Bayesian enhanced strategic decision making for reliability.

Successful strategies for maintenance and replacement require good decisions. We might wish to determine how often to perform preventive maintenance, or the optimal time to replace a system. Alternatively, our interest might be in selecting a threshold to adopt for action under condition monitoring, or in choosing suitable warranty schemes for our products. Stochastic reliability models involving unknown parameters are often used to answer such questions. In common with other problems in operational research, some applications of maintenance and replacement are notorious for their lack of data. We present a general review and some new ideas for improving decisions by adopting Bayesian methodology to allow for the uncertainty of model parameters. These include recommendations for specifying suitable prior distributions using predictive elicitation and simple methods for Bayesian simulation. Practical demonstrations are given to illustrate the potential benefits of this approach.