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You are here: Home / Member News / QPRC to Focus on Advancing Industrial Statistics for Complex Systems

QPRC to Focus on Advancing Industrial Statistics for Complex Systems

May 1, 2026 1 Comment


The 2026 Quality and Productivity Research Conference will be held at the Hilton Santa Fe Historic Plaza in Santa Fe, New Mexico, from June 22–25. The mission of QPRC is to improve the quality of products and services and the productivity of industries through advances in statistical methods. The theme of QPRC 2026 is “Advancing Industrial Statistics for Complex Systems.”

A full-day short course, “Modern Topics in Screening Designs,” will be presented by Christopher Nachtsheim on June 22. The technical program, offered June 23–25, will emphasize statistical methodologies and creative problem-solving approaches to addressing scientific, industrial, and business challenges.

QPRC brings researchers and practitioners together to foster interdisciplinary research and develop innovative solutions to practical problems. Student involvement is valued and encouraged; therefore, student support is available, including QPRC student scholarships and the Mary G. and Joseph Natrella Scholarship. 

QPRC is sponsored by the ASA Section on Quality and Productivity and organized in partnership with Los Alamos National Laboratory.

If you are interested in providing local support or giving a talk, please contact one of the committee members. Questions about the conference can be emailed to Abby Nachtsheim.


Filed Under: Member News, Quality and Productivity, Section News Tagged With: industries, Los Alamos National Laboratory, Mary G. and Joseph Natrella scholarship, New Mexico, QPRC 2026, Santa Fe

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  1. Viska Clanella Nugroho says

    July 12, 2026 at 2:58 pm

    What impressed me most is that the conference presents statistics as a strategic driver of industrial innovation rather than merely a mathematical discipline. Advanced statistical methods help organizations improve product quality, optimize processes, reduce variability, and support evidence-based decision-making across increasingly complex industrial systems

    Reply

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