AI-Powered Analytics for Sustainable Quality Enhancement: Re-positioning Statistical Quality Control in a Dynamical Business World

Muzalwana Abdul Talib; Saber Abdelall Mohamed  Ahmed

doi:10.37934/arbms.39.1.184192

Authors

Muzalwana Abdul Talib Department of Decision Science, Faculty of Business and Economics, Universiti Malaya 50603 Kuala Lumpur, Malaysia
Saber Abdelall Mohamed Ahmed Egyptian Ministry of Education, Cairo, Egypt

DOI:

https://doi.org/10.37934/arbms.39.1.184192

Keywords:

Artificial intelligence, Statistical Quality Control, predictive analytics, quality management, real-time data, Industry 4.0, machine learning, business agility

Abstract

The modern business landscape is increasingly dynamic and data-rich, driven by the technological changes and demands for sustainability compliance. Traditional quality control approaches heavily rely on historical data and only detect problem after it has occurred. This reactive approach falls short in today’s fast-moving and data-driven markets. This paper examines the transformation of SQC into an adaptive, AI-powered analytics framework that brings machine learning, neural networks, and real-time data analytics. This integration enables predictive and prescriptive decision making hence supporting sustainable quality enhancement. The paper posits SQC as a strategic enabler of sustainable quality enhancement, contributing to the quality management literature by redefining SQC as an intelligent, analytic-driven systems. This paper offers the practical implications and emerging cases and proposes directions particularly in the ethical integration of AI and cross-department adoption. These insights then highlight the need to reposition SQC as a key driver of long-term business agility, resilience and value in the era of Industry 4.0.