Streamlining Diagnostics of Electrical-Connection-Related Errors in General Assembly Using Augmented Reality Wearables
Name
salata-easalata-mba-mgt-2025-thesis.pdf
Description
Thesis PDF
Size
20.5 MB
Format
Adobe PDF
Checksum (MD5)
aae588b5441dd873f87ebf24378f0aea
Author(s)
Salata, Elizabeth
Advisor(s)
Roemer, Thomas
Gray, Martha
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Electrical connection errors arise frequently during manufacturing. It is optimal to repair these errors during General Assembly Trim Line stations when the wiring harnesses are still exposed and easily accessible. However, the time required to locate the cause of the errors often exceeds Trim station cycle times, so most repairs are delayed until after General Assembly. Due to the implications of shutting down the line, this results in significantly higher repair times, scrap costs, and resources. To overcome these challenges, there is clear evidence supporting the use of Augmented Reality (AR) tools to innovate and streamline manufacturing processes. This master's thesis identified deficiencies in the current standard operating procedure for addressing errors and used a human-centered design approach to develop a novel error diagnostic process using an AR overlay technique to pin point on the vehicle where the problem lies. This thesis also conducted an experiment to assess the performance, success rate, and perceived cognitive load of the two processes. The data collected from the experiment provided sufficient evidence that the diagnostic process developed for this thesis reduces the elapsed time to locate the connection error by 75% with a statistically significant reduction in overall perceived cognitive load. The likelihood of widespread adoption of the AR overlay process was assessed from an estimate of further AR hardware development, safety considerations in automotive manufacturing environments, and the level of enthusiasm of all stakeholders who were consulted for this research project.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
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