Enhancing Quality Management through Advanced Statistical Techniques

Cahyo Adi Nugroho; Janatika Putra  Perdana; Dendy  Tirtoadisuryo; Asep Ferry  Rachmat; Irwan Syah  Erlangga

doi:10.62207/vbrcgz16

Authors

Cahyo Adi Nugroho Kazian School Of Management, India Author
Janatika Putra Perdana Sekolah Tinggi Ilmu Ekonomi IEU Surabaya, East Java Author
Dendy Tirtoadisuryo Kazian School Of Management, India Author
Asep Ferry Rachmat Atlanta College of Liberal Arts and Sciences Georgia, USA Author
Irwan Syah Erlangga Universitas Islam Negeri Kiai Haji Achmad Siddiq, Jember, East Java Author

DOI:

https://doi.org/10.62207/vbrcgz16

Keywords:

Machine Learning, Quality Management, Process Optimization, High Variability Production, Dynamic Systems

Abstract

Production environments with high variability present challenges in maintaining quality consistency, which are difficult to address using traditional approaches. This research aims to evaluate the impact of machine learning (ML)-based optimization on long-term quality management in industrial sectors that experience high production fluctuations. Using a systematic literature review approach with the PRISMA method, this research analyzes 18 studies related to the implementation of ML in quality process optimization. Results show that ML significantly supports product stability, defect reduction, and sustainable operational efficiency. The implications of this research strengthen the application of ML as a relevant and effective method for improving long-term quality in dynamic production environments.

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