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The present paper valuates the statistical and economic performance of Adaptive Shewhart quality control chart strategies which allow at least one of the design parameters (sample size n, sampling interval h and control limit coefficient k) to change in real time based on the actual values of the sample statistics. The automation of production processes more and more often requires a real-time analysis of control charts to make prompt corrections to the process under study. So, by evaluation of the different control chart adjustments one can assure better consistent upgrading of reliability through improved quality control, which provides an economic mean for attaining the required product-goals.

The present paper aims at considering the problems connected with design of experiments in investigation of the product quality factors of rubber compound for automobile tires. More precisely, it deals with application of mathematical models describing quality factors influencing the formation of such a compound; finding optimal answers to the requirements of the standardized rubber compound for automobile tires and so forth. The mathematical modeling and optimization is done on a given design of experiment and experimental data. Methodologically is used mainly the regression analysis. The appointed approaches for optimization in the paper give opportunity for forming of one or other compound with quality conforming to standards. That methodology can be applied in the analysis of other types of industries and products, as well.

To prosper in today's economic climate, the automotive manufacturers must dedicate their efforts to a continuous improvement. In our work here we focus upon some shop floor application. Through the use of modified Shewhart control charts we show that the measurements from such industrial processes are often serially correlated and this may lead to wrong judgment about the capability of a process. Taking into account a week-period observations, we analyze various causes for the quality capability problems of a machine in an automotive production. We show that subsequently applying modified control chart technique and time series analysis can provide us with visual and prompt detection of the data autocorrelations, which undoubtedly will assure more realistic results about the process.