence with Qlik Sense: Implement self-service data analytics with insights and guidance from Qlik Sense experts, Packt Publishing, 2019.
  • Nahmias, S. and Olsen, T.L. Production and Operations Analysis, 7th edition, McGraw-Hill, 2015.
  • Silver, E.A., Pyke, D.F. and Thomas, D.J. Inventory and Production Management in Supply Chains, 4th edition, CRC Press, 2017.

Evaluation

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