One objective of Industry 4.0 is to reach a better system performance as well as to have a better consideration of humans. This would be done by benefiting from knowledge and experience of humans, and balancing in a reactive way some complex or complicated tasks with intelligent systems. Several studies already dealt with such an objective, but few are done at a methodological level, which forbids, for example, the correct evaluation of design choices in terms of human awareness of the situation or mental workload when designing intelligent manufacturing systems integrating the human. Indeed, increasing the intelligence and autonomy of industrial systems and their composing entities (resources, products, robots…), as fostered by Industry 4.0, increases their overall complexity. This modification reduces the ability to understand the behaviors of these systems, and leads to the difficulty for humans not only to elaborate alternative decisions when required, but also to make effective decisions and understand their consequences. This paper evaluates such a design methodology, the Cognitive Work Analysis (CWA), and its applicability when designing an assistance system to support Human in the control of Intelligent Manufacturing System in Industry 4.0. Among several functions identified through the application of CWA, the assistant system might have to integrate a digital twin of the intelligent manufacturing system. The evaluation of the methodology through the one of the designed assistant systems is done using a micro-world, which is an intelligent manufacturing cell composed of intelligent mobile ground robots, products, and static production robots interacting together and with a human supervisor in charge of the reaching of several time-based and energy-based performances indicators. The assistant system embeds a digital twin of the intelligent manufacturing system. Twenty-three participants took part in experiments to evaluate the designed assistance system. First results show that the assistance system enables participants to have a correct awareness of the situation and a correct evaluation of their alternative decisions, while their mental workload is managed and expected production performances are reached. This paper contains an analysis of these experiments and points out some limits of the CWA method in the context of Industry 4.0, especially the lack of tool enabling to specify clearly the cooperation processes between the supervisor and the intelligent manufacturing system. This paper concludes with potential research avenues, the main one being the potential benefits of coupling CWA with human–machine cooperation principles to fine tune and adapt the cooperation between the human and the intelligent manufacturing system.