Integrating Artificial Intelligence into Continuous Improvement: An AI-Assistant Supporting 8D Problem-Solving in Automotive Manufacturing

This work-in-progress paper examines how an AI-based assistant can be embedded within established continuous improvement processes to enhance value stream analysis in an automotive supplier environment. Drawing on a revelatory single-case study of a German automotive supplier that deployed an AI-Assistant in collaboration with a specialised German start-up, the research investigates the coupling of artificial intelligence with the 8D problem-solving methodology. Using a qualitative research design grounded in grounded theory principles, data were collected from multiple sources across the manufacturing operation and analysed through open, selective, and theoretical coding. Preliminary findings indicate that embedding the AI-Assistant into the 8D cycle produces substantial operational gains, including reductions in order lead time of at least 50 percent (reaching up to 90 percent in selected areas), an increase of at least 80 percent in the value creation rate for materials subjected to completed 8D measures, and meaningful reductions in manual scheduling effort, scrap, and work-in-progress. Beyond these quantifiable results, the integration yielded qualitative benefits in transparency, systematic problem identification, master data quality, and root-cause analysis speed. The paper contributes empirical evidence to the Industry 4.0 literature by demonstrating that AI technologies can augment rather than displace established lean methodologies, thereby addressing a critical gap identified in prior research on integrated AI-enabled manufacturing optimisation systems.