Abstract
In this paper we investigate how simplifying assumptions in the
stochastic modeling of machine downtimes affect the output performance
measures. Typically, literature in this area either addresses the
estimation of statistical input modeling as such or investigates how
sensitive output performance measures of queueing or simulation models
are to the choice of stochastic properties of the input distributions.
Practitioners, however, often prefer to use simplified formulae for a
rough-cut analysis. The results presented clearly show, that
unjustified simplification might lead to erroneous results. The
immediate conclusion out of the results is that sampling shop-floor
data should not only include first order statistics, but also measures
that allow to monitor and model the variability of the machinery.
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