The use of collaborative pick-and-place robots has been gaining popularity in recent years, given their ability to work alongside humans in various industrial and manufacturing environments. The use of smart wearables as the Industrial Internet ofWearable Things (IIoWT), to facilitate efficiency of such environments is receiving vast attention. In this article, we present the development of a novel smart glove (SM-EXO) integrated with the RobotStudio simulation software for use in a multi-user pick-and-place collaborative robot-simulated environment. The proposed smart glove uses an intuitive force-feedback concept to provide users with a sense of constraint while moving their fingers, thus giving them a more organic feeling. A series of experiments were conducted in the simulated environment pairing the SM-EXO with several robotic arms to evaluate the feasibility and repeatability of the concept. Our results show that the SM-EXO effectively improves human control over the robotic arm in the simulated environment. These results can be directly translated into the real environment as well as AR/VR environments for training purposes. To facilitate intelligent learning and prediction of human intentions, we propose a finger-based gesture understanding approach using Shape Memory Alloy wires and displacement sensors embedded in the smart glove. This study provides a foundation for further research in Industry 4.0, with future directions and applications domains discussed with a focus on two specific use cases. The first use case is worker safety and convenience in hazardous environments in Industry 4.0. The second is based on rehabilitation using SM-EXO controlled simulated set of tasks.