Integrating Data Science into Total Quality Management and its Effects on Enterprises
Mohammad Berrish1, Khaled Abuain2, Abdulbasit Khashkhush3
1Dr Mohammad Berrish, Department of Clinical Skills. Faculty of Medicine, University of Tripoli, Tripoli, Libya.
2Dr Khaled Abuain, Department of Community, Faculty of Medicine, University of Tripoli, Tripoli, Libya.
3Abdulbasit Khashkhush, Department of Computer, Faculty of Science, University of Tripoli, Tripoli, Libya.
Manuscript received on 25 July 2026 | First Revised Manuscript received on 04 August 2026 | Second Revised Manuscript received on 11 August 2026 | Manuscript Accepted on 15 August 2026 | Manuscript published on 30 August 2026 | PP: 21-28 | Volume-12 Issue-12, August 2026 | Retrieval Number: 100.1/ijmh.A189913010926 | DOI: 10.35940/ijmh.A1899.12120826
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Data Science, Total Quality Management, Enterprises
Scope of the Article: Social Sciences