Operating cost budgeting methods: quantitative methods to improve the process






Abstract Operating cost forecasts are used in economic feasibility studies of projects and in budgeting process. Studies have pointed out that some companies are not satisfied with the budgeting process and chief executive officers want updates more frequently. In these cases, the main problem lies in the costs versus benefits. Companies seek simple and cheap forecasting methods without, at the same time, conceding in terms of quality of the resulting information. This study aims to compare operating cost forecasting models to identify the ones that are relatively easy to implement and turn out less deviation. For this purpose, we applied ARIMA (autoregressive integrated moving average) and distributed dynamic lag models to data from a Brazilian petroleum company. The results suggest that the models have potential application, and that multivariate models fitted better and showed itself a better way to forecast costs than univariate models.

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