TY  - JOUR
A1  - Hagedorn, Christopher
A1  - Huegle, Johannes
A1  - Schlosser, Rainer
T1  - Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning
JF  - Journal of intelligent manufacturing
N2  - In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.
KW  - Causal structure learning
KW  - Log data
KW  - Causal inference
KW  - Manufacturing
KW  - industry
Y1  - 2022
U6  - https://doi.org/10.1007/s10845-022-01952-x
SN  - 0956-5515
SN  - 1572-8145
VL  - 33
IS  - 7
SP  - 2027
EP  - 2043
PB  - Springer
CY  - Dordrecht
ER  -