In any product line, no two articles are perfectly identical. Statistical techniques are also very useful in determining sample size, deciding rate of recurrence of inspection, deciding natural limits of variation of the process, testing conformity of sample to specification provided and so on. Experiments designed to assess the advantages of novel types of processing or to determine optimal conditions also fall into the category of SQC. The main focus of statistical techniques is to avoid defects that are produced in the manufacturing process. Statistical techniques are important tools for effective process control and innovative solutions to problems. The only reasonable method of addressing the above problems is to examine a small fraction of the population or output, on the assumption that the results of the sample are representative of the untested population or output.Ħ0 − x ¯ = − 1.645 σ 65 − x ¯ = − 0.130 σ To do this, garment blanks must be inspected, but the rate of production is too high to examine every single blank. He is content with this level of defective items but he does not want it to increase, so he aims to control the level, that is, to detect quickly any increase in the number of defective items being produced so that remedial action can be taken. For example, a garment manufacturer knows from past experience that usually 2% of the garment blanks he produces are defective.
The rate of production is too high to examine every product. To determine this average exactly, the waist size of every man in the population would have to be measured, which would of course be prohibitively expensive and time-consuming. In order to design the trousers, therefore, he must know the average waist size of the men in the population to whom he is hoping to sell the trousers. For example, a menswear manufacturer marks the size of the trousers he produces according to the waist size. There would therefore be no product left to work with. When the batch of yarn is delivered, it would be impractical to test the whole consignment for whether or not the average linear density lies within the tolerances, as the standard test for linear density is destructive. It has been settled between the two parties that each consignment of yarn delivered to the fabric manufacturer should have an average linear density inside the tolerance range 40 ± 1 Tex. For example, a fabric manufacturer buys yarn from a spinning mill. The standard test is destructive in nature. However, inspection of all of the raw materials and finished goods is impossible, because: The manufacturing processes currently in use are not capable of producing completely identical products. Variation in the quality of manufactured textiles is inevitable. To do this it is necessary to use tools from the SPC category. However, although acceptance sampling is helpful in deciding on acceptability after the product has been produced, it does not aid in identifying a quality problem during the production process. Acceptance sampling can help to solve this problem. Although descriptions of specific characteristics are helpful, they are not enough to identify whether there is a problem with quality. Descriptive statistics are used to describe certain quality characteristics, such as the central tendency and variability of observed data. The tools in each of these categories provide different types of information for use in quality analysis. Based on the results of the sample, a decision is made as to whether a batch of goods should be accepted or rejected. iii.Īcceptance sampling: This involves random inspection of a sample of goods. SPC is used to determine whether the process is functioning properly or not. SPC: This involves inspecting a random sample of the output from a process and deciding whether the characteristics of the products in the sample fall within a predetermined range.
This group includes the mean, standard deviation, range and distribution of data. It can be divided into three broad categories: i.ĭescriptive statistics: These are used to describe quality characteristics and relationships. SQC comprises the set of statistical tools used by quality control professionals.
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