Unveiling the causes of waiting time in business processes from event logs

IF 3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Katsiaryna Lashkevich, Fredrik Milani, David Chapela-Campa, Ihar Suvorau, Marlon Dumas
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引用次数: 0

Abstract

Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times in activity transitions can help analysts identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose observed waiting times in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the process cycle time efficiency. The approach is implemented as a software tool called Kronos that process analysts can use to upload event logs and obtain analysis results of waiting time causes. The proposed approach was empirically evaluated using synthetic event logs to verify its ability to discover different direct causes of waiting times. The applicability of the approach is demonstrated in a real-life process. Interviews with process mining experts confirm that Kronos is useful and easy to use for identifying improvement opportunities related to waiting times.

从事件日志中揭示业务流程等待时间的原因
业务流程中的等待时间往往出现在个案从一项活动过渡到另一项活动时。因此,分析活动转换中等待时间的原因可以帮助分析人员确定缩短流程周期时间的机会。本文提出了一种流程挖掘方法,可将每个活动转换中观察到的等待时间分解为多个直接原因,并分析每个已识别原因对流程周期时间效率的影响。该方法以名为 Kronos 的软件工具的形式实施,流程分析师可使用该工具上传事件日志并获取等待时间原因的分析结果。我们使用合成事件日志对所提出的方法进行了实证评估,以验证其发现造成等待时间的不同直接原因的能力。该方法的适用性在实际流程中得到了验证。与流程挖掘专家的访谈证实,Kronos 在确定与等待时间相关的改进机会方面非常有用且易于使用。
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来源期刊
Information Systems
Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
2.70%
发文量
112
审稿时长
53 days
期刊介绍: Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.
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