Time series methods base their forecasts on extrapolations from past patterns and inter-relationships. Consequently, they work well only when the future is similar to the past or when changes (by chance) happen to cancel out; they are also quite handicapped when it comes to the consideration of environmental factors. In a turbulent environment with high uncertainty; the need for accurate forecasts is paramount. Hence, this paper proposes a design-approach, which incorporates the principles of Design of Experiments (DOE) into the real-time forecasting model, such as causal methods, to minimise the standard error. DOE is employed to select the most significant factors while forming the causal equation and further to perfect the coefficients of these factors. A pilot study data has been used to validate the proposed model.
|Original language||English (US)|
|Number of pages||18|
|Journal||International Journal of Industrial and Systems Engineering|
|State||Published - 2007|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Industrial and Manufacturing Engineering