AI assisted Image recognition for EEE parts kitting and auditing
Description
Due to the limits in personnel capacity, warehouse inventory contract (TRAX), and the limits in the Goddard Material Management System (MMS), EEE parts kitting and auditing is labor intensive, and error prone. This project aimed to utilize the image recognition potential to help reduce partial man-hour involved in the kitting and auditing process.
Detailed example
A parts kitting list in spreadsheet format, against released BOMs, including the Part Number, Quantity, Manufacture, Date code/lot, batch data.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The outcome of this project will benefit the Goddard EEE parts engineer, the Printed Wiring Assembly card lead, the EEE parts technician, the vendor who receives the kit, and the Quality Assurance (QA). The measurements KPI include: 1) The man-hour saved in generating the correct parts kitting list with all required information; 2) The accuracy of with the assistance from AI, comparing the old method. The overall benefit should consider a combination of both factors, not just focused on accuracy improvement.
Controls / human review
ATO: Not reported; PIA: Not published