Impact Evaluation and Prioritization Framework for Manufacturing Inspection Technology Investment
Name
didio-ididio-mba-mgt-2025-thesis.pdf
Description
Thesis PDF
Size
2.32 MB
Format
Adobe PDF
Checksum (MD5)
bdb055cefc5d1941a7505c513880adaa
Author(s)
DiDio, Isabella
Advisor(s)
Zheng, Karen
Simchi-Levi, David
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Advancements in visual inspection technologies and machine learning algorithms present Johnson & Johnson Vision with an opportunity to enhance quality control for Acuvue contact lenses, addressing inefficiencies such as unnecessary scrap, customer complaints, and lead time variability. With over 5 billion lenses produced annually across 100 manufacturing lines, the proposed inspection implementation of advanced camera optics and machine learning aims to improve defect detection accuracy, minimize manual inspection, and reduce customer complaints.
An impact evaluation and prioritization framework was developed to strategically implement these upgrades across 100 manufacturing lines, integrating historical data analysis, financial modeling, and engineering risk assessments. Key findings highlight that complaint reduction, scrap savings, and labor cost reductions are the primary drivers of cost savings, with inventory savings offering incremental benefits over time.
In conclusion, this research demonstrates the process of integrating advanced technologies into manufacturing processes. By aligning engineering solutions with strategic business objectives, the findings provide actionable insights for managing large-scale technological upgrades across global networks.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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