Implementing artificial intelligence to promote sustainability efforts in manufacturing : a study of German SMEs

• Main Author: Schönhofen, Theresa
• Publication Date: 2024
• Format: Master thesis
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.14/46349
• topic: Artificial intelligence; Sustainability; Manufacturing; German SMEs; Influences; Implementation; Inteligência artificial; Sustentabilidade; Manufatura; PME alemãs; Influências; Implementação

This study explores the trending topic sustainability and the rapidly developing technology artificial intelligence (AI). The research analyzes their influences on the implementation of AI for sustainability efforts in manufacturing in the context of German small and medium sized enterprises (SMEs). The potential of AI to increase sustainability performance in manufacturing is still underexplored, particularly in the German SME sector and requires therefore further research. A qualitative research approach was applied and data was collected by interviewing twelve industry experts and analyzed using Gioia's methodology. The study highlights both the drivers, such as competitive pressure and the challenges, such as a lack of expertise, faced by German SMEs. Furthermore, opportunities for actions are identified through the conducted research, for example, collaboration with other SMEs, which can facilitate the implementation of AI for sustainability. It reveals that the main driver for the adoption of AI for sustainability efforts is cost reduction in manufacturing through process optimization and resource efficiencies. The existing literature highlights the potential of sustainable manufacturing in combination with AI to optimize resources such as energy consumption or waste disposal. This research adds to the literature by identifying advanced environmental AI optimizations that traditional approaches cannot fully exploit due to their limitations. Therefore, manufacturers could leverage their environmental and economic efforts to the maximum. This study aims to be a useful reference for both industry practitioners aiming to leverage AI for enhanced sustainability and researchers studying the intersection of technology and environmental management.