Process mining has matured as analysis instrument for process-oriented data in recent years. Manufacturing is a challenging domain that craves for process-oriented technologies to address digitalization challenges. We found that process mining creates high expectations, but its implementation and usage by manufacturing experts such as process supervisors and shopfloor workers remain unclear to a certain extent. Reason (1) is that even though manufacturing allows for well-structured processes, the actual workflow is rarely captured in a process model. Even if a model is available, a software for orchestrating and logging the execution is often missing. Reason (2) refers to the work reality in manufacturing: a process instance is started by a shopfloor worker who then turns to work on other things. Hence continuous monitoring of the process instances does not happen, i.e., process monitoring is merely a secondary task, and the shopfloor worker can only react to problems/errors that have already occurred. (1) and (2) motivate the goals of this study that is driven by Technical Action Research (TAR). Based on the experimental artifact TIDATE -- a lightweight process execution and mining framework -- it is studied how the correct execution of process instances can be ensured and how a data set suitable for process mining can be generated at run time in a real-world setting. Secondly, it is investigated whether and how process mining supports domain experts during process monitoring as a secondary task. The findings emphasize the importance of online conformance checking in manufacturing and show how appropriate data sets can be identified and generated.

中文翻译：

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时间和数据的作用：制造领域的在线一致性检查

近年来，过程挖掘已经成为面向过程数据的分析工具，已经成熟。制造业是一个充满挑战的领域，渴望寻求面向流程的技术来应对数字化挑战。我们发现过程挖掘创造了很高的期望，但是在一定程度上仍不清楚制造专家（例如过程主管和车间工人）的实施和使用情况。原因（1）是，即使制造允许结构合理的过程，实际的工作流程也很少被过程模型捕获。即使模型可用，也常常缺少用于编排和记录执行情况的软件。原因（2）涉及制造中的工作现实：一个流程实例是由车间工人启动的，然后该工人转而从事其他工作。因此，不会发生对流程实例的连续监视，即，流程监视仅是次要任务，并且车间工人只能对已经发生的问题/错误做出反应。（1）和（2）激发了由技术行动研究（TAR）驱动的本研究的目标。基于实验工件TIDATE（轻量级的流程执行和挖掘框架），研究了如何确保流程实例的正确执行以及如何在现实世界中的运行时生成适合于流程挖掘的数据集。环境。其次，调查了过程挖掘是否将过程挖掘作为辅助任务，以及在过程监视过程中如何为领域专家提供支持。