• repair task materials
• field service techincians
• household appliance maintenance
• spare parts prediction

This master's thesis explores the domain of spare parts prediction for household appliance repair tasks within the field of Artificial Intelligence. The study involves the analysis of a dataset from a Swiss household appliance company, focusing on the adaptation of an existing system to the new data. The research extends to the enhancement of the system through the optimization of the algorithm's search logic and the refinement of spare parts categorization. Additionally, a novel version of the algorithm is proposed, capable of dynamically predicting the number of required parts based on the input task's complexity. The achieved outcomes showcase a stable and portable system, demonstrating improved efficiency and adaptability in predicting spare parts for household appliances.