 A memory type control chart is an important tool of statistical process control for monitoring small to moderate shifts in the manufacturing process. This paper proposes a new hybrid exponentially weighted moving average, HUMA, control chart based on Bayesian theory using ranked set sampling, RSS, schemes for posterior and posterior predictive distributions with informative priors and different loss functions, LFs. The proposed Bayesian HUMA control chart is compared against existing Bayesian average exponential weighted moving average of Bayouma and Bayesian HUMA control charts under simple random sampling, SRS, schemes. The results show that the proposed Bayesian HUMA control chart under RSS schemes is more sensitive in detecting out-of-control signals than the Bayouma and Bayesian HUMA control charts under SRS schemes. This article was authored by Mad Khan, Das Mohamed Khan, Mohamed Nora Lameen and others.