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Many companies have set Parts per Million (PPM) or Parts per Million Opportunities (PPMO) goals in their quest for continuous improvement. The time-between-events (TBE) CUSUM has been suggested for monitoring the number of good units or the number of opportunities that occurred between discoveries of consecutive bad units. We focus on the robustness of the TBE CUSUM. Robustness, in this case, refers to sensitivity of the procedure to make the proper decisions regarding a shift in the mean defect rate when, in fact, the time between events is not exponential. We examine and report average run lengths (ARLs) under both a Weibull and a lognormal time between events distribution. Our results indicate that the TBE CUSUM is extremely robust for a wide variety of parameter values for both the Weibull and lognormal distributions. The implications of these results in practice imply that users of the TBE CUSUM procedure need not be concerned about departures from the exponential TBE distribution. Practical implementation of the TBE CUSUM procedure is also discussed. [ABSTRACT FROM AUTHOR]

Bayesian burn-in procedures are developed for limited failure populations in which defective items fail soon after they are put into operation and non-defective ones never fail during the technological life of the items. Sequential schemes guaranteeing the prescribed quality of a product are derived based on prior information on the quality of a product and accumulated failure information up to the decision point. Numerical examples are provided. [ABSTRACT FROM AUTHOR]

Presents the statistical and probabilistic properties of the Burr XII distribution and describes its relationship to other distributions used in reliability analyses. Statistical properties; Probability plotting procedure; Examples; Conclusion.

Explains the difference between statistical process control (SPC) and engineering process control (EPC). Presentation of a case that demonstrates successful integration of SPC and EPC for a product in a hybrid industry; Comparison of SPC and EPC.

Computer-Integrated Manufacturing (CIM) and related techniques provide more data faster and more often than was possible in the past. Also, high-quality manufacturing requires methods capable of detecting process changes even when the process is producing in the defects per million range. Past research has shown that the best method for analyzing binary data for most situations was the CUSUM procedure. Today's manufacturing environment requires even faster, more accurate detection of process changes than the CUSUM offers. This paper describes a study in developing a system based on using multiple statistical tests together to attain the extra speed and accuracy needed. The results of the research show that the system performs very well when measured against the baseline method: a CUSUM procedure with an ht value of 4.2. The system outperformed the baseline handily in speed of detection. At some points, the system would have correctly classified the equivalent of over 10% of the total received signals before the baseline. For processes measured in defects per million, the actual time differential would be even more pronounced. In addition, the system performed at least as well in accuracy, providing a composite performance over the test scenarios of 95.02% compared with 94.57% for the baseline. [ABSTRACT FROM AUTHOR]

Trade-off analysis after optimization of an economic CUSUM control chart is proposed. This procedure exploits the relatively flat response surface in the neighborhood of the optimal design. Comments are made with respect to criticisms of pure economic models and the difficulties with constrained optimization. Two previously published numerical examples are used to demonstrate that, by using trade-off analysis, large gains in statistical properties are attainable with little increase in cost. By focusing on designs with improved in-control and out-of-control run length performance, false-alarm rates can be reduced and the ability to detect process shifts quickly can be increased. [ABSTRACT FROM AUTHOR]