Conventional control charts often assume normality, which may not hold for many engineering processes. In cases where processes follow an Inverse Maxwell \(\left( {{\text{IM}}} \right)\) distribution, as seen in various industrial applications, it becomes crucial to employ suitable monitoring methods. To address this gap, this study introduces the hybrid exponentially weighted moving average ( \({\text{HEWMA}}_{{{\text{IM}}}}\) ) chart for the \({\text{IM}}\) distribution. Performance evaluation includes metrics like average run length, median run length, and standard deviation run length. Comparative analysis with existing IM distribution-based charts such as the Shewhart V chart ( \({\text{V}}_{{{\text{IM}}}}\) ), exponentially weighted moving average ( \({\text{EWMA}}_{{{\text{IM}}}}\) ), and extended EWMA ( \({\text{EEWMA}}_{{{\text{IM}}}}\) ) charts reveal the \({\text{HEWMA}}_{{{\text{IM}}}}\) chart’s superior efficiency. Real-world applications in brake pad production and carbon fiber strength testing validate its practicality and engineering applications. In conclusion, \({\text{HEWMA}}_{{{\text{IM}}}}\) is a novel tool tailored to monitor \({\text{IM}}\) processes efficiently, offering enhanced process monitoring for diverse industries.
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