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Improving quality of statistical process control by dealing with non-normal data in automotive industry

Andrássyová Z., Paulicek T., Pichna P., Kotus M.

Management Systems in Production Engineering

2012

nr 3 (7)

26-30

Study deals with an analysis of data to the effect that it improves the quality of statistical tools in processes of assembly of automobile seats. Normal distribution of variables is one of inevitable conditions for the analysis, examination, and improvement of the manufacturing processes (f. e.: manufacturing process capability) although, there are constantly more approaches to non-normal data handling. An appropriate probability distribution of measured data is firstly tested by the goodness of fit of empirical distribution with theoretical normal distribution on the basis of hypothesis testing using programme StatGraphics Centurion XV.II. Data are collected from the assembly process of 1st row automobile seats for each characteristic of quality (Safety Regulation -S/R) individually. Study closely processes the measured data of an airbag´s assembly and it aims to accomplish the normal distributed data and apply it the statistical process control. Results of the contribution conclude in a statement of rejection of the null hypothesis (measured variables do not follow the normal distribution) therefore it is necessary to begin to work on data transformation supported by Minitab15. Even this approach does not reach a normal distributed data and so should be proposed a procedure that leads to the quality output of whole statistical control of manufacturing processes.

W artykule dotyczącym metod i technik prognozowania wartości kluczowych wskaźników efektywności obsługi (ang. Key Performance Indicators) zdefiniowano oraz sklasyfikowano te wskaźniki. Przedstawiono wymagania oraz sposób doboru wskaźników KPI. Dokonano przeglądu, a także scharakteryzowano metody i techniki prognozowania stanu organizacji utrzymania ruchu. Przedstawiono koncepcję wykorzystania wybranych metod oraz narzędzi gromadzenia oraz przetwarzania danych dla potrzeb prognozowania tego stanu.