No abstract

Hydraulic shovels are large-scale equipment that set the pace for the mineral extraction process, so ensuring the service level of your repairable components is considered critical because of its impact on availability.

The research sought to generate a model using Monte Carlo Simulation, which allows predicting the mean time to repair (MTTR) and lost profits due to machine stoppage, two of the most important indicators for decision making in planning and programming maintenance, in this case to serve a fleet of 4 EX5500 model hydraulic shovels that operate in an open pit mining site, and that are high-cost equipment with a great impact on the extractive process. The model was developed on the Excel computer platform, managing to simulate the random behavior of the maintenance services, which determine the working times in the machine and, consequently, the detention of the equipment, whose penalty is reflected in the loss of profit. The model managed to project maintenance services with 67% success, from 180 iterations with 10 replications, estimating an MTTR of 375 hours per month, with an associated lost profit of 31,457,464 USD.

Equipment-intensive industries must manage critical components due to their impact on the availability and high inventory carrying costs. In this context, this study seeks to assess mean times between interventions (MTBI) and mean times between failures (MTBF) to determine optimal replacement times for critical repairable components used in six EX5500 hydraulic excavators operating at an open-pit mining site. For these purposes, the authors compared a base policy using the MTBF values provided by the equipment manufacturer, against the proposed policy using the MTBI values obtained from equipment intervention records. The results from the study, revealed that the MTBI policy was able to streamline the replacement times for critical repairable components, thus, generating a cost optimization model at a higher level of reliability