# Overlap in Automatic Root Cause Analysis in Manufacturing: An Information Theory-Based Approach  
[PERSON]  
1 Instituto de Ciencia de Inovacao en Engenharia Mecanica e Engenharia Industrial (INEGI), Associate Laboratory for Energy, Transports and Aerospace (LAETA), Campus da FEUP, R. Dr. [PERSON] 400, 4200-465 Porto, Portugal 1  
[PERSON]  
2 Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciencia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Campus da FEUP, R. [PERSON] 400, 4200-465 Porto, Portugal 2  
[PERSON]  
2 Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciencia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Campus da FEUP, R. [PERSON] 400, 4200-465 Porto, Portugal 2  
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Footnote 7: email: {https://www. between traditional RCA methods and automated solutions. More recently, efforts have been made to develop automated solutions for root cause analysis that incorporate notions of causality instead of relying only on correlations. References [20; 21; 22] are examples of such works. As described in [23], root causes can be understood at three different levels, depending on the types of data available: (i) the root cause's location, (ii) the physical characteristics of the root cause, and (iii) the human/organizational aspects of the root cause. The first level portrays the location of the root cause in the manufacturing process. For example, [24; 25] are studies that use this type of data. The second level focuses on what physical attributes are the root cause (e.g., a sudden increase in voltages or high temperature). Examples of studies using this type of data are [26; 27; 28]. The third and final level centers on the human and organizational aspects of the root cause (e.g., equipment maintenance), so as to identify what triggered the physical problem. References [29; 30] are examples of studies exploring this final level of root causes. In this paper, we use the first level of data and try to find the location of the root cause. We choose this type of data as it is the most available one, and solutions based on it can be advantageous to more factories. Analyzing location-type data is still relevant, despite being just the first level of data. To detect the physical aspects of ARCA, factories require proper infrastructure, which some factories do not have. However, data on how the product flows through the manufacturing process are usually readily available, which enables the determination of the root causes' locations. In situations where the manufacturing process is particularly complicated (e.g., in semiconductor manufacturing [31]), even production flow data can become difficult to analyze, which justifies the use of Data Mining (DM) and Machine Learning (ML) techniques in the development of solutions that aid in diagnosis. An issue of locating the root causes is overlap, as presented in [32]. A manufacturing process can be seen as a progression of steps products go through, starting as raw materials or parts and becoming a finished product in the end.