Resilience in production environments is defined as the system’s ability to avoid, adapt, and rapidly recover from disruptive events, attaining normal operational conditions with minimal time and financial investment. Current methods lack the capability to quantify risks based on real-time data from production devices, creating a critical gap. This article presents a novel method aimed at enhancing resilience by integrating data from micro and meso levels within precision machining production plants. By utilizing real-time monitoring data, from machines and production processes, the proposed approach is based on the assessment of risks of failure of machines and early detection of anomalies in machining processes, to support decision-making. Leveraging the Industrial Internet of Things (IIoT), the method collects and analyses real-time machines and machining process data for risk assessment and early anomaly detection. The proposed method minimizes the impact of unexpected failures by incorporating failure risk calculations into plant-level decision-making. Validation of the method was conducted using a milling machine in a controlled laboratory setting, demonstrating the significance of real-time data and health assessment in developing resilient industrial systems. Preliminary results indicate strong potential for industrial application, with the next step being implemented in a production plant.