Machine learning approaches for manufacturing usually o er recommendation lists, e.g., to support humans in fault diagnosis. For instance, if a product does not pass the final check after the assembly, a recommendation list may contain likely faulty product components to be replaced. Thereby, the list ranks these components using their probabilities. However, these probabilities often di er marginally, while economic impacts, e.g., the costs for replacing components, di er significantly. We address this issue by proposing an approach that incorporates costs to re-rank a list. Our evaluation shows that this approach reduces fault-related costs when using recommendation lists to support human labor.