Abstract

The nuclear industry faces significant challenges in optimizing facility efficiency due to complex information systems, fragmented data exchange, and often implicit human factors. To address these challenges, this study proposes an innovative integrated analytical approach that combines graph theory with the Technology-Organization-People model for human-system integration. This approach allows the structuration between the technological, organizational, and human dimensions of complex socio-technical systems to provide a more comprehensive understanding of data management strategies. In addition, we introduce a method for extracting and estimating the cognitive load experienced by the human entities, allowing for the consideration of intrinsic human factors.

A synthetically generated dataset was used to simulate real-world operations, allowing us to apply the graph theory method called Betweenness centrality to identify critical nodes providing insight into the underlying structure of nuclear facility dataflows. Our results demonstrate the effectiveness of combining graph theory methods with human-centered models to highlight the critical role of human factors in data management strategies. The results of this study have significant implications for improving human-centered considerations as well as the efficiency, reliability, and performance of nuclear facilities throughout their lifecycles.

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