Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology

• Published in: IEEE access Vol. 12; pp. 80266 - 80282
• Main Authors: Koval, Leonid, Knollmeyer, Simon, Mathias, Selvine G., Asif, Saara, Uzair Akmal, Muhammad, Grossmann, Daniel, Bregulla, Markus
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Data integration; Data mining; Data-driven decision making; Decision making; Deep learning; deep learning algorithms; Fourth Industrial Revolution; industry 4.0 technologies; information modeling (IM); Knowledge representation; Literature reviews; Machine learning; machine learning in manufacturing; Manufacturing; Metadata; Modelling; Neural networks; Production; Reviews; Root cause analysis; root cause analysis (RCA); State-of-the-art reviews

Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review m...