Enabling Proactive Quality in Commercial Airplanes using Natural Language Processing
Name
allinson-caallins-mba-mgt-2022-thesis[1].pdf
Description
Thesis PDF
Size
1.75 MB
Format
Adobe PDF
Checksum (MD5)
c34d1b3207b169d8ed86aad9b5ec14b6
Author(s)
Allinson, Christian
Advisor(s)
Boning, Duane
Spear, Steven
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Quality management systems traditionally draw insight from structured, often numerical, sources of data; unstructured, free-text representations of quality data are less frequently employed despite having high informational value, and often require additional human effort to prepare their contents for use. An ability to extract and proactively employ this information enables a richer analysis of quality performance.
The primarily free-text reports generated by Boeing Commercial Airplane's "in-service investigation" (ISI) process are taken as an example of such quality data. We investigate both an unsupervised clustering method and a supervised classification method to group these reports by the broader "quality topic" they pertain to, using semantic relationship-maintaining text "embeddings" as features. We find success in supervised classification, and describe a method to relate ISI records with quality records from other parts of the commercial airplane value stream via standardized "code" metadata.
We extend the use of similarity techniques to investigation execution and propose a "helper" tool that automates parts of the manual data collection and relationship-finding process. The benefits of using such a tool over traditional keyword searches are described through an illustrated example.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
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