Predictive Analytics Methods for Supply Chains

DS10
Semester: 3rd,
ECTS Credits: 15

Vasiliki Kazantzi

(Course Coordinator)

Syllabus

  • The importance of forecasting in the operation of systems, with emphasis on the Supply Chain
  • The role of data quality and its analysis in the accuracy and effectiveness of forecasts
  • Time series analysis methods and trend projection
  • Causal methods, structural factor analysis methods
  • Box-Jenkins method
  • Forecasting with seasonal data
  • Data classification trees
  • Regression trees
  • Neural networks
  • Cluster analysis
  • Multidimensional scaling
  • Problems and case studies in Logistics
  • Selection of forecasting model
  • Error estimation

Recommended Bibliography

  • Jaggia, S., Kelly, A., Lertwachara, K. and Chen, L. Business Analytics, McGraw Hill, 2023
  • Taylor, B.W. Introduction to Management Science, Pearson, 2019
  • Render, B., Strair, R., Hanna, M., Hale, T. Quantitative Analysis for Management, Pearson, 2018
  • Barry Keating, J. Holton Wilson and John Solutions Inc. Forecasting and Predictive Analytics with Forecast X, McGraw Hill, 2019
  • Heizer, J., Render, B. and Munson, C. Operations Management: Sustainability and Supply Chain Management, Pearson 2023
  • Vandeput, N. Data Science for Supply Chain Forecasting, De Gruyter, 2021
  • Abbott, D. Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, John Wiley & Sons, 2014