The consistency of production outputs relies on the operational effectiveness of manufacturing resources and the reliability of interconnected subsections of the production lines and equipment. In this study, lower production throughput, linked to operational inefficiencies and a higher frequency of longer downtimes in automated brick-making machinery, is observed in factory “A”. These random failures of automated brick manufacturing machinery at the factory where the research was conducted resulted in an average annual production loss of 13% in 2023, based on figures extracted from Network and Equipment Performance System Quality Assurance (NEPSQA), herein referred to as factory "A" for anonymity. Based on NEPSQA-tracked estimates, over 85% of these production losses are linked to downtime caused by inefficiencies in the production line. Lower production line reliability is caused by a higher number of deficiencies triggering manufacturing system failures. An Artificial Neural Network (ANN) algorithm was applied in this research to identify and distinguish production line deficiencies. The approach facilitated the development of appropriate Reliability-Centred Maintenance (RCM) activities to reduce failure occurrences. The ANN algorithm improved the RCM by reducing the Average Hours Interruption Indices (AHII) by 56.9% and the Average Frequency Interruption Indices (AFII) by 25.34% per month, as reported in the NEPSQA data.