A Unified ISO 9001–Driven Framework for Integrating RFID and Predictive Analytics in Industry 4.0 Quality Systems

Abiola Olawore
POMPEA College of Business, University of New Haven, United States
DOI – http://doi.org/10.37502/IJSMR.2026.9303

Abstract

The rapid evolution of Industry 4.0 has fundamentally transformed manufacturing ecosystems through the integration of cyber-physical systems, Internet of Things (IoT) technologies, and advanced data analytics. Within this context, maintaining robust and adaptive Quality Management Systems (QMS) aligned with ISO 9001 presents both significant opportunities and complex challenges. This study proposes a unified, standards-driven framework that integrates Radio Frequency Identification (RFID) technology and predictive analytics into ISO 9001-based quality systems to enable intelligent, data-driven quality management. The framework adopts a multi-layered architecture encompassing real-time data acquisition, data integration and preprocessing, predictive analytics, and decision-support mechanisms aligned with ISO 9001 principles. RFID technologies facilitate granular, real-time tracking of materials and processes, while predictive analytics leverages machine learning models to forecast equipment failures, detect quality deviations, and optimize operational performance. This integration transforms traditional reactive quality control into a proactive, predictive paradigm that enhances process reliability and reduces operational inefficiencies. Methodologically, the study employs a systematic literature synthesis and conceptual modeling approach, complemented by a proposed validation pathway using synthetic datasets and performance evaluation metrics. The framework emphasizes critical design principles, including data quality, interoperability, scalability, and compliance with regulatory standards. The findings demonstrate that the convergence of RFID, predictive analytics, and ISO 9001 enables enhanced traceability, improved decision-making, reduced waste, and strengthened organizational resilience. This research contributes to the emerging field of Quality 4.0 by providing a comprehensive, scalable, and standards-aligned blueprint for integrating advanced technologies into modern quality management systems, with significant implications for manufacturing and other data-intensive industries.

Keywords: Industry 4.0; ISO 9001; Quality Management Systems; RFID; Predictive Analytics; Digital Transformation; Manufacturing; Process Improvement; Data Integration.

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