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By: Dominic D. Nwagbaraocha

The risk of having a recall in trade or a consumer complaint which greatly affects our market share, should be avoided/eliminated. Think of a production line where before a defective product (no code, low weight) comes out of the line, you get an alarm or product is taking of the line.



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Building a predictive quality system;

  • Quality team skill upgrade: Team needs to take some machine learning or artificial intelligence courses.
  • The use of digital system and the readiness to invest (this should be driven by cost benefit ratio from cost of quality point of view).



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Based on historical defects and all its related process and production data, a defect prediction model can be developed in order to predict future quality defects.


Creating a predictive model for quality defects

  • Data Gathering: The process starts with historical data acquisition.
  • Data preparation:  Format and align the raw data into a unified model, remove some outliers.
  • Machine learning: Training of one or more prediction models.
  • Models’ evaluation/selection: Prediction models are evaluated and compared by using KPIs.
  • Defect Prediction: Best model is then used for the online prediction system, which feeds it with actual production data to predict defects. 


About the Author

Dominic is a process & quality consultant with over 10 years’ experience leading various continuous improvement in world-class organizations. founder of Doruem Process Services and Co-Founder to Nodal Point Engineering,  with core competence in manufacturing excellence and digital manufacturing. 

You can reach him on LinkedIn here.

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