With advances in Industry 4.0 and the growing availability of low-cost sensors, high-frequency data collection has become more accessible for manufacturing industries. These technologies enable the creation of digital shadows (DSs) to visualize and monitor the production processes. In this context, the DS captures the physical manufacturing process in a virtual environment, enhancing process monitoring and providing a dynamic, interactive, and unidirectional flow of data from the real world to the virtual model for performance analysis and maintenance planning. This article presents a modular approach by establishing foundational methods for transitioning towards DS by creating a digital model of a semiautomatic process of polyvinyl chloride (PVC) welding in a manufacturing environment. Utilizing Industrial Internet of Things–enabled sensing systems, the proposed framework follows a modular approach to develop a sensor architecture and demonstrates real-time data acquisition within the production line. The captured operational data, reflecting the operational status of the equipment, can be called the early-stage process DS. These data are then processed into the information-knowledge-modeling structure through live dashboards and analytics and further integrated into simulations, laying the groundwork for a future digital twin (DT). The implementation combines cloud-based visualization platforms with open-source Python web frameworks for data analytics, ensuring cost-effectiveness and ease of deployment. Real-time monitoring will support performance evaluation and machine metrics tracking, addressing the complexities of high-mix, high-volume production. This data-driven foundation establishes a clear pathway for the evolution from DS to DT in PVC welding manufacturing.