  Remanufacturing is a significant factor in securing sustainability through a
circular economy. Sorting plays a significant role in remanufacturing
pre-processing inspections. Its significance can increase when remanufacturing
facilities encounter extreme situations, such as abnormally huge core arrivals.
Our main objective in this work is switching from less efficient to a more
efficient model and to characterize extreme behavior of core arrival in
remanufacturing and applying the developed model to triage cores. Central
tendency core flow models are not sufficient to handle extreme situations,
however, complementary Extreme Value (EV) approaches have shown to improve
model efficiency. Extreme core flows to remanufacturing facilities are rare but
still likely and can adversely affect remanufacturing business operations. In
this investigation, extreme end-of-use core flow is modelled by a threshold
approach using the Generalized Pareto Distribution (GPD). It is shown that GPD
has better performance than its maxima-block GEV counterpart from practical and
data efficiency perspectives. The model is validated by a synthesized big
dataset, tested by sophisticated statistical Anderson Darling (AD) test, and is
applied to a case of extreme flow to a valve shop in order to predict
probability of over-capacity arrivals that is critical in remanufacturing
business management. Finally, the GPD model combined with triage strategies is
used to initiate investigations into the efficacy of different triage methods
in remanufacturing operations.
