Deming Lecture

John Eltinge Chair
United States Census Bureau (retired)
 
John Eltinge Organizer
United States Census Bureau (retired)
 
Emily Fekete Organizer
American Statistical Association
 
Tuesday, Aug 5: 4:00 PM - 5:50 PM
0493 
Invited Paper Session 
Music City Center 
Room: CC-Dean Grand Ballroom B&C 
Deming has contributed extensively to quality movement through his quality management concepts as well as popularizing Shewhart's statistical process control (SPC) techniques in Japan. Traditional quality and process control techniques have been used with much success in the manufacturing industry over the years. However, the advent of complex manufacturing processes and the availability of extensive in-situ process measurements have dictated the need for a paradigm shift.  In this presentation, I will describe a quality engineering framework for in-process quality improvement (IPQI) by integrating statistics and data science with engineering knowledge and system theory. IPQI takes full advantage of in-process sensing data to achieve process monitoring, fault diagnosis, and quality improvement. The implementation of IPQI leads to fault detection, root cause diagnosis, automatic compensation, and defect prevention. The presentation will describe the framework, methodologies, and successful applications to manufacturing processes. I will also discuss challenges and opportunities for data-rich manufacturing systems.

Keywords

Quality improvement

Deming Lecture

manufacturing processes and systems 

Applied

Yes

Main Sponsor

Deming Lectureship Committee

Presentations

From Statistical Process Control to In-Process Quality Improvement

Deming has contributed extensively to quality movement through his quality management concepts as well as popularizing Shewhart's statistical process control (SPC) techniques in Japan. Traditional quality and process control techniques have been used with much success in the manufacturing industry over the years. However, the advent of complex manufacturing processes and the availability of extensive in-situ process measurements have dictated the need for a paradigm shift. In this presentation, I will describe a quality engineering framework for in-process quality improvement (IPQI) by integrating statistics and data science with engineering knowledge and system theory. IPQI takes full advantage of in-process sensing data to achieve process monitoring, fault diagnosis, and quality improvement. The implementation of IPQI leads to fault detection, root cause diagnosis, automatic compensation, and defect prevention. The presentation will describe the framework, methodologies, and successful applications to manufacturing processes. I will also discuss challenges and opportunities for data-rich manufacturing systems.  

Speaker

Jianjun Shi, Georgia Institute of Technology