Exploring the potential of Operator 4.0 interface and monitoring

In the context of smart factories, where intelligent machines share data and support enhanced functionalities at a factory level, workers are still seen as spectators rather than active players (Hermann, Pentek, & Otto, 2017). Instead, Industry 4.0 represents a great opportunity for workers to become part of the intelligent system; on one hand, operators can generate data to program machines and optimize the process flows, on the other hand they can receive useful information to support their work and cooperate with smart systems (Romero et al., 2016). Diversely from machines, humans are naturally smart, flexible and intelligent, so putting the operators in the digital loop can bring more powerful and efficient factories. The paper aims at defining a theoretical human-centered framework for Operator 4.0, and testing its feasibility and impact on companies, thanks to the integration of human factors in 4.0 computerized industrial contexts. The proposed framework is based on data collection about the workers’ performance, actions and reactions, with the final objective to improve the overall factory performance and organization. Data are used to assess the workers’ ergonomics performance and perceived comfort and to build a proper knowledge about the human asset of the factory, to be integrated with the knowledge derived from machine data collection. The framework is cased on the adoption of an Operator 4.0 monitoring system, which consists of an eye tracking and a wearable biosensor, combined to a proper protocol analysis to interpret data and create a solid knowledge. Virtual prototypes are used to make the workers interact with the digital factory to conveniently simulate the human–machine interaction (HMI) in order to avoid bottlenecks at the shop floor, to optimize the workflows, and to improve the workstations’ design and layout. The study represents a step toward the design of human-centred industrial systems, including human factors in the digital twin. The research approach has been successfully tested on an industrial case study, developed in collaboration with CNH Industrial, for the re-design of assembly workstations.