Many firms are occupied with determining the optimal replacement time of machinery. Machine replacement is a complex investment decision that requires the estimation of future cash flows and other parameters. The non-deterministic character of future cash flows has given rise to stochastic models, that take into account this uncertainty. This study has applied a theoretical stochastic asset replacement model in practice. It was found that the stochastic replacement model can be used on real data by performing a weighted least squares (WLS) regression. Decision-makers should however be aware of the model assumptions and limitations of the model. The replacement decision-making process can be automated using a Python script that is provided in this study. However, the CMMS that was used in the case study needs to be upgraded to have additional features. When one wants to perform an analysis of assets on a system level, the expected replacement year value can be used. Until now, the probability distribution of the expected replacement year had to be computed by means of Monte Carlo simulation. In this report, a closed form solution is used for the expected replacement year distribution when operating cost follows a geometric Brownian motion (GBM). With this contribution, decision-makers in engineering asset management now have the opportunity to rapidly analyse and perform probabilistic computations on the expected economic life of deteriorating machinery on system levels, such as geographic systems or weather systems. As only few studies on stochastic asset replacement are empirical, a second important contribution of this study is the application of the model in a case study. The case study concerns HVAC systems of petrol stations in the Netherlands. The paper describes how to perform a weighted least squares (WLS) regression so that model parameters can be easily estimated for real cases. Finally, several new insights and barriers on implementing theoretical models in practice are introduced.