الفهرس 
AbstractOnly 14 pages are availabe for public view
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Abstract
Enterprise Resource Planning (ERP) systems became an essential asset for manufacturing and commercial enterprises to integrate enterprise departments managing day-to-day transactions and provide reports which support decision making. In addition to operational reports, advanced ERP systems provide business intelligence features of analytical and sophisticated reports based on data warehousing and online analytical processing (OLAP).
ERP systems commonly gather data from all departments of an enterprise which makes it a rich source of data to apply data mining. In order to avoid reaching the point of “Data Rich, Poor information”, applying data mining tasks and methods can benefit large amounts of data to discover previously unknown patterns and knowledge that are useful to decision makers. Integrating data mining with ERP systems can analyze historical data in order to understand the present, and predict future events of the business.
However, there are a lot of data mining techniques available, which makes choosing the right data mining technique is one of the most difficult data mining tasks, The aim of this research is to define the applications of data mining in ERP systems and how ERP users can benefit by applying data mining techniques on their ERP systems and ends up by creating an open framework that can benefit data mining techniques in ERP systems.
The research is conducted by studying standard ERP modules and features and data mining process, tasks and methods in order to identify the applicability and benefits of data mining in each module and the corresponding techniques. Then we conduct a practical case-study to integrate data mining with a traditional ERP system in order to satisfy business requirements of a given customer.