Predictive Quality Analytics Dissertation 2021.pdf (2.18 MB)
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Predictive Quality Analytics

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thesis
posted on 03.01.2022, 01:10 by Salim A SemssarSalim A Semssar
Quality drives customer satisfaction, improved business performance, and safer products. Reducing waste and variation is critical to the financial success of organizations. Today, it is common to see Lean and Six Sigma used as the two main strategies in improving Quality. As advancements in information technologies enable the use of big data, defect reduction and continuous improvement philosophies will benefit and even prosper. Predictive Quality Analytics (PQA) is a framework where risk assessment and Machine Learning technology can help detect anomalies in the entire ecosystem, and not just in the manufacturing facility. PQA serves as an early warning system that directs resources to where help and mitigation actions are most needed. In a world where limited resources are the norm, focused actions on the significant few defect drivers can be the difference between success and failure

History

Degree Type

Doctor of Technology

Department

Technology Leadership and Innovation

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Jon Padfield

Additional Committee Member 2

Linda Naimi

Additional Committee Member 3

Kathryn Newton

Additional Committee Member 4

Michael Dyrenfurth