Data-Driven Decision Making: Enhancing Quality Management Practices Through Optimized MIS Frameworks

Abstract

This study explores the transformative role of data-driven decision-making (DDDM) and advanced Management Information Systems (MIS) in enhancing quality management practices across industries. By leveraging data analytics, predictive tools, and emerging technologies, organizations can achieve superior decision-making accuracy, operational efficiency, and customer satisfaction. A systematic review of 52 peer-reviewed articles, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, provides a comprehensive analysis of the adoption trends, technological advancements, challenges, and industry-specific applications of DDDM in quality management. The findings reveal that DDDM frameworks significantly outperform traditional quality management methods, offering enhanced adaptability and real-time responsiveness to complex challenges. Key insights include the pivotal role of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and blockchain technologies in transforming MIS capabilities, as well as the persistent barriers posed by organizational resistance, legacy system limitations, and ethical concerns. Despite these challenges, evidence from the reviewed articles underscores the superiority of DDDM in achieving quality excellence, making it an indispensable approach for organizations aiming to thrive in a data-driven business environment.