This paper presents the development and calibration of a digital twin for a wheel loader that integrates a physical machine with a high-fidelity virtual model. The digital twin supports automated diagnostics, operational optimisation, and predictive simulations to enhance construction efficiency. Calibration using experimental data from the physical wheel loader improves the model’s accuracy, ensuring realistic replication of system mechanics. A physics-based multibody dynamics model was developed in AGX Dynamics as the core digital model. The physical loader was instrumented with pressure transducers on hydraulic cylinders, load pins to measure bucket forces in excavation, a quadrature encoder for linear displacement, and an inclinometer for bucket orientation. Data from actual operations were used to calibrate the digital model so that simulated excavation forces matched experimental measurements. Results show that while the uncalibrated model accurately predicts bucket loads before excavation, significant deviations occur during soil interactions. Calibration effectively mitigates these discrepancies, yielding a validated digital twin capable of accurate excavation-force prediction and reliable performance analysis.