Crude oil is the mixture of petroleum liquids and gases that is extracted from the ground by oil wells. It is an important source of fuel and is used in the production of several products. Given the important role price of the crude oil plays, it becomes extremely important for managers to predict future oil price while making operational decisions such as: when to purchase material, how much to produce and what modes of transportation to use. The goal of this paper is to develop a forecasting model to predict the oil prices that aid management to reduce operational costs, increase profit and enhance competitive advantage. We first analyze the primary theories related to the forecast of oil price followed by the reviews of two main streams of forecast theory, which are Target Capacity Utilization Rule (TCU) and Exhaustible Resources Theory. We implement a Target Capacity Utilization Rule recursive simulation model and test it on the historical data from 1987 through 2017 to predict crude oil prices for 1991 through 2017. We tried several variations of the base model and the best method produced MAD, MSE, MAPE and MPE of 12.676, 280.92, 0.2597, 0.028, respectively. We further estimated the forecasts of the oil prices at a monthly level based on our yearly forecast of oil prices from our best method. The calculated MAD, MSE, MAPE and MPE values are 5.66, 82.1163, 0.1246 and 0.038, respectively, which shows our model is promising again at a monthly level.