当前位置: X-MOL 学术Comput. Ind. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Pattern-based action engine: Generating process management actions using temporal patterns of process-centric problems
Computers in Industry ( IF 10.0 ) Pub Date : 2023-09-26 , DOI: 10.1016/j.compind.2023.104020
Gyunam Park , Daniel Schuster , Wil M.P. van der Aalst

As business environments become more competitive, organizations strive to improve their business processes to reduce costs and increase quality and productivity. As process improvement traditionally embraces manual creative tasks that are time-consuming and labor-intensive, the need for automating it arises. Action-Oriented Process Mining (AOPM) aims to support automated process improvement by leveraging various process mining techniques. To that end, AOPM first monitors the presence of operational constraints, i.e., operational problems, in business processes, e.g., a high waiting time for patients to register. Next, it produces interim management actions designed to address these transient problems by analyzing the monitoring results. For instance, if an excessive waiting time persists for more than a week, the system might recommend dispatching additional resources for the upcoming week. Contrary to the mature process mining support for monitoring operational constraints, the action part is typically missing in today’s process mining tools. In this work, we propose an action engine to support the automatic generation of actions. It analyzes temporal patterns of monitoring results and produces action plans that describe the execution of management actions. We have demonstrated a use case using the data of a Dutch financial institute to evaluate the feasibility of the proposed action engine and conducted experiments to evaluate its effectiveness.



中文翻译:

基于模式的操作引擎:使用以流程为中心的问题的时间模式生成流程管理操作

随着商业环境的竞争变得更加激烈,组织努力改进其业务流程以降低成本并提高质量和生产力。由于流程改进传统上涉及耗时且劳动密集型的手动创造性任务,因此需要将其自动化。面向行动的流程挖掘(AOPM)旨在通过利用各种流程挖掘技术来支持自动化流程改进。为此,AOPM 首先监控业务流程中是否存在操作限制,即操作问题,例如患者挂号等待时间较长。接下来,它会制定临时管理措施,旨在通过分析监控结果来解决这些暂时性问题。例如,如果等待时间过长持续超过一周,系统可能会建议为下一周调度额外的资源。与监控操作约束的成熟流程挖掘支持相反,当今的流程挖掘工具通常缺少操作部分。在这项工作中,我们提出了一个动作引擎来支持动作的自动生成。它分析监控结果的时间模式并生成描述管理行动执行情况的行动计划。我们使用荷兰金融机构的数据演示了一个用例,以评估所提出的动作引擎的可行性,并进行了实验来评估其有效性。在这项工作中,我们提出了一个动作引擎来支持动作的自动生成。它分析监控结果的时间模式并生成描述管理行动执行情况的行动计划。我们使用荷兰金融机构的数据演示了一个用例,以评估所提出的动作引擎的可行性,并进行了实验来评估其有效性。在这项工作中,我们提出了一个动作引擎来支持动作的自动生成。它分析监控结果的时间模式并生成描述管理行动执行情况的行动计划。我们使用荷兰金融机构的数据演示了一个用例,以评估所提出的动作引擎的可行性,并进行了实验来评估其有效性。

更新日期:2023-10-01
down
wechat
bug