Synergien von Lean Management und Künstlicher Intelligenz in der Produktion: Potenziale für das Qualitätsmanagement

Abstract

To date, a wide range of empirical and theoretical literature has addressed the interdependence between information and communication technologies and lean manufacturing. However, a gap remains in current research regarding the analysis of interfaces and interactions between artificial intelligence and lean manufacturing. In light of increasing business challenges, particularly within the European manufacturing sector, integrated approaches aimed at further development are gaining strategic importance for enhancing overall organizational performance and ensuring long-term competitiveness. Against this background, the central research question arises as to the extent to which the application of artificial intelligence can optimize lean management in manufacturing companies, with a specific focus on quality management in the automotive industry. This existing research gap currently prevents manufacturing enterprises from fully leveraging the synergistic potential of both approaches to strengthen their competitive position. This master’s thesis is structured into six chapters. The introductory chapter outlines the relevance of the topic, elaborates on the problem statement, and derives the research objectives and guiding questions. Based on this foundation, the methodological approach is described to ensure the transparency of the scientific procedure. Chapter Two establishes the theoretical foundations of quality management within an industrial context. Chapter Three addresses the principles, methods, and current significance of lean management. Chapter Four discusses the technological foundations of artificial intelligence and its application in quality management. Chapter Five forms the analytical core of the thesis, systematically examining the synergies between artificial intelligence and lean management in the domain of quality management. This includes identifying specific areas of influence and opportunities for optimizing quality management processes. Furthermore, the development of decision-making processes through artificial intelligence, as well as related opportunities and barriers, are explored. The final chapter summarizes the key findings, acknowledges limitations, and provides an outlook on future research directions. The analysis of the underlying principles and methods of lean management and artificial intelligence illustrates the synergistic potential that emerges from their integration in the context of quality management. The targeted combination of the strengths of both approaches enables manufacturing companies to respond effectively to economic challenges and, particularly within the automotive industry, to secure long-term competitiveness. The resulting synergy effects make a substantial contribution to the optimization of quality-related processes in production. Taking into account the identified opportunities and potential obstacles, these effects can be purposefully leveraged to not only enhance quality management but also sustainably improve the quality of strategic decisionmaking.

Date of Award	2025
Original language	German (Austria)
Supervisor	Hans-Peter Feichtenschlager (Supervisor)