2351 that the haulage system could be operating under a configuration that warrants preventive attention at a systemic level, even in the absence of widespread critical alarms. For interpretive purposes, and considering the conceptual nature of the model, exploratory QPIFAT ranges are proposed. Values below 30 would represent a state of low latent potential, while values between 30 and 45 would indicate moderate potential requiring enhanced monitoring. Values between 45 and 60 would reflect a state of high latent potential, and values above 60 would suggest a critical condition approaching systemic collapse thresholds. Preliminarily, it can be stated that a state of high latent potential does not exist when the QPIFAT remains below 45, without a sustained upward trend. It is important to note that these ranges are exploratory and not normative and will be calibrated during the pilot phase using real system data. The main contribution of QPIFAT is not to replace existing fatigue detection systems, but to complement them by providing an anticipatory reading of the system's state before observable events occur and supporting earlier and more systematic preventative management. This approach allows for a shift in fatigue management from a predominantly reactive approach to a systemic and preventative one, aligned with the challenges of large-scale mining and with the goal of building safer, more resilient, and sustainable operations. ACKNOWLEDGMENTS Vice Presidency of Operations, Vice Presidency of SHE, Vice Presidency of HR, Mine Operations Management, Mine Management Superintendence, Mine Management Engineers, Operational Excellence Management, Safety and Health Team of Minera Las Bambas. CONCLUSIONS • The Quantum Potential Index applied to fatigue and drowsiness is a conceptual proposal aimed at anticipating high potential states in mining haulage systems before their operational manifestation. • Current fatigue monitoring systems fail to detect fatigue once the event has occurred; QPIFAT complements this approach by observing the state of the system prior to collapse, when it is still possible to intervene preventively and in a less disruptive manner. • While the current system focuses on managing individual conditions, QPIFAT is oriented towards managing the systemic state, integrating observable, latent, human and environmental variables. • The proposal does not require the incorporation of new hardware, but rather a reorganization of the existing intelligence and data already available in the operation. • This version describes the theoretical framework, methodology and a conceptual application, in line with the WMC 2026 guidelines; empirical validation and coefficient calibration will be developed during the pilot phase according to the official schedule. • No claims of risk reduction or performance improvement are made at this stage. 15. REFERENCES Reason, J. (1997). Managing the Risks of Organizational Accidents. Ashgate. Hollnagel, E. (2014). Safety-I and Safety-II. Ashgate. American Psychological Association (2009). Publication Manual of the APA (6th ed.).
RkJQdWJsaXNoZXIy MTM0Mzk2