Üretici ve tüketici risklerini dikkate alarak oluşturulan ölçüm si̇stemleri̇ anali̇zi̇ni̇n değerlendi̇ri̇lmesi̇

With the globalization of the world, the development of technology and the spread of the internet, a more conscious consumer profile has started to be formed. In the past, when customers evaluated the price of a product or service, the variables such as quality, reliability and easy transportation have gained priority. In this competitive environment, companies are trying to meet the demands and expectations of customers with minimum cost and high quality in order to survive. Therefore, in order to improve their quality, enterprises have started to implement some quality improvement techniques such as Total Quality Management, Lean Production, Statistical Process Control, Measurement Systems Analysis under the name of Quality Management Systems. Quality management systems need to be developed in order to improve the quality, to improve efficiency, to ensure continuity and to make all these decisions in line with effective decisions with minimum cost. It is very important for the firms to be able to detect any mistakes that may occur in a production process or that may occur before the loss (time, cost, etc.) that may occur. Control of processes is provided by statistical techniques and control graphics. In order to perform an effective analysis of the data of a process or product variables and to apply statistical process control, the reliability of the data must first be determined by measurement systems analysis. In this study, it is aimed to examine the effect of the variables by applying measurement systems analysis and control graphics techniques on the width measurements of a certain number of pieces taken from the production process of an enterprise producing plastic tablecloths, to test whether the process is under control and the accuracy of the accepted sample number. The results of the measurement analyzes were evaluated with the MINITAB 17 statistical package program and compared. Keywords: Measurement Systems Analysis, Analysis of Variance, R&R, Quality.