Graduate Thesis Or Dissertation

 

An economic comparison of X[bar], cumulative sum and geometric moving average control charts for controlling process mean Pubblico Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/c534fr86z

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  • In production processes, there are two types of variations that affect production quality - - variations produced by chance causes and variations produced by assignable causes. One of the main instruments in quality control used to control quality by distinguishing between variations produced by chance causes and a real process change is the control chart. Each type of control chart has advantages and disadvantages in a specified situation. For example, some control charts fail to detect small shifts, while the others are ineffective to detect large shifts in process mean. In this study, three types of control charts, namely, X[bar], cumulative sum, and geometric moving average control charts were compared on an economic basis. A simulation model was developed to simulate the control chart functions in a typical production process. The simulation was executed in BASIC on an IBM PC/XT. Before comparison, each control chart was matched so that all the control charts have the same characteristics when the process operates in-control for a certain period of time. The effects of the type of control chart, sample size, sampling interval, and the magnitude of shift in process mean on profit per hour were observed and analyzed using Analysis of Variance (ANOVA). The results show that, in general, the cumulative sum control chart has advantage over the other two types of control charts when shift of small magnitude of about 0.5σ is present. X[bar]-control chart is ineffective to detect small shifts; however, its effectiveness increases sharply as the magnitude of shift increases to values of 1.5σ or beyond. Geometric moving average control chart gives best results at intermediate shift levels of about l.0σ. Of the three sample sizes (3, 4 and 5) used in this study, sample size of five yields the highest profit per hour. However, too large a sample size may result in a decrease of profit per hour if the testing causes the destruction of items and the cost of sampling per item is very high. Small sampling interval of one hour yields the highest profit per hour among three sampling intervals (1, 2 and 4 hours) used in this study. Too small sampling interval could yield lower profit per hour if the increased cost of more frequent sampling, more investigations caused by false alarms, and more frequent shut down of the production process exceeds the savings from early detection of the shift, particularly, when the cost of sampling, the cost of searching for an assignable cause, and the income per hour of production are very high.
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