Estimating maintenance budget using Monte Carlo simulation

Asset maintenance is driven by allocation of yearly maintenance budget (YMB). Lack of scientific methods for its evaluation has led to allocations that are subjective and erroneous causing wastage or lack of resources. The objective of the work is to estimate yearly budget for plant maintenance. In this work, the constraint of subjective quantification of budget influencing parameters proposed in the literature is removed. Suggested methodology is more realistic in a real world context and results into more rational estimation of maintenance budget. Graph theory and matrix approach model along with Monte Carlo simulations are deployed to evaluate maintenance budget by assigning random values to the budget parameters within specified range. MatLab code is deployed for its implementation. The budget influencing parameters are simulated for pre-defined maintenance levels. Inferential statistics is applied to find the appropriate distribution and evaluate the interval estimation of maintenance budget. The maintenance budget so estimated is validated by comparing it with world-class and best practices values reported in the literature. The Monte Carlo simulation results for different maintenance levels are presented with mean, variance and interval estimation of maintenance budget. The methodology suggested in this work will be a useful aid to maintenance managers in assigning YMB to their plant systems and provide indications for maintenance system corrections. This will avoid using historic data or subjective expert judgments.