As a result, risk management tends to maintain a retrospective and reactive character, with low sensitivity to detect emerging states or unprecedented combinations of variables that have not yet been previously experienced. In the specific area of fatigue and drowsiness, current systems allow for the real-time identification of fatigued operators; however, they have structural limitations in anticipating systemic configurations prior to the event's manifestation. The recurrence of alerts, their concentration by shift, route, or specific periods, and the persistence of events despite the implementation of active breaks, demonstrate that fatigue must be addressed as an emergent phenomenon of the work system, and not solely as an individual condition. In this context, the Quantum Potential Index Applied to Fatigue (QPIFAT) proposes to incorporate unobserved potential states within a formal structure, with the purpose of anticipating possible futures before their materialization, thus complementing traditional detection and control approaches. 3. STATE OF THE ART Globally, research in predictive mining has focused on: ● Machine learning models based on supervised and historical training. ● Predictive maintenance systems. ● Equipment reliability models. ● Bayesian probability applied to security events. ● Discrete event simulation models. ● Predictive indicators derived from human behavior. Although these approaches offer value, they share a common limitation: none explicitly incorporates the concept of overlapping possible futures, understood as the simultaneous coexistence of multiple preventative configurations before the materialization of an event. A similar conceptual framework can be found in: ● Intrinsically Safe Design (ISI). ● Early hazard identification (EN-HazID) in process engineering. ● Models of complex systems in aviation and petrochemicals. ● Advanced “leading indicators” approaches. However, none of them formalize the representation of simultaneous potential states. The QPIFAT seeks to fill this methodological gap. At the industrial level, fatigue management relies on work-rest rules, physiological monitoring, statistical analysis of alarms, and probabilistic models. While these approaches offer value, none formalizes the simultaneous coexistence of multiple possible futures before an event occurs, nor does any structuredly integrate latent and human variables within an anticipatory framework. 4. FOUNDATIONS OF QPIFAT The Quantum Potential Index Applied to Fatigue is a conceptual model inspired by principles of quantum physics, used metaphorically to represent the behavior of complex organizational systems. The use of the term “quantum” does not imply the direct application of quantum mechanical equations but rather serves as an analogy to describe the coexistence of multiple possible operational futures before an event occurs. 5
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