Monte Carlo simulation was used to predict the long-term financialperformance related to the technical performance of dairy herds.The indicators addressed were derived from data collected routinelyin the herd. They indicated technical performance that can beaffected by the farmer or the consultant, and they were derivedfrom expected cause-effect relations between technical performanceand financial performance at the herd level. The study includedthe indicators shape of lactation curve, reproduction efficiency,heifer management, variation between cows in lactation curvepersistency, mortality in cows and calves, dynamics of bodycondition, and somatic cell counts. Each indicator was definedby 2 or 3 levels, and 2- and 3-factor interactions were includedin the simulation experiment, which included 72 scenarios. Eachscenario was replicated 200 times, and the resulting gross marginper cow was analyzed as the measure of financial performance.The potential effects of the selected indicators on the grossmargin were estimated by means of an ANOVA. The final modelallowed estimation of the financial value of specific changeswithin the key performance indicators. This study indicatedthat improving the shape of the herd-level lactation curve by1 quartile was associated with an increase in gross margin of227 per cow year. This represents 53% of the additional availablegross margin associated with all the management changes includedin the study. The improved herd-level lactation curve increasedthe gross margin 2.6 times more than improved reproduction efficiency,which again increased the gross margin 2.6 to 5.9 times morethan improved management related to heifers, body conditionscore, mortality, and somatic cell counts. These results wereimplemented in a simple "metamodel" that used data extractedfrom ordinary management software to predict herd-specific financialperformance related to major management changes. The metamodelwas derived from systematic experiments with a complex simulationmodel that was used directly for advanced herd-specific decisionsupport. We demonstrated the use of these key performance indicatorsto forecast the financial consequences of different "what-if"herd management options, with emphasis on herd health economics.