Yu Hu
School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China
Wen-jie Li
School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin, 300384, China
ABSTRACT
By analyzing the demand for iron and steel enterprise group differences, this study presents the basic ideas to achieve demand forecast and the key of the ideas is to identify the various groups affiliated enterprises demand forecasting modeling and implementation. Introduction of applying stochastic processes of non-determine seasonal ARIMA model is to forecast the demand for the group subsidiary companys products or market segments. In the model solution process, due to capacity constraints of iron and steel enterprises, the use of a certain confidence interval is solved. By testing and verifying the Iron and Steel Groups sales data, we know using seasonal ARIMA model for a particular area of a product demand forecasting modeling and forecasting is feasible when we deal with non-stationary time series for the steel and iron industry requests.
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How to cite this article
Yu Hu and Wen-jie Li, 2013. Construction and Implementation of Demand Forecasting Management model of
Group Steel Enterprise. Information Technology Journal, 12: 5447-5453.
DOI: 10.3923/itj.2013.5447.5453
URL: https://scialert.net/abstract/?doi=itj.2013.5447.5453
DOI: 10.3923/itj.2013.5447.5453
URL: https://scialert.net/abstract/?doi=itj.2013.5447.5453
REFERENCES
- Dickey, D.A. and W.A. Fuller, 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49: 1057-1072.
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