Manufacturing systems typically contain processing and assembly stages whose output quality is significantly affected by the output quality of preceding stages in the system. This study offers and empirically validates a procedure for (1) measuring the effect of each stage's performance on the output quality of subsequent stages including the quality of the signal product, and (2) identifying stages in a manufacturing system where management should concentrate investments in process quality improvement. Our proposed procedure builds on the precedence ordering of the stages in the system and uses the information provided by correlations between the product quality measurements across stages. The starting point of our procedure is a computer executable network representation of the statistical relationships between the product quality measurements; execution automatically converts the network to a simultaneous-equations model and estimates the model parameters by the method of least squares. The parameter estimates are used to measure and rank the impact of each stage's performance on variability in intermediate stage and final product quality. We extend our work by presenting an economic model, which uses these results, to guide management in deciding on the amount of investment in process quality improvement for each stage. We report some of the findings from an extensive empirical validation of our procedure using circuit board production line data from a major electronics manufacturer. The empirical evidence presented here highlights the importance of accounting for quality linkages across stages in (a) identifying the sources of variation in product quality and (b) allocating investments in process quality improvement.Quality and Process Improvement, Total Quality Management, Investments in Learning, Multistage Manufacturing Systems
Therapeutic plasma exchange (TPE) without plasma replacement results in coagulation factor removal. Warfarin decreases the activity of vitamin K dependent coagulation factors. The combined effect of TPE and warfarin on the coagulation system has not been studied. A prospective, observational study was conducted in patients undergoing TPE while on warfarin. One plasma volume TPEs were performed on the COBE Spectra Apheresis System (Terumo BCT, Lakewood, CO) with 5% albumin. International normalized ratio (INR), fibrinogen, and factor II activity were obtained pre and post procedure. Eight patients underwent 121 TPEs that met study criteria with pre and post data. The average pre values were INR 2.09 ± 0.58, fibrinogen 263 ± 76 mg/dl, and factor II 29 ± 16% and the average post values were INR 4.12 ± 1.44, fibrinogen 105 ± 31 mg/dl, and factor II 13 ± 7%. The pre-INR was ≥2.00 for 55% of TPEs. The pre value (Y0 ) predicts the post value (Y) by the following equations Y = -0.54 + 2.21Y0 , Y =12.10 + 0.35Y0, and Y =1.83 + 0.39Y0 for INR, fibrinogen, and factor II respectively. In conclusion, pre procedure laboratory values can predict the post laboratory values for patients on warfarin receiving single plasma volume TPE with albumin replacement. The post-INR is approximately twice the pre-INR. At normal and mildly elevated pre-INR, the effect of TPE on the INR is less marked. A single plasma volume TPE decreases the plasma level by ∼65% for fibrinogen and 60% for factor II.
Manufacturing systems typically contain processing and assembly stages whose output quality is significantly affected by the output quality of preceding stages. The deficiencies of using standard statistical process-monitoring procedures in such systems have been highlighted in the literature. This article proposes a procedure to monitor process and product quality in multistage systems. By accounting for the quality of the input to each stage, the procedure not only detects the presence of out-of-control conditions but also helps to identify the stages responsible for such departures. We extend previous research to the common case where the process parameters are unknown. An extensive performance study shows that the procedure is effective in detecting out-of-control conditions and that it convincingly outperforms existing methods. We illustrate the use of the procedure using production line data from a major electronics manufacturer.
This article studies the performance of the Shewhart chart of Q statistics in the detection of process mean shifts in start-up processes and short runs. We propose an accurate, analytic approximation of this chart's run-length distribution. Our study reveals that the chart has an early detection advantage in that it is more likely than other methods to detect a process mean shift within the first few observations following the shift. This is a desirable property because early detection should make it easier to identify the cause of the shift, increasing the rate of continuous quality improvement. In addition, our analysis illustrates the importance of reacting immediately to out-of-control signals from the chart as compared to waiting for subsequent observations to confirm the presence of a shift.
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