KI-gestützte Business Intelligence im Supply Chain Management: Wahrnehmung und Auswirkungen des "Auto Bot" auf die Arbeitseffizienz

Abstract

(i) Motivation and Problem Statement This master's thesis examines the perception and expected impacts of AI-powered Business Intelligence (BI) on work efficiency in Supply Chain Management (SCM). The motivation stems from the increasing importance of Artificial Intelligence (AI) as a transformative technology that is causing a paradigm shift in SCM against the backdrop of digitalization and exponentially growing data volumes. Despite the enormous market potential, many AI projects fail not because of the technology itself, but due to organizational hurdles such as a lack of employee acceptance or insufficient change management. A research gap exists regarding empirical studies that investigate the actual perception of such technologies from the perspective of direct users during the critical, early implementation phase. This thesis, therefore, focuses on the newly introduced "Auto Bot" system at the DM distribution center in Enns as a concrete use case. (ii) Content Structure and Methodology The thesis is structured into seven chapters. Following the introduction (Chapter 1) and the presentation of the theoretical foundations of AI, SCM, and BI (Chapter 2) , Chapter 3 uses a literature review to analyze the positive and negative influencing factors of AI on BI and derives the research hypotheses from this analysis. The methodological approach (Chapter 4) is based on a quantitative cross-sectional survey design to measure immediate perceptions following the system's introduction. Using a standardized online questionnaire, data was collected from 26 employees at the Enns distribution center between May 29, 2025, and June 17, 2025, all of whom had participated in the kick-off meeting. The constructs were measured using a 5-point Likert scale. (iii) Concrete Results of the Thesis The empirical investigation shows that employees generally expect a significant increase in efficiency from using the "Auto Bot". This expectation is significantly driven by three positively perceived factors: AI-powered automation, improved decision support, and real-time data availability. Surprisingly, at this early stage, the postulated negative factors a lack of transparency, the need for specialized skills, change management challenges, and AI hallucinations showed no significant negative impact on the expected efficiency. User-friendliness and the age of the employees also had no significant impact. In the combined regression model, perceived automation proved to be the most statistically significant and thus most dominant predictor for the expected increase in efficiency.

Date of Award	2025
Original language	German (Austria)
Supervisor	Gerold Wagner (Supervisor)