All-in-all-out (AIAO) systems expose the fact that some pigs require longer to market weight than others (some even 4 to 6 weeks longer). This fact went unnoticed in continuous flow operations. This variability in weights in the AIAO systems can result in significant economic loss. To calculate variability, statistical tools such as mean (average), median (middle value of a data set), mode (value that occurs most frequently), minimum and maximum values, standard deviation (measure of dispersion), and coefficient of variation (a percentage). Sample group size to determine these factors is related to the variability. For example, if you want to determine coefficient of variation for weaning weights, a large number is needed. This is because the variability is so high. A list of causative agents for variability is listed along with an explanation as: 1) Pre-natal influences; 2) Post-natal influences; 3) Post-weaning influences; 4) Herd health and pathogen exposure; 5) Feed and water. Aside from these primary factors, there is also the social behavior theory. This regards the hierarchy that pigs create which results in the inferior pigs having a restriction of feed and water, being crowded, or having competition with limited resources. Production targets for variability (coefficients of variation) should not exceed 20% of weaning weights, 12 to 15% for nursery exit weights and 8 to 12% for weight at first pull from the finishing barn. If these are exceeded, managers can enhance the growth of the slower growing pigs via improving the herd health or having better access to food and water. Variability can be managed by pre-planned segregation (splitting up groups based on expected future performance), parity segregation (separate housing of gilts and their offspring), increasing weaning age, increasing overall weight gain (to minimize the impact of tail-enders), and weighing pigs at marketing.