Predicting Utility Bills for Air Combat Command A Study of Forecasting Techniques

Many companies use forecasting techniques as a tool in managing their assets. Trends in such items as sales, population and inventory levels have all been determined at one time or another using forecasting, yet research indicates that these tools have not been utilized to predict utility budgets. This research was conducted to determine if such techniques could be applied to the specific task of predicting the utility bill at an Air Force base. Three quantitative models were chosen, the Moving Average, Exponential Smoothing and Regression, to determine their applicability to the task at hand. One base within Air Combat Command, Langley AFB, Hampton, Virginia, was studied due to its location and visibility. The models were tested using the actual data as reported to Air Combat Command. All models proved to be acceptable, with two of the models requiring further modification and refinement. This research concludes that the applicability of quantitative methods for predicting utility bills is viable, and recommends the use of the Regression model because of its ability to include multiple input variables impacting the outcome and its ability to address seasonality. Further refinements and modifications to the models analyzed in this research are recommended before their application on a broader scale