Applying the generalized autoregressive conditional Heteroskedastic model to analyze and forecast the field failure data of repairable systems

Purpose – Analyzing and forecasting reliability is increasingly important for enterprises. An accurate product reliability forecasting model cannot only learn and track a product's reliability and operational performance, but can also offer useful information that allows managers to take follow‐up actions to improve the product's quality and cost. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is already extensively used to analyze and forecast time series data. However, the GARCH model has not been used to analyze and forecast the failure data of repairable systems. Based on these concerns, this study proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Design/methodology/approach – This paper proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Empirical results from electronic systems designed and manufactured by suppliers of the Chrysler Corporation are presented and discussed. Findings – The proposed method can analyze and forecast failure data for repairable systems. Not only can this method analyze failure data volatility, it can also forecast the future failure data of repairable systems. Originality/value – Advanced progress in the field of reliability prediction estimation can benefit engineers or management authorities by providing important decision support tools in which the prediction accuracy suggests financial and business outcomes as well as other outcome application results.