Automating Routing of Product Returns for Failure Analysis with Neuro-Symbolic AI

Abstract Before failure analysis (FA) can start, a product must get from the customer to the correct location, which is not always trivial, especially in larger companies with many FA labs. Automating and optimizing this routing, therefore reducing manual labor, misrouting, and turnaround time, requires the development of problem-solving methods utilizing both explicit and implicit knowledge. The first type refers to known routing rules, e.g., based on lab equipment or certifications, whereas the second type must be induced from available data, e.g., by analyzing customer descriptions using machine learning (ML) methods. Therefore, to solve the routing problem, we suggest a neurosymbolic integration of natural language processing methods into the symbolic context of a logic-based solver. The conducted evaluation shows that the suggested method can reduce the reships by appr. 33% while ensuring the fulfillment of all shipment constraints.