This study revisits the Newsvendor problem within the context of Industry 5.0, introducing a novel experimental framework, the N2G, which aims to more accurately simulate the complex decision-making environments faced by modern operators. Employing a fixed decision horizon, role-specific simulations, and a streamlined decision-making process, our approach significantly enhances the realism of the experimental conditions. Conducted with 75 graduate students from industrial and mechanical engineering disciplines, the study investigates the effects of variable demand distributions—constant, negative exponential, and normal—on ordering behaviors. Findings reveal that the N2G setting leads to a more pronounced alignment of orders with optimal levels under negative exponential and normal conditions, effectively mitigating the pull-to-center effect (PTCE). Under constant demand, although the PTCE persists, it is considerably less pronounced than observed in traditional experimental settings. These results underscore the importance of situational realism in reducing cognitive biases and suggest that enhanced experimental designs can provide valuable insights for developing training programs and decision-support systems tailored to the needs of Industry 5.0.