Annotation: The quality analysis of products is a crucial component of production management and reliability enhancement for manufactured or repaired items. Today, the growing complexity of technological processes and the expanding range of products necessitate eff ective quality control methods that ensure reliable assessment with minimal time and resource expenditure. One of the most eff ective tools for achieving this is statistical quality control. It enables informed decisions about product quality based on sample inspection results. This paper examines two approaches for evaluating the quality of repaired products: the single sampling method and the sequential analysis method. The single sampling method helps determine whether the entire batch meets the specifi ed quality criteria using a representative sample. This method involves calculating the optimal sample size and the acceptable number of defective items, taking into account the admissible risk levels for both the supplier and the customer. The principal advantage of this method lies in the simplicity of calculations and the ability to predict the probability of batch acceptance or rejection. The sequential analysis method developed by Wald off ers a more fl exible approach for large batches of products. It allows devising a control plan in the form of a graph divided into three regions: acceptance, rejection, and an intermediate zone where additional testing is required. This method reduces the number of necessary tests, saves resources, and ensures quality assessment with specifi ed reliability and risk levels. To accurately classify batches in the intermediate region, the truncation method is applied, allowing a fi nal decision on batch quality based on additional data. This paper presents calculation examples based on real repair data, demonstrating the practical eff ectiveness of both methods. The results show that statistical quality control enhances the reliability of decisions made, optimizes the product inspection process, and ensures a high level of product reliability under given economic and technical conditions. The fi ndings are practical for quality control and reliability assurance engineers, as well as for managers at production facilities, seeking to introduce statistical control into product repair and modernization processes.

Annotation: The measurement errors upon conducting flight tests for launch vehicles are evaluated by considering the interferences and uncertainties in the measurement system procedure. Formal use of this approach can lead to unpredictable consequences. More reliable evaluation of errors upon conducted measurements can be achieved if the measurement process is regarded as a procedure of successive activities for designing, manufacturing, and testing the measurement system and the rocket including measurements and their processing during the after-flight analysis of the received data. The sampling rates of the main controlled parameters are three to ten times higher than the frequency range of their changing. Therefore, it is possible to determine the characteristics of the random error components directly on the basis of registered data. The unrevealed systematic components create the basic uncertainty in the evaluation of the examined parameter’s total measurement error. To evaluate the precision and measurement accuracy of a particular launch, the article suggests specifying the preliminary data on measurement error components determined during prelaunch processing and launch. Basic structures of algorithms for evaluation of precision and measurement accuracy for certain mathematical models that form the measured parameters were considered along with the practical case when static correlation existed among the measured parameters.