Journal of Applied Mechanics Reviews and Reports
ISSN: 3069-0803 | DOI: 10.64030/3069-0803
Mustafa M. Mansour
In today’s data-driven industrial landscape, Big Data Analytics (BDA) is reshaping the way manufacturers approach quality management. Traditional quality control methods often react to defects after they occur, leading to inefficiencies and increased production costs. This paper proposes a comprehensive framework for implementing predictive quality management by leveraging BDA tools and technologies. The framework is structured around four critical phases: data acquisition, data integration, predictive modeling, and continuous improvement. Data acquisition involves the collection of real-time information from sensors, machines, and production environments using Industrial Internet of Things (IIoT) technologies. Integration ensures that heterogeneous data sources are standardized and processed for analysis. In the modeling phase, advanced analytics and machine learning algorithms are used to identify patterns, detect anomalies, and predict defects before they affect production. Finally, continuous feedback mechanisms allow for ongoing adjustments and refinements, creating a dynamic and adaptive quality management system. The proposed approach enhances operational efficiency, reduces waste, improves product reliability, and supports faster decision-making. Real-world applications show that integrating BDA into manufacturing systems not only improves quality outcomes but also contributes to overall business agility. Despite the benefits, challenges such as high implementation costs, data security, and workforce skill gaps must be addressed for successful adoption. This framework provides a practical roadmap for industries aiming to embrace predictive analytics in quality management and paves the way for smarter, more sustainable manufacturing practices.