The advent of Industry 4.0 has revolutionized the manufacturing landscape, introducing advanced technologies such as the Internet of Things (IoT) to optimize production processes. This book chapter proposes a comprehensive digital twin framework that harnesses the power of IoT technologies, specifically sensor data, to enhance efficiency and performance in process industries. Our proposed framework is designed to leverage real-time sensor data from bioprocesses and combine it with manually collected data to create a virtual representation of the real process. This envisioned digital twin not only mirrors the current state of the system but also enables predictive analysis and proactive decision-making. We discuss how using the notion of process, as defined in the business process management area, enables the integration of various components of bioprocesses and how IoT-enabled technologies can create a real-time connection between the physical and virtual processes. We also discuss the challenges of realizing the proposed digital twin and offer potential solutions to those challenges. We demonstrate the applicability of the proposed framework via a case study with a large pharmaceutical company. In particular, in the context of predictive process monitoring, we show the current baseline can be outperformed if our framework is adopted.