Application of Ensemble Models to Predict Quality Performance in Supply Chains

Information plays a key role in today's world, and the ability to work with data is an increasingly important skill. Processing and analyzing information allows making informed decisions, optimizing processes and adapting to a rapidly changing environment. Big data and predictive analytics are becoming tools for improving the efficiency and competitiveness of companies in various industries, especially in logistics. The diploma thesis deals with the problem of selecting a forecasting method in supply chains. Different methods and approaches are analyzed and their applicability for specific tasks and indicators is determined. The work consists of three chapters: the first chapter is devoted to the literature analysis and definition of forecasting objectives, the second chapter is devoted to the problem statement and characterization of approaches, the third chapter is devoted to the solution implementation and analysis of its effect. The research is relevant because forecasting allows adapting to changes in the external and internal environment, which is especially important for continuous supply chain processes.