Optimizing field service operations with NLP and deep learning-based spare parts prediction

Field service technicians typically rely on their experience and fault descriptions to identify and select the appropriate spare parts for repairing an appliance. However, missing parts often lead to multiple service visits, increasing operational costs and reducing customer satisfaction. To address this issue, we propose DL-SPP. sbert , a Deep Learning-based Spare Part Prediction system that leverages NLP and structured data to accurately predict the spare parts needed for repairs. DL-SPP. sbert processes historical repair data, appliance metadata, and fault descriptions by leveraging SBERT sentence embeddings to capture the semantic meaning of textual inputs. Unlike retrieval-based methods, our system directly learns the relationships between input features and spare parts usage through a multi-label classification model. DL-SPP. sbert was evaluated on two real-world case studies, where it achieved up to a 64 % reduction in multiple-visit interventions, surpassing the performance of state-of-the-art baselines. A detailed explainability study highlights the key role of fault descriptions and appliance metadata in prediction accuracy, confirming DL-SPP. sbert as an effective tool for optimizing spare parts provisioning. Finally, experimental results indicate that updating the model with newly collected data is fundamental to preserve predictive performance over time in continuously evolving real-world industrial settings. • Novel approach based on NLP and deep learning for spare parts prediction in appliance repair. • Uses structured data and textual fault description to enhance predictive accuracy. • Outperforms state-of-the-art methods in two real-world industrial cases. • Cuts unnecessary field service technician visits by up to 64 %, improving service efficiency. • Quality of textual fault descriptions is key to predictive performance.