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Studies and results have departed from the increasing number of industrial digitalization systems interlinked to available production systems and the increased amount of input and real-time data generated from the shop floor.
The focus of this work package has been on how new disruptive systems, built on the Industrial Internet of Things (IoT) and sensor-based technologies measure machine status and systems status. Specifically, how data-driven and user-driven data are interrelated with everyday manual production work. Hence, to what degree production data are extracted, aggregated, and visualized for everyday decision-making, which affects long-term business values.
User studies of the IoT systems, CoPilot at GKN Aerospace Sweden AB, and On-top at Siemens Energy AB Trollhättan show that machine data, system data, and manual/operative decisions create challenges to practitioners and work routines but have created a more sustainable work environment, improved production estimation and data aggregation.
The studies include design, implementation, and everyday use and the possibilities to instantly visualize the status of production work. Research methods have been conducted with a design-in-use approach, from managers, technicians, and operators' perspectives, with specific emphasis on employees' everyday work routines and learning at work.
Data collection during 2020-2022 included a total of 25 formalized sessions: interviews, field observations, and focus groups, in addition to regular meetings at both companies. General results show a need to further study the representation and augmentation of data and handle the complexity of real-time data with work practice in heterogeneous IT infrastructures on the shop floor.