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Control chart is the basic tool of the statistical process control (SPC). It aims to an early detection of errors in the process and thereby ensures compliance with the required level of stability. The statistical process control is an integral part of the production management necessary for achieving a high product quality. Just the quality of the product decides the customer satisfaction and thus the success of the whole company. Classical Shewhart control charts can be used only if there are met certain basic assumptions. These assumptions include, for example, data normality, their independence and constant mean and variance. In practice, such as the metallurgical industry, those assumptions about the data are not necessarily always met. In the case that these conditions are not met, there may be used non-parametric and robust control charts. This paper presents some of these non-traditional control charts. This article aims to define the difference between robust and non-parametric methods. Another aim of this article is to present the possibility of evaluating the robustness, and the evaluation of effectiveness based of individual control charts. Particular evaluation methods are complemented by practical examples from the metallurgical process. Conclusion of the article includes comparisons of the used control charts, both in terms of robustness and effectiveness. During preparation of this article accessible pieces of knowledge on the issue were compared, including the use of SPC in the metallurgical industry. This article is the basis for further examination of the problem, including a more detailed processing of the software support.

Keywords: robustness, effectiveness, statistical process control, non-parametric control charts, steelmaking process

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