Digital Thread Implementation Using ML for Traceability in Smart Factories

Abstract

The transition to Industry 4.0 has catalyzed the development of smart factories that leverage advanced technologies for enhanced operational efficiency, agility, and quality. Within this landscape, traceability across the entire product lifecycle has become a critical requirement, particularly in highly regulated or quality-sensitive industries. The digital thread a data-driven framework that links information across the product's lifecycle offers a promising solution to achieve end-to-end visibility and traceability. However, the scale, complexity, and heterogeneity of industrial data pose significant challenges to implementing a fully functional digital thread. This paper proposes an integrated approach for implementing the digital thread in smart factories using Machine Learning (ML) techniques. By exploiting ML's ability to handle high-dimensional, time-series, and multimodal data, the proposed framework enables predictive traceability, root cause analysis, and anomaly detection throughout the manufacturing process. The architecture connects data from IoT-enabled shop floors, MES, ERP, and PLM systems, creating a unified traceability framework that evolves dynamically with production changes. We also discuss the deployment of ML models within this framework and how they adapt to new data through continuous learning mechanisms. To validate the approach, we present a case study simulating traceability in a smart manufacturing line, demonstrating improved defect detection, reduced downtime, and better lifecycle visibility. The paper concludes with a discussion on challenges, such as data integration, ML model generalization, and the need for standardization across platforms, and outlines future research directions including federated learning, blockchain integration, and real-time decision-making with digital twins.