The effect of combining reliability, availability and maintainability modelling and stochastic simulation modelling on production efficiency

Abstract

Many world leaders in manufacturing are currently designing future facilities at various global locations. A Reliability, Availability and Maintainability (RAM) study is an important part of the basic engineering process. State-of-the-art RAM modelling, however, was not able to address the combined variability and complexity of oil and gas facilities. An opportunity thus arose to use stochastic simulation in combination with RAM models to meet the challenge of determining the reliability and production efficiency of a facility. This approach also allowed new factors to be considered in the RAM analysis such as ramp-up/down rates, upstream and downstream upsets and storage. RAM modelling identifies the critical equipment and systems that contribute to lost production and defines the frequency and duration of outages. The basis is a Reliability Block Diagram (RBD) with parallel and series equipment configurations. This deterministic calculation can be complemented by the use of a Monte Carlo simulation to assess stochastic factors. Neither of these techniques, either alone or in combination, was able to address the variability and complexity of a major value chain. The combination of RAM and Stochastic models demonstrates best practice for process reliability modelling of manufacturing companies with complex value chains.