Loading...
Please wait, while we are loading the content...
Similar Documents
A Hybrid Statistical Data Preprocessing and Data Forecasting Model on ERP Based Supply Chain Management (SCM) Databases
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sekhar, M. Sam Chalapathi, P.venkata |
| Copyright Year | 2019 |
| Abstract | As size of data increases, most of Enterprise Resource Planning (ERP) systems become automated either in standalone environment or cloud environment. These ERP systems have become more complicated and complex when the number of feature space of the Supply Chain Management (SCM) database increases. Most of the traditional ERP tools analyze the SCM data by using the standard data pre-processing and forecasting models. Also, the size of the feature space in the ERP tools is fixed and noisy. In order to overcome these issues in the traditional ERP tools, a novel filtered based forecasting model is required to improve the prediction accuracy on the large feature space and data size. In this paper, a novel statistical kernel data pre-processing based forecasting model is designed and implemented on SCM dataset. Experimental results proved that the present model has high computational forecasting accuracy compared to the traditional forecasting models. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ijssst.info/Vol-19/No-6/paper25.pdf |
| Alternate Webpage(s) | https://doi.org/10.5013/ijssst.a.19.06.25 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
