Monte Carlo simulation was used to predict the long-term financial performance related to the technical performance of dairy herds. The indicators addressed were derived from data collected routinely in the herd. They indicated technical performance that can be affected by the farmer or the consultant, and they were derived from expected cause-effect relations between technical performance and financial performance at the herd level. The study included the indicators shape of lactation curve, reproduction efficiency, heifer management, variation between cows in lactation curve persistency, mortality in cows and calves, dynamics of body condition, and somatic cell counts. Each indicator was defined by 2 or 3 levels, and 2- and 3-factor interactions were included in the simulation experiment, which included 72 scenarios. Each scenario was replicated 200 times, and the resulting gross margin per cow was analyzed as the measure of financial performance. The potential effects of the selected indicators on the gross margin were estimated by means of an ANOVA. The final model allowed estimation of the financial value of specific changes within the key performance indicators. This study indicated that improving the shape of the herd-level lactation curve by 1 quartile was associated with an increase in gross margin of 227 per cow year. This represents 53% of the additional available gross margin associated with all the management changes included in the study. The improved herd-level lactation curve increased the gross margin 2.6 times more than improved reproduction efficiency, which again increased the gross margin 2.6 to 5.9 times more than improved management related to heifers, body condition score, mortality, and somatic cell counts. These results were implemented in a simple "metamodel" that used data extracted from ordinary management software to predict herd-specific financial performance related to major management changes. The metamodel was derived from systematic experiments with a complex simulation model that was used directly for advanced herd-specific decision support. We demonstrated the use of these key performance indicators to forecast the financial consequences of different "what-if" herd management options, with emphasis on herd health economics.