In industrial and medical laboratories, prior to initiating the daily test procedure, the accuracy of the system is observed by measuring the reference control values. Measurements and test results may be below or above the reference value. Hence, the control data is employed in order to ensure that the test scores lie in the targeted range values, and the results obtained from the control data are evaluated via computer-based analysis. The results of the analysis have significant importance for control and improvement of process. Furthermore, the data to be analyzed may be the test results of a product as well as the measurement outcomes obtained from a laboratory. In this study, an algorithm named as Adaptive Precision Point Algorithm is proposed to evaluate the control data and to increase the stability by reducing the loss. In this schema, the contribution to the reduction of the total systematic error was observed by calculating the target working point and the Adaptive Precision Point deviations. Measurement outputs, in other terms the data, are processed in Adaptive Precision Point Algorithm. The algorithm determines a new adaptive working point for the incoming data by doing the required computations for precision working point. Moreover, the deviation between adaptive working point and the specified working point, which is defined according to the standards and rules, is calculated. By this way, adaptive working point is being utilized throughout the reduction of systemic errors. According to the results of the research, Adaptive Precision Point Algorithm eliminates the systematic errors on a large scale. The suggested algorithm provides results within the accepted quality deviation limits so it does not form a negativity in the understanding of quality. It is also observed that the algorithm sets a positive correlation between the minimized test results and reduction of time and material usage. Furthermore, the research and the algorithm offer a cost-effective solution. Consequently, the contribution and significance of the proposed algorithm can be understood in a better way by considering that it does not only maintain the quality limits but it also minimizes the cost and time spent during the testing of thousands of laboratory samples.