This paper investigates the effect of inverse kinemat-ics (IK) on operator performance during the telemanipulation of an industrial robot. Robotic teleoperation is often preferred when manipulating objects in extreme conditions. In many applications, e.g., hazardous and high-consequence environments, operators cannot directly perceive the robot motions and have to rely only on CCTV views of the scene for situational awareness while teleoperating the heavy-duty industrial robots. Making best guesses for the IK plays a significant role on the task success rate and increases the operator cognitive load significantly. In this context, we develop a new optimisation-based IK solver that is robust with respect to the robot's singularities and assists the operator in generating smooth trajectories. Inspired by a successful algorithm used in computer graphics to solve the IK problem and devise smooth movements (FABRIK), our algorithm takes advantage also of the kinematic structure of the robot in order to decouple the notoriously difficult IK problem of orientation and position. To evaluate the effectiveness of the proposed method, we have compared its performance to that of the commonly used Jacobian pseudo inverse-based method in terms of positional accuracy and task-space reachability. We also report the results of telemanipulation experiments with human test-subjects. Our proposed IK algorithm outperforms classical IK methods on both objective metrics of task success, and subjective metrics of operator preference.