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Predictive 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

  1. 10.25394/pgs.17158370.v1
Identiferoai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17158370
Date03 January 2022
CreatorsSalim A Semssar (11823407)
Source SetsPurdue University
Detected LanguageEnglish
TypeText, Thesis
RightsCC BY 4.0
Relationhttps://figshare.com/articles/thesis/Predictive_Quality_Analytics/17158370

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