Statistics process measurement systems analysis

Failure modes analysis Statistical process control Measurement systems analysis Employee motivation On-the-job training Efficiency will increase through common application of requirements for Continuous improvement in cost Continuous improvement in productivity Employee motivation On-the-job training... 

The intent in this chapter is not to present in great detail the mathematics behind the statistical methods discussed. An excellent reference manual assembled by the Automotive Industry Action Group (AIAG), Fundamental Statistical Process Control, details process control systems, variation, action on special or common causes, process control and capability, process improvement, control charting, and benefits derived from using each of these tools. Reprinted with permission from the Fundamental Statistacal Process Control Reference Manual (Chrysler, Ford, General Motors Supplier uality Requirements Task Force , Measurement Systems Analysis, MSA Second Edition, 1995, ASQC Press. 

Depending on the chosen approach (see Section 3.1) service quality can be measured from the customer s and the service provider s point of view. Measuring from the service provider s point of view involves gathering data that are internally available, such as performance measures or quality cost (Eversheim 1997). They can be analyzed using well-known methods from quality management in manufacturing processes, such as statistical process control (Gogoll 1996). In addition, service quality can be assessed indirectly by an overall analysis of the quality system, which is done by a... 

The customer satisfaction measurement system must connect to the internal measures of a company and to external customer evaluations. To provide evidence, surveys can be performed to make possible a gap analysis (the gap is the difference between what the customer should experience and what the customer actually experiences). The data of actual-to-expected performance allow the application of advanced statistic techniques such as regression analysis to determine empirically the relative impact and/or importance of attributes and processes to customer satisfaction (the power of this technique and an example of its application are illustrated in Anton [1996]). To improve customer satisfaction, identifying the specific attributes and processing the most predictive of customer satisfaction decisions alxtut investment of resources will yield the greatest benefit. 

Likewise, in continuous polymerizations, the end-use and polymer properties variables may be measured very infrequently, and most often, off-line. In addition, the laboratory analysis time adds a large delay to the measurement Under these circumstances, the measurements are substantially uncorrelated in time, and again, statistical process control may be an appropriate approach to control of polymer quality or end-use properties. If the samples are sufficiently infrequent that the process settles between samples, conventional SPC can be applied. This amounts to manual steady-state ccxitrol with the need for control identified by statistical techniques. If the system does not "settle between samples, more specialized techniques of analysis are available [63]. Good reviews of the applicability of SPC to chemical processes are given by MacGregor [64,65]. 

The traditional approach for process monitoring is to compare measurements against specified limits. This limit checking technique is a standard feature of computer control systems and is widely used to validate measurements of process variables such as flow rate, temperature, pressure, and liquid level. Process variables are measured quite frequently with sampling periods that typically are much smaller than the process setthng time (see Chapter 17). However, for most industrial plants, many important quality variables cannot be measured on-line. Instead, samples of the product are taken on an infrequent basis (e.g., hourly or daily) and sent to the quality control laboratory for analysis. Due to the infrequent measurements, standard feedback control methods fike PID control cannot be applied. Consequently, statistical process control techniques are implemented to ensure that the product quality meets the specifications. 

In principle, FCS can also measure very slow processes. In this limit the measurements are constrained by the stability of the system and the patience of the investigator. Because FCS requires the statistical analysis of many fluctuations to yield an accurate estimation of rate parameters, the slower the typical fluctuation, the longer the time required for the measurement. The fractional error of an FCS measurement, expressed as the root mean square of fluorescence fluctuations divided by the mean fluorescence, varies as 1V-1/2, where N is the number of fluctuations that are measured. If the characteristic lifetime of a fluctuation is r, the duration of a measurement to achieve a fractional error of E = N l,/- is T = Nr. Suppose, for example, that r = 1 s. If 1% accuracy is desired, N = 104 and so T = 104 s. 

When the analytical laboratory is not responsible for sampling, the quality management system often does not even take these weak links in the analytical process into account. Furthermore, if sample preparation (extraction, cleanup, etc.) has not been carried out carefully, even the most advanced, quality-controlled analytical instruments and sophisticated computer techniques cannot prevent the results of the analysis from being called into question. Finally, unless the interpretation and evaluation of results are underpinned by solid statistical data, the significance of these results is unclear, which in turn greatly undermines their merit. We therefore believe that quality control and quality assurance should involve all the steps of chemical analysis as an integral process, of which the validation of the analytical methods is merely one step, albeit an important one. In laboratory practice, quality criteria should address the rationality of the sampling plan, validation of methods, instruments and laboratory procedures, the reliability of identifications, the accuracy and precision of measured concentrations, and the comparability of laboratory results with relevant information produced earlier or elsewhere. 

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