Harnessing ML and IIoT for Traceability in Continuous Production Systems: A Conceptual Framework - Abdelaal, Kholoud M (AUC Knowledge Fountain, 2026)
In the era of rapid technological advancement, the manufacturing sector faces increasing pressure to leverage emerging technologies to enhance operational efficiency and minimize waste. In this context, traceability plays a pivotal role, as it provides complete visibility of processes and products throughout manufacturing systems, enabling them to identify areas for improvement and take corrective actions accordingly. Additionally, traceability ensures compliance, supports product recalls, provides a clear understanding of the system’s performance, and enables fact-driven decision-making in multiple aspects of the manufacturing system. Although the broad spectrum of traceability applications in batch production-based plants, traceability remains challenging to achieve in continuous production systems due to their unique characteristics. This thesis seeks to integrate Industrial Internet of Things (IIoT), Machine Learning (ML), and Big Data Analytics (BDA) to establish a smart traceability system in continuous production plants. This study aims to provide a general conceptual framework for achieving traceability and overcoming traceability challenges in continuous manufacturing systems, regardless of the industry type and/or company size. The proposed framework was validated through one-to-one interviews with experts in related fields with various backgrounds. The validation resulted in a more comprehensive framework that accommodates a broader range of industrial needs. Afterward, it was verified through a case study implementation at a production plant in the Fast-Moving Consumer Goods (FMCG) industry for a baby diaper production line. Verification results showed a ~30% improvement in material recording accuracy, a 90% reduction in data entry time, and the elimination of 60 minutes of stocktaking stoppage per shift. The integration of IIoT, ML, and dashboards enabled real-time monitoring, predictive alerts, and enhanced decision-making, confirming the framework’s feasibility and effectiveness in continuous production systems.