Simulation-based learning plays a crucial role in robotics education by allowing students to practice complex tasks safely and cost-effectively. Digital twins—virtual replicas of physical robots—foster a deeper understanding of kinematics, control, and automation, yet many open-source simulators lack the reliability and precision necessary for consistent educational use. Commercial platforms like Festo CIROS offer advanced multi-body modeling and inverse-kinematics capa-bilities, but no digital twin exists for the Niryo Ned2 collaborative robot. This study addresses this gap by developing a fully functional digital twin of the Niryo Ned2 in CIROS, including peripheral devices such as a conveyor, object feeder, ultrasonic sensor, and sorting bins. A MELFA Basic program implements color-based pick-and-place operations using inverse-kinematics motion and by integrat-ing digital inputs and outputs. The resulting simulation provides a realistic, in-teractive environment that mirrors physical robot behavior, enabling hands-on experience in programming, sensor integration, and automation workflows with-out physical hardware. The work demonstrates the potential of CIROS as a robust educational platform for digital twins and lays the foundation for future en-hancements, including vision-based and AI-driven automation training.