A web-based ERP data mining system for decision making

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.

[1]  Lu Liu,et al.  Application of data mining in supply chain management , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[2]  Corey Salka Ending the MOLAP/ROLAP Debate: Usage Based Aggregation and Flexible HOLAP (Abstract) , 1998, ICDE 1998.

[3]  J.W.T. Lee,et al.  An ordinal framework for data mining of fuzzy rules , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[4]  Michael J. A. Berry,et al.  An Introduction to Data Mining , 2003 .

[5]  Christopher R. Westphal,et al.  Data Mining Solutions: Methods and Tools for Solving Real-World Problems , 1998 .

[6]  Alok N. Choudhary,et al.  A parallel scalable infrastructure for OLAP and data mining , 1999, Proceedings. IDEAS'99. International Database Engineering and Applications Symposium (Cat. No.PR00265).

[7]  Erick Thomsen,et al.  Microsoft? OLAP Solutions , 1999 .

[8]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[9]  Reudiger Buck-Emden,et al.  Sap R/3 System: A Client/Server Technology , 1996 .

[10]  A. Konig Interactive visualization and analysis of hierarchical neural projections for data mining , 2000 .