Bridging Epidemiological Modelling and Quality Engineering: Optimal Mitigation of Viral Contagion

Abstract

Industry 5.0 is based on the digital technologies of Industry 4.0. However, it shifts the main focus from pure efficiency to a sustainable, resilient, human-centred approach. Since quality control and waste reduction are essential to sustainability, this exploratory study proposed an innovative approach: the adaptation of epidemiological models to quality control. Specifically, this paper investigates how the classical Susceptible-Infected-Recovered (SIR) model, usually applied to biological disease and information transmission, can be also adapted to determine the optimal moment to apply decisions of quality control and, or, predictive maintenance interventions. The methodology formulated an Optimal Control (OC) problem which is first validated using an empirical case of disinformation spread and solved numerically through an indirect method (using the CasADi software). Results show that OC strategies were able to minimise the system's global cost as well as reduce the number of infected individuals by 8.46\%, when comparing to a non controlled scenario. It is concluded that, by transferring this mathematical framework to physical manufacturing production lines, conceptualizing the propagation of defects in processes or components as an infectious phenomenon, engineers and managers can be equipped with a quantitative, data-driven tool. This allows the optimisation of timely interception of defects at their source, ensuring a sustainable reduction of industrial waste as well as the value of operators’ decisions within production centres in the context of Industry 5.0.