Integrating Data Science into Total Quality Management and its Effects on Enterprises
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This investigation examines the integration of Data Science (DS) into Total Quality Management (TQM) processes within enterprises, focusing on DS's impact on quality and operational efficiency. Jordanian enterprises often face considerable challenges in maintaining high standards of quality and efficiency due to limited resources. This study examines how DS affects key Total Quality Management (TQM) metrics, including defect rates, operational efficiency, customer satisfaction, inventory management, and downtime, across varying degrees of DS adoption. This study used a quantitative, correlational research approach, drawing on data from structured surveys and operational records from businesses classified as having low, moderate, or high DS integration. The ANOVA, t tests, and correlation analyses indicated significant statistical improvements across all metrics associated with higher levels of Data Science (DS) integration. Specifically, DS-driven quality control was correlated with lower defect rates, improved production efficiency, and greater customer satisfaction. Additionally, using DS in predictive maintenance and inventory management reduced waste and downtime. These results imply that incorporating DS into Total Quality Management (TQM) offers businesses strategic advantages, such as improved quality, operational resilience, and enhanced competitiveness. The study concludes that integrating DS into TQM processes is a promising approach for companies aiming for sustainable growth in a technology-focused market.
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