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Forecasting is a vital part of the planning process of most private and public organizations. A number of extant measures: Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE), have been used to assist in judging the forecast accuracy, and concomitantly, the consequences of those forecasts. In this paper we introduce, evolve, and implement a practical and effective method for assessing the accuracy of forecasts, the Percent Forecast Error (PFE). We test and evaluate the PFE, and modified optimized PFE (MOPFE), against the MAD, MSE, and MAPE measures of forecast accuracy using three time series datasets.

Frequently, problems involving a management activity involve the incurring of a setup charge for the activity. In these cases, the total cost of the activity is the sum of the variable cost related to the level of the activity and a set up cost required to initiate the activity. In this research, we will also use goals that involve demand management of service centers based upon the revenue potential of the customer districts they will serve.

This paper will detail the development of a multi-objective mathematical programming model for audit sampling of balances for accounts receivable. The nonlinear nature of the model structure will require the use of a nonlinear solution algorithm, such as the GRG or the genetic algorithm embedded in a Solver spreadsheet modeling system, to obtain appropriate results.

The number of business analytics classes and programs has been rapidly increasing over the past 20 years. However, while we are educating students with the knowledge of advanced analytical techniques and tools, we are not providing them with sound problem-solving skills. In an effort to improve student problem-solving skills by increasing their understanding and increasing their engagement, in this chapter we will describe, discuss the implementation of, and discuss the results of employing an experiential learning (EL) exercise in an analytics course.

This research examines the use of econometric models to predict the total net asset value (NAV) of an asset allocation mutual fund. In particular, the mutual fund case used is the Vanguard Wellington Fund (VWELX). This fund maintains a balance between relatively conservative stocks and bonds. The period of the study on which the prediction of the total NAV is based is the 24-month period of 2010 and 2011 and the forecasting period is the first three months of 2012. Forecasting the total NAV of a massive conservative allocation fund, composed of an extremely large number of investments, requires a method that produces accurate results. Achieving this accuracy has no necessary relationship to the complexity of the methods typically employed in many financial forecasting studies.

Data envelopment analysis (DEA) is a multicriteria technique which can take into account multiple inputs and outputs to produce a single aggregate measure of relative efficiency for a set of comparable units. DEA takes into consideration other objectives by including the appropriate variables as part of the DEA model. However, as we will demonstrate, collapsing all the inputs and outputs of several objectives into one aggregate performance measure weakens DEA's ability to discriminate the individual impact of each of these objectives. In this chapter, we apply a multiple objective extension to DEA, called multiple objective DEA (MODEA), which simultaneously controls the weights assigned to the variables found in more than one objective. This MODEA approach more fully measures the impact of each objective and allows the decision-maker to address trade offs among these objectives. The usefulness of the MODEA approach is demonstrated by applying it to the hypothetical example.