Livestock production is recognized as one of the most important sub-sectors of agriculture, whose growth and development depend on the correct implementation of scientific and technical principles. Numerous obstacles hinder the development of the livestock sector, the most critical of which is inadequate productivity. In this context, achieving a precise understanding of the factors that influence productivity enhancement is not merely an objective but an unavoidable necessity for improving the profitability of dairy production units.Genetic improvement of dairy cattle, which aims to increase and enhance the genetic quality of future generations, can play a pivotal role in boosting productivity. With the incorporation of novel traits into breeding programs, such as residual feed intake (RFI), body weight composite (BWC), and others, the focus of researchers has shifted from indiscriminately increasing milk yield toward improving feed efficiency. Given that the primary challenge facing dairy cattle herds is suboptimal productivity, the adoption of advanced breeding tools, such as the establishment of comprehensive information ecosystems, development of economic profit functions and interpretation of costs and revenues, construction and analysis of lactation curves, and simulation of optimized production scenarios using programming languages, can provide substantial contributions to productivity improvement. The present study demonstrates that the application of advanced statistical software to derive farm-specific lactation curves and compare them with standard curves, to estimate breeding values for various traits and rank dairy cows accordingly, to calculate economic selection indices tailored to domestic market and economic conditions and to determine the optimal milk production level through simulation, can significantly enhance economic efficiency.