On Predicting Spare Parts for Field Services by Leveraging Fault Description and Historical Repairing Data

The traditional process of repairing electrical appliances, including industrial appliances, is activated by a fault report submitted by the owner of the appliance that summarizes the symptoms of the fault. Then, based on their experience and on the fault report, the field service technicians visit the customer, bringing the spare parts they consider necessary. If the on-site repair requires spare parts that the technicians did not bring, they are forced to return to the customer’s location, thus increasing costs and causing customer dissatisfaction. To improve the success rate and consequently to reduce the number of visits, we propose a Spare Parts Prediction (SPP) system which integrates principles from Case-Based Reasoning and Natural Language Processing. This system recommends a sorted list of spare parts by analyzing the appliance fault descriptions and leveraging historical data of repair and maintenance interventions. Application of the SPP system in a real-world scenario presented a substantial reduction of multiple visits up to 50%.