Quantifying production availability before and after design decisions are made — so that equipment selection, redundancy levels, spare part holdings and maintenance strategies are based on modelled evidence, not engineering intuition.
Most operational availability targets are set by commercial teams without quantitative analysis of whether the asset system can achieve them. The result is reliability targets that the plant was never designed to meet — and maintenance budgets that cannot compensate for design-stage decisions that have already been made.
RAM modelling is a stochastic simulation technique that models an asset system — from individual component failure rates through to system-level production availability — using Monte Carlo simulation to quantify the probability distribution of outcomes. It answers the question: given what we know about these assets, equipment and maintenance strategies, what availability can this system actually achieve? RAM models are built from ISO 14224 failure rate data, vendor reliability specifications and operational experience — calibrated to your specific operating environment, maintenance strategy and spare part holdings.
RAM modelling capability is assessed within GARPI™ Dimension 2 (Reliability Engineering). Find out how your organisation's use of quantitative reliability analysis compares to industry peers — and identify where reliability engineering investment delivers the highest return.