.png){kind=icon}
.png){kind=icon}
Return
Data-based description of process performance in end-to-end order processing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cirp.2020.03.013.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To master ongoing market competitiveness, manufacturing companies try to increase process efficiency through process improvements. Mapping the end-to-end order processing is particularly important, as one needs to consider all order-fulfilling core processes to evaluate process performance. However, today's traditional process mapping methods such as workshops are subjective and time-consuming. Therefore, process improvements are based on gut feeling rather than facts, leading to high failure probabilities. This paper presents a process mining approach that provides data-based description of process performance in order processing and thus objectively and effortlessly maps as-is end-to-end processes. The approach is validated with an industrial case study. (C) 2020 CIRP. Published by Elsevier Ltd. All rights reserved.
Keywords:
Process
Performance
Machine learning
Process mining
Journal
C
IF:
3.6
Papers:
3.4K
Citations:
1.3W