Understanding Failure Mode Effect Analysis Data Using Interactive Visual Analytics

• Published in: IEEE computer graphics and applications Vol. 39; no. 6; pp. 17 - 26
• Main Authors: Basole, Rahul C., Qamar, Ahsan, Pal, Biswajyoti, Corral, Michael, Meinhart, Matthew, Narechania, Arpit, Potel, Mike
• Format: Magazine Article
• Language: English
• Published: United States IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytics; Automobile industry; Automotive engineering; Complex systems; Data analysis; Decision analysis; Decision making; Domains; Failure analysis; Failure modes; Questions; System effectiveness; Systems engineering; Vehicle dynamics; Visual analytics
• ISSN: 0272-1716; 1558-1756
• DOI: 10.1109/MCG.2019.2944230

Providing actionable insights through interactive visual analytics is essential to effective decision making. Yet, many complex systems engineering (SE) domains still lack such tools. Design reviews are often still based on static snapshots of data, without any dynamic interaction, data curation, and view creation capabilities to answer salient analysis questions. In this study, we report on a tool called DataHawk that helps answer common questions associated with one prominent SE context, namely failure mode and effect analysis (FMEA). The tool provides powerful exploration capabilities that enable system engineers, designers, and managers to probe FMEA data from multiple starting points, build questions dynamically, and find triangulated answers using multiple views rapidly. Field results are illustrated through a usage scenario from the automotive industry and show that the tool demonstrates the needed versatility, scalability, and effectiveness for real-world engineering data.