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

Hamza Saad
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Abstract

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.
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过程挖掘和序列聚类在工业问题识别中的应用
过程挖掘已经成为最好的程序之一,它可以以可视化的细节勾勒出生产过程的事件日志。我们解决了工业生产过程中容易出现的重要问题——瓶颈。分析过程的重点是找出生产线上的瓶颈,以改善生产流程。给定足够的存储历史日志,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的合适答案。流程挖掘通过挖掘事件日志来诊断生产力流程,这有助于暴露优化关键生产流程的机会。我们发现,由于编织活动,在这个过程中存在相当大的瓶颈。通过与专家的讨论,大家一致认为织造工艺的主要问题,特别是机器在超载过程中筋疲力尽。系统的改进是通过团队合作来衡量的;工艺周期提高到91%,工人的工作效率提高到96%,产品质量提高了85%,交货时间从几天、几周优化到几小时。
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