tThis paper reviews the literature and then applies power transformer case studies to develop a riskassessment model that augments the probabilistic capabilities of Reliability-centred maintenance (RCM).It is usually very difficult to assess the effectiveness of the RCM at its inception, when data is inadequate orwhen it is implemented on a large population of assets, especially when most of them are small in size asin the distribution systems. Thus, the major contribution of the study rests on applying a key performanceindicator (KPI) for assessing the effectiveness of the RCM programmes, obtained by trending the profileof the mean-time-to-first-failure (MTTFF) and the average annual repair costs. The MTTFF, determinedusing Markov analysis, is inversely proportional to the costs. Besides, the method of moments is applied tostatistical, historical data to generate a failure probability distribution comparative model, which lacksin the current practices for conducting a failure mode effect and criticality analysis (FMECA). Finally,the Markov derives complement of uptime-steady-state probability as input for FMECA, using limiteddata; which is an improvement on the current approaches. The overall approach developed offers a costeffective risk-priority-screening model for transformers, which can be applied prior to rigorous testingand inspection procedures on individual items during the RCM application.