Digital twin technology is transforming wood production lines in 2026 by creating virtual replicas of physical processes that enable simulation, predictive maintenance, and real-time process optimization. This technical white paper examines the implementation of digital twins in woodworking manufacturing, analyzing their architecture, benefits, and integration challenges. We define a digital twin as a dynamic, data-driven virtual model that mirrors a physical system, receiving real-time data from sensors and updating its behavior to reflect actual conditions. In the context of wood production lines, digital twins can simulate everything from raw log processing to finished panel output. The paper begins by describing the technological stack: IoT sensors (vibration, temperature, pressure, acoustic emissions) for data acquisition, edge computing for preprocessing, cloud-based storage and analytics, and the simulation engine (often based on finite element analysis or discrete event simulation). We present a detailed case study of a plywood manufacturing plant that implemented a digital twin to optimize its drying process: by simulating airflow and temperature profiles, the plant reduced drying time by 15% while improving moisture uniformity. Another case study covers a CNC routing cell where digital twin-based predictive maintenance reduced unplanned downtime by 40% by detecting tool wear patterns and scheduling interventions proactively. The paper provides technical specifications for building a digital twin: data modeling (using standardized formats like OPC UA), system integration (with MES and ERP), and user interfaces (dashboards for operators and managers). We also address the advanced analytics layer, using machine learning to identify anomalies, predict failures, and recommend process adjustments. Challenges such as data quality, model validation, and cybersecurity are discussed, with mitigation strategies. The paper concludes with a strategic roadmap for woodworking businesses considering digital twin adoption, including cost estimation, ROI analysis, skill development, and a phased implementation plan. We also explore future developments: the integration of AI agents that can autonomously control production parameters based on twin simulations, and the use of augmented reality to visualize digital twins in the physical workshop.