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Aziz-Khairulfazi, R
Languages: English
Types: Doctoral thesis
Subjects:
Since way back in the 1970's, academic research has attempted to improve forecasting practice. Unfortunately, this process is still incomplete and ongoing.\ud There has been little research in the forecasting area in incorporating forecasting techniques, systems and administration (Mentzer and Kahn, 1997). Several other\ud problems have been identified upon reviewing the literature, such as the limited use of modern forecasting practices (Flores and Duran, 1998). In particular, quantitative methods were slow to gain acceptance in practice, whilst forecasting techniques are\ud little known and little used. The purpose of our investigation is to find out if the findings from various related studies persist or have changed over time and whether there are any other factors\ud involved in forecasting that should be highlighted in the current practice. The motivation for this research is to bridge the gap between theory and practice in order\ud to improve forecasting practices and enhance the quality of forecasts. We carry out four types of study, namely pilot study, postal survey, case study and follow-up survey, and we base our work on several related publications\ud (Wheelwright and Clarke, 1976; Sparkes and McHugh, 1984; Dalrymple, 1987; Winklhofer et al, 1996; Mahmoud et #/., 1992). We explain our findings using statistical analysis and we offer different ways of explaining the behaviour patterns of the factors involved in forecasting. We demonstrate links between particular variables by offering basic mathematical functions to describe the effects of changes and by presenting an application of methods for improving point predictions and\ud cost functions (Goodwin, 1996 and Percy, 1993).\ud This research contributes to the present literature on forecasting in which behavioural issues have not been as thoroughly explored as have quantitative\ud methods. It is intended that our findings will strengthen principles and enhance strategies within organisations towards achieving the ultimate goal of accurate\ud forecasting.
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