ERP systems contain huge amounts of data related to the actual execution of business processes. These systems have a particular way of recording activities which results in an unclear display of business processes in event logs. Several works have been conducted on ERP systems, most of them focusing on the development of new algorithms for the automatic discovery of business processes. We focused on addressing issues like, how can organizations with ERP systems apply process mining for analyzing their business processes in order to improve them. The data handling aspect of ERP systems contrasts with those of BPMS or workflow based systems, whose systematical storage of events facilitates the application of process mining techniques. CRISP-DM has emerged as the de facto standard for developing data mining and knowledge discovery projects. Successful data mining requires three families of analytical capabilities namely reporting, classification and forecasting. A data miner uses more than one analytical method to get the best results. The objective of this paper is to improve the usability and understandability of process mining techniques, by implementing CRISP-DM methodology for their application in ERP contexts, detailed in terms of specific implementation tools and step by step coordination. Our study confirms that data discovery from ERP system improves strategic and operational decision making.
翻译:企业资源规划系统包含大量与实际执行业务流程有关的数据,这些系统具有一种特别的记录活动方式,导致在事件日志中不清晰地显示业务流程。对企业资源规划系统进行了一些工作,其中多数侧重于为自动发现业务流程开发新的算法。我们侧重于解决各种问题,例如,拥有企业资源规划系统的组织如何应用流程采矿来分析其业务流程以便加以改进?企业资源规划系统的数据处理方面与业务流程管理系统或工作流程系统的数据处理方面形成对比,该系统的系统存储事件便利了流程采矿技术的应用。CRISB-DM已经成为开发数据挖掘和知识发现项目的实际标准。成功的数据挖掘需要具有报告、分类和预测等分析能力的三个组合。数据挖掘者使用不止一种分析方法取得最佳结果。本文件的目的是通过实施CRISP-DM方法,在企业资源规划系统中应用这些技术,在具体实施工具和一步步协调方面进行详细分析。我们的研究证实,企业资源规划系统的数据发现改善了战略和业务决策。