Extrapolative forecasting models have been available for many years and as most organizations have the need to regularly develop forecasts one might anticipate the widespread use of these models. The evidence in Australia indicates that computer based forecasting systems are not being widely used and in fact a number of established systems have been discarded, with the issue of forecast accuracy often being mentioned as a problem area. Two experiments are carried out to examine this issue by comparing judgemental and quantitative forecasts. Other problem areas mentioned as contributing to the abandonment of forecasting systems include the difficulty of manually reviewing the computer forecasts and the effort required to carefully massage the forecast database to remove extraordinary events.
KEY WORDS Forecasting Accuracy Judgemental forecasting
Most organizations have the need to develop short term forecasts. To mention a few applications, manufacturing companies forecast sales for inventory and manufacturing scheduling, banks forecast deposit and withdrawal rates and government organizations forecast their spending rate. To provide assistance in short term forecasting, researchers have developed a number of alternative techniques to statistically analyse a time series and project it forward on the basis of its past data. Growing out of the early and still widely used technique of moving average forecasting, exponential smoothing (Brown, 1963) represented an advance which provided better forecasts with less computation. In 1970 Box and Jenkins (see revised edition, 1976) published their new technique which provided a means to tailor an individual model for each time series. Several developments followed the Box-Jenkins approach including , , Bayesian forecasting . These provided the model tailoring aspects of Box and Jenkins but with less human modelling expertise required and less computational work. An additional significant advantage is their adaptive capability which updates the model parameters or the model itself on the basis of the forecast error after each new observation is received.
The focus of this paper is on the practical application of quantitative forecasting techniques, and some of the major problems that attend their use. After a brief review of the extent of use of forecasting models this paper explores the important issue of forecast accuracy, and then touches briefly on some aspects of forecast user/forecasting system interface.