Integrating Artificial Intelligence into Total Quality Management in MSMEs: A Quantitative Study on Quality Enhancement and Operational Efficiency

Abstract

This study investigates the integration of Artificial Intelligence (AI) into Total Quality Management (TQM) processes within Micro, Small, and Medium Enterprises (MSMEs), focusing on how AI enhances quality and operational efficiency. MSMEs often face significant challenges in maintaining high standards in quality and efficiency due to resource constraints. This research examines AI’s impact on key TQM metrics—defect rates, operational efficiency, customer satisfaction, inventory management, and downtime reduction—across varying levels of AI adoption. A quantitative, correlational research design was employed, using data from structured surveys and operational records from MSMEs with low, moderate, and high AI integration levels. Results from ANOVA, t-tests, and correlation analyses revealed statistically significant improvements in all metrics associated with higher AI integration. Specifically, AI-driven quality control was associated with lower defect rates, improved production efficiency, and higher customer satisfaction. Furthermore, AI-enabled predictive maintenance and inventory management contributed to reduced wastage and downtime. These findings suggest that AI integration in TQM offers MSMEs strategic advantages by enhancing quality, operational resilience, and competitiveness. The study concludes that adopting AI within TQM processes is a transformative approach for MSMEs aiming for sustainable growth in a technology-driven market. Future research could explore industry- specific applications and examine long-term effects of AI adoption in MSMEs.