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Data-based description of process performance in end-to-end order processing
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AI
DOI：10.1016/j.cirp.2020.03.013.png)
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摘要
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.
Keyword:
Process
Performance
Machine learning
Process mining
期刊
C
IF:
3.6
论文数:
3.4K
被引数:
1.3W
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