Abstract
The attribute quality control charts are one of the main useful tools to use in control of quality product in companies. In this paper utilizing the statistical procedures to find the attribute quality control charts for through fuzzified the real data which we got it from Baghdad Soft Drink Company in Iraq, by using triangular membership function to obtain the fuzzy numbers then employing the proposed ranking function to transform to traditional sample. Then, compare between crisp and fuzzy attribute quality control.
Highlights
1.Introduction Statistical procedures are one of the main useful tools in supervision the production process by using control charts and sample inspection plans and these two procedures depend on the random variations which happen in terminology determine the production process is subject to specifications of quality control or not, since the units produced differ in quality, if these imbalances and deviations are minor, the production is acceptable but if these differences and deviations exceed certain limits
Control Chart is one of the most common statistical methods used in terms of monitoring the changes that occur during the stages of product process, it is determined by whether the process is statistically accurate by the observation recorded from the samples drawn
The data was taken from one of the important production plants is a laboratory for the production of drinking water bottles to detect the specifications and efficiency of production accurately and quickly, the results showed that fuzzy control chart are more accurate and economically faster in controlling the quality of production, leading to the detection of defective units during the production process, which helps to detect error quickly
Summary
Statistical procedures are one of the main useful tools in supervision the production process by using control charts and sample inspection plans and these two procedures depend on the random variations which happen in terminology determine the production process is subject to specifications of quality control or not, since the units produced differ in quality, if these imbalances and deviations are minor, the production is acceptable but if these differences and deviations exceed certain limits. The company makes the objective in manufacturing the items which vacant from defective and congruent to specifications, utilizing the control charts to esteem the statistical techniques to control the quality production. The data was taken from one of the important production plants is a laboratory for the production of drinking water bottles to detect the specifications and efficiency of production accurately and quickly, the results showed that fuzzy control chart are more accurate and economically faster in controlling the quality of production, leading to the detection of defective units during the production process, which helps to detect error quickly. F.Franceschine and D.Romano (1999), proposed a method for the online control of qualitative of the product/service using control charts for linguistic variables[5]. M.Hadi and M.Mahmoudzadeh (2017), presented the fuzzy statistical process control development for attribute quality control chart by using Monte Carlo simulation method[13].
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