A web-based ERP data mining system for decision making. (1st January 2003)

• Record Type: Journal Article
• Authors: Chen, Ruey-Shun; Chen, C.C.; Chang, C.C.
• Is Part Of: International journal of computer applications technology. Volume 17:Number 3(2003)
• Page Start: 156
• Page End: 169
• Publication Date: 2003-01-01
• Subjects: data mart -- data mining -- data warehouse -- Decision Tree -- enterprise resource planning (ERP) -- OLAP; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

Most of the enterprises having finished, one after the other, in the automation of transaction processing, the key issue for the future competitiveness of enterprises will be the automation of decision-making processing and forecast processing. To complement the deficiency of the forecast flow in enterprise resource planning (ERP) systems, we propose here a data searching architecture that can be built on the ERP system while meeting the individual needs of enterprises and providing real time accurate forecasts on changes in the future market. The present paper is based on the transaction flow processing power of ERP. It integrates the database and online analytical processing (OLAP) technologies, transaction and decision making flows of ERP, then uses data searching technology to integrate decision making and forecast flows. The data-searching engine is the actual design of a categorising module. This research can finish within an hour the analyses and forecasts, which would take the traditional information technology system about three weeks to process the mock analyses. This makes decision making easier for entrepreneurs and thus enables them to handle well the always-changing market and establish themselves firmly. This research applies the ERP data searching system on the IC testing industry. Using the ERP data searching system in this study to investigate the testing status of the testing machine, resolutions are found pinpointing the factors that cause the high frequency of machine down. The results of, according to the actual experiment, the tested testing machine shows an average improvement rate of 83% over the number of machines down.