The constant factor 3 in the definition of the Z-factor is motivated by the normal distribution, for which more than 99% of values occur within three times standard deviations of the mean. If the data follow a strongly non-normal distribution, the reference points (e.g. the meaning of a negative value) may be misleading.  
Another issue is that the usual estimates of the mean and standard deviation are not robust; accordingly many users in the high-throughput screening community prefer the "Robust Z-prime" which substitutes the median for the mean and the median absolute deviation for the standard deviation. Extreme values (outliers) in either the positive or negative controls can adversely affect the Z-factor, potentially leading to an apparently unfavorable Z-factor even when the assay would perform well in actual screening
In addition, the application of the single Z-factor-based criterion to two or more positive controls with different strengths in the same assay will lead to misleading results 
. The absolute sign in the Z-factor makes it inconvenient to derive the statistical inference of Z-factor mathematically. A recently proposed statistical parameter, strictly standardized mean difference (SSMD), can address these issues. One estimate of SSMD is robust to outliers.