Forecasting field failure data for repairable systems using neural networks and SARIMA model

Purpose – To propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs. Design/methodology/approach – This study proposes a method for analysing and forecasting field failure data for repairable systems. The novel method constructs a predictive model by combining the seasonal autoregressive integrated‐moving average (SARIMA) method and neural network model. Findings – Current methods for analysing and forecasting field failure data for repairable systems do not consider the seasonal effect in the data. The proposed method can not only analyse the trends and seasonal vibration of the data, but can also forecast the short‐ and long‐term reliability of the system based on only a small amount of historical data. Research limitations/implications – This study adopts only real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method. Practical implications – Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements. Originality/value – The proposed method is superior to other prediction techniques in predicting future real failure data.