Der Einsatz von Machine Learning in der Supply Chain von österreichischen Industrieunternehmen

Abstract

This master’s thesis examines the role of machine learning in the supply chain of Austrian industrial companies. Against the backdrop of current challenges such as global crises including the COVID-19 pandemic and the war in Ukraine, as well as rapid technological progress, the study highlights both the potential and the obstacles associated with the application of machine learning. The objective of the thesis is to analyze the current use of machine learning in the supply chain, to identify facilitating and hindering factors of its implementation, and to evaluate the extent to which machine learning can contribute to creating a sustainable competitive advantage. To address these questions, a qualitative research design was chosen. Between May and July 2025, guideline-based interviews were conducted with eight experts in the field of supply chain management from Austrian industrial companies. The data were analyzed using Mayring’s qualitative content analysis, which enabled a systematic identification of key themes, patterns, and interrelationships. The findings reveal that machine learning has so far only been applied in initial areas within the supply chain of Austrian industrial companies. Current applications are mainly concentrated in demand forecasting, quality control, transport optimization, and risk management. Key facilitators of successful implementation include high data quality, consistent information systems, strong management support, and interdisciplinary collaboration between departments. In contrast, significant barriers consist of limited human and financial resources, insufficient data availability, economic uncertainties, and cultural reservations toward data-driven decision-making. Furthermore, the interviews indicate that machine learning can make a substantial contribution to decision support by improving forecasting accuracy, transparency, and efficiency in the supply chain. However, a sustainable competitive advantage can only be achieved if companies build trust in the systems, establish clear responsibilities, and strategically integrate machine learning into corporate practice. Overall, the thesis contributes to a deeper understanding of the current state, the challenges, and the opportunities of applying machine learning in the supply chain of Austrian industrial companies. It demonstrates that success depends not only on technological factors but also, to a large extent, on organizational, cultural, and strategic conditions.

Date of Award	2025
Original language	German (Austria)
Supervisor	Sonja Straßer (Supervisor)