Modern quality control originated with Walter A.
Shewhart, then working at Bell Telephone Laboratories. Shewhart devised a control chart named after him in and in published his method in Economic Control of Quality of Manufactured Product. Shewhart's method saw its introduction at Western Electric Company's Hawthorn plant in Joseph Juran was one of the people trained in the technique.
Quality tools – GSS
In Juran published his very influential Quality Control Handbook. Edwards Deming went to Japan to assist in the preparation of the Japanese Census. Being an expert on statistical methods, the Japanese Union of Scientists and Engineers JUSE , having heard of Shewhart's techniques, invited Deming to lecture on statistical quality control. Deming gave a series of lectures in aimed both at describing SQC and at motivating his audience of executives.
He pointed out the linkage between quality, productivity, and potential gains in market share. He found an enthusiastic audience. JUSE also invited Juran to lecture in with similar success, but by that time Deming had achieved wide prominence in Japan. With the great success enjoyed by SQC in Japan, and through his own abilities as a teacher and promoter of quality control and related management approaches, Deming became the iconic figure in the field, the "father of quality control.
Japanese improvements in industrial performance eventually aroused interest in the United States in the early s, led by Lockheed Corporation. Quality control then took on a life of its own in this country.
EE 390C: Statistical Methods in Engineering and Quality Assurance
Before the advent of statistical quality control, control was exercised by inspecting the output of manufacturing processes and removing defective items. The modern technique established an upstream method for detecting deviations from specified quality—early detection—used to trigger analysis of causes and then changes to manufacturing procedures.
SQC requires that the producer first identify several characteristics of a product to be measured, typically its dimensions, fit with other parts, smoothness, reflectivity, etc.
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Carefully conducted test runs are made first; every part is measured and its measurements are recorded. Upper and lower boundaries are set for every measurement from one or repeated test runs, with the idea that any part that falls within these boundaries conforms to the product's quality standard.
The center line between the boundaries is then used as a base-line for measurement. Once this quality standard is set, production can begin. The quality control activity during production consists of taking samples from the run continuously, taking measurements on the samples immediately, and then plotting them rapidly on a Shewhart Chart.
During production, measurements typically fall close to the center line, some above it, some below it, some on the center line. A certain amount of divergence is natural and cannot be avoided. So long as the plotted points are within the accepted boundaries, the product conforms to the quality standard. But SQC demands that if the plotted points begin to show a trend away from the center, rather than clustering randomly around it—or, worse yet, begin to fall outside the boundaries in either direction—then production must stop. The incoming raw material, the production machinery, and other inputs, such as lubricants, must next be examined to discover why results are trending in the wrong direction or fall outside the acceptable range.
SQC thus provides early warning that quality is deteriorating. When the method is applied strictly, production cannot resume until problems are detected and fixed—as shown by brief test runs.
Needless to say, money is saved by preventing wasteful production of parts later, products that fail to fit, or parts that result in product failure in use. In aircraft and autos, such failures can mean injury and death and massive lawsuits.
Corrective actions taken early improve the process as a whole. In due time they lead to better equipment designs. The technique also lends itself to the gradual ratcheting up of quality. This is accomplished by setting "acceptable boundaries" more narrowly and then modifying the production process until the new quality goal is met. The powerful statistical and graphical possibilities enhance the value we get from the data.
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More case studies. Data Science We develop models geared to your real-world needs and apply state-of-the-art statistical methods to analyse your data. These problems can range from selecting random data samples to designing statistical experiments. Process engineers also might need to know how to construct and apply various prediction regression equations. These analyses usually include the application of statistical process control procedures, covering univariate and multivariate samples. In addition, there is a need in all areas of application to be able to test a statistical hypothesis.
In short, the increase in process data has led process engineers to seek more training in statistics.
This result immediately generates questions: "What type of statistical background is necessary to produce statistical engineers? Most university engineering degree curriculums are already filled with required engineering courses and include limited time for external course electives, such as in statistics. Statistical courses recommended for someone serving as a statistical engineer can vary. A first course for example, applied statistics I usually contains data presentation frequency tables, histograms and box plots , descriptive statistics mean, median, mode, range, variance, standard deviation, quartile and quantile , basic normal probability theory and statistical hypothesis testing.
Several one-sample testing procedures—such as the binomial test, normal test and the t-test—are detailed, along with confidence intervals. The appropriate tests of hypotheses for one-sample variance problems are also included. A continuation of this course for example, applied statistics II will expand the coverage to two-sample tests of hypotheses for the mean for example, two sample t-test and paired t-test and the two-sample variance test for example, the F-test. The chi-square goodness of fit test through the one-way analysis of variance ANOVA procedure will also be covered.
An important topic covered in this second course is the correlation between two variables. Not only is the technical definition of correlation presented, but graphical techniques are also introduced to recognize when correlation exists between two variables. Figure 1 shows a scatterplot of the shoe size versus height of 85 male college students. In this plot, the height variable increases as the shoe size increases.
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In general, this second set of applied statistics courses emphasizes the appropriate application of these procedures. We cannot overemphasize the importance of these two foundational courses, which lay the groundwork for the development of other statistical procedures. For example, it is easy to develop Shewhart charting procedures univariate statistical process control by establishing confidence intervals on the population mean.
Also, good coverage of the testing of hypotheses of equal means for the two-sample problem provides a natural lead-in to ANOVA procedures. ANOVA procedures constitute a second important area of statistical concentration for a statistics engineer. This topic is important not only in learning how to compute different ANOVA procedures for example, one-way and two-way , but also to understand experimental design concepts, such as completely randomized design, randomized block design and factorial design.