Optimizing Your Asset Replacement Strategy
Proceedings Publication Date
Huub Roeterink
Maurice Vos, Huub Roeterink
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A generic decision problem for companies operating or owning ageing network assets is when to replace components. Should you preventively replace components or correctively? This paper presents a real-life case addressing this question. The case concerns an economic decision model that has been built for a large-scale renovation program for gas pressure reduction stations (PRS) in the network of a leading gas transmission system operator. The industrial case comprises a pool of about 1000 PRS which became operational over a period of several decades. A PRS has two or three skids, each skid comprising about ten components, though the exact configuration and age of the stations varies. Therefore, an optimal replacement strategy is to be defined reflecting the differences. The methodology assumes a base scenario in which the whole skid with ten components is replaced preventively. Alternative scenarios are explored in the approach, e.g. to replace an individual component not before it will definitively fail (run-to-failure), or to replace some components preventively, and let the other run to failure. Main output of the economic decision model is an expected Net Present Value (NPV) of the specified time horizon for each replacement scenario. Main model input is the lifetime distribution of individual components. Other inputs are cost data for replacements and corporate financial parameters. By means of a Monte Carlo simulation, a NPV distribution is calculated per scenario and the average is presented as key output. The functionality of the Excel based decision tool is appropriate for a broad variety of asset replacement decisions. It delivers the best economic choice by comparing different replacement strategies using a company-wide accepted financial evaluation criterion. The decision tool integrates different knowledge areas of a company such as finance, procurement and asset integrity & maintenance management.

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