标题： 过程挖掘与序列聚类在识别工业问题中的应用 显示英文标题

标题： Application of Process Mining and Sequence Clustering in Recognizing an Industrial Issue

摘要： 流程挖掘已经成为可以以可视化细节概述生产过程事件日志的最佳程序之一。 我们已经解决了在工业过程中容易出现的重要问题，即瓶颈问题。 分析过程专注于提取生产线中的瓶颈，以提高生产流程。 在有足够的存储历史日志的情况下，流程挖掘领域可以通过减轻生产流中的瓶颈来提供优化生产流程的合适答案。 流程挖掘通过挖掘事件日志来诊断生产率过程，这有助于揭示优化关键生产过程的机会。 我们发现由于编织活动的存在，过程中存在相当大的瓶颈。 通过与专家的讨论，一致认为编织过程中的主要问题是，特别是那些在过载过程中耗尽的机器。 系统的改进通过团队合作进行了测量；流程的周期时间提高了91%，工人的绩效提高了96%，产品质量提高了85%，交货时间从几天和几周优化到了几小时。 显示英文摘要

摘要： Process mining has become one of the best programs that can outline the event logs of production processes in visualized detail. We have addressed the important problem that easily occurs in the industrial process called Bottleneck. The analysis process was focused on extracting the bottlenecks in the production line to improve the flow of production. Given enough stored history logs, the field of process mining can provide a suitable answer to optimize production flow by mitigating bottlenecks in the production stream. Process mining diagnoses the productivity processes by mining event logs, this can help to expose the opportunities to optimize critical production processes. We found that there is a considerable bottleneck in the process because of the weaving activities. Through discussions with specialists, it was agreed that the main problem in the weaving processes, especially machines that were exhausted in overloading processes. The improvement in the system has measured by teamwork; the cycle time for process has improved to 91%, the worker's performance has improved to 96%,product quality has improved by 85%, and lead time has optimized from days and weeks to hours.