Predictive Quality Analytics Dissertation 2021.pdf (2.18 MB)
Download filePredictive Quality Analytics
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 PadfieldAdditional Committee Member 2
Linda NaimiAdditional Committee Member 3
Kathryn NewtonAdditional Committee Member 4
Michael DyrenfurthUsage metrics
Categories
Keywords
machine Learning Methods Enable Predictive Modelingquality assurance plansupply chain networkmanufacturingmanufacturing enterprisesindustrial organizationProcess Control and SimulationManufacturing ManagementManufacturing Safety and QualityInterdisciplinary Engineering not elsewhere classifiedEngineering PracticeManufacturing Processes and Technologies (excl. Textiles)