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Adjusting service performance criteria based on categorised information from previous reports

This paper uses non-linear least square method for developing stochastic models to fit the historical ordinal data for evaluating service performances. This paper then provides formula to adjust current criteria to a future point, to set criteria to promote performance, and determine criteria to hedge risks. This paper shows that current criteria can be adjusted for a future point by incorporating the trend f and time factors. By evaluating the benefit of achieving the criterion and the loss of missing the criterion, this paper develops the equations to adjust current criteria under different conditions. By setting a downside risk constraint, companies can exclude the criterion choices which are very risky even if the returns are extremely high when they succeed. The paper further describes, by using numerical examples, the impacts of parameters to the downside risk.