A real options approach for joint overhaul and replacement strategies with mean reverting prices

Due to its significant impact on economic performance, an effective equipment overhaul and replacement strategy is a key aspect of physical asset management in capital-intensive industries, such as the mining industry. Classical approaches suggest periodic interventions based on the physical condition of the equipment, considering factors such as availability and operational costs. These fixed models generally ignore two important aspects: first, the flexibility of the decision to overhaul or replace, which may be re-evaluated within a given period, and second, the uncertainty around economic factors that may affect future maintenance decisions, such as the product price. This work improves on classical models by considering the effect of integrated price uncertainty in the definition of joint overhaul and replacement strategy, using a real options approach and a mean reversion binomial model to represent the uncertainty in price. More specifically, we develop a real options model and use a backwards recursion algorithm to determine an optimal intervention policy that maximizes expected profits. We then present a numerical study of the mining industry to validate the effectiveness of the proposed methodology. Results show that the option-based decision model economically outperforms the classical periodic strategy approach from with net present value increments ranging from 36.8 to 8.6%, according to the number of periods in the maintenance cycle, offering evidence that a new approach to equipment overhaul and replacement strategy is needed.