Improving Forecasting Accuracy in the Case of Intermittent Demand Forecasting

In making forecasting, there are many kinds of data. Stationary time series data are relatively easy to make forecasting but random data are very difficult in its execution for forecasting. Intermittent data are often seen in industries. But it is rather difficult to make forecasting in general. In recent years, the needs for intermittent demand forecasting are increasing because of the constraints of strict Supply Chain Management. How to improve the forecasting accuracy is an important issue. There are many researches made on this. But there are rooms for improvement. In this paper, a new method for cumulative forecasting method is proposed. The data is cumulated and to this cumulated time series, the following method is applied to improve the forecasting accuracy. Trend removing by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is executed to the production data of X-ray image intensifier tube device and Diagnostic X-ray image processing apparatus. The forecasting result is compared with those of the non-cumulative forecasting method. The new method shows that it is useful for the forecasting of intermittent demand data. The effectiveness of this method should be examined in various cases.

@article{Takeyasu2014,
  title     = {Improving Forecasting Accuracy in the Case of Intermittent Demand Forecasting},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {5},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Daisuke Takeyasu and Asami Shitara and Kazuhiro Takeyasu},
  doi       = {10.14569/IJACSA.2014.050521},
  url       = {https://doi.org/10.14569/IJACSA.2014.050521}
}