Predictor Modelling of Production Tonnages of Two Truck Fleets of a South American Open-pit Mining Company

In loading and transportation operations, production is affected mainly by the increase in the distances between the pits, increasing the costs per transported ton. Therefore, it is imperative to predict the performance behaviour of mining trucks to develop the best production strategies. This was done by modelling a predictor through the multiple linear regression formula and the SSD estimation error looking for its minimum value to make effective predictions, based on the ASARCO standard and its derived indicators, through real data of two fleets of a South American open-pit mining company. The predictor model has an estimation error of 0.0346 in the first Komatsu 930E-4SE fleet and of 0.0083 in the second Liebherr T282B fleet, where the close relationship of the latter two and their meagre dispersion when comparing their actual tonnage are highlighted and predicted, evidencing its notable trend line and very close upper and lower limits. In conclusion, the model is effective in predicting the production tonnages of both fleets with minimal error.