Statistical process control methods

Statistical process control methods are applied to preparative chromatography for the case where cut points for the effluent fractions are determined by on-line species-specific detection (e.g., analytical chromatography). A simple, practical method is developed to maximize the yield of a desired component while maintaining a required level of product purity in the presence of measurement error and external disturbances. Relations are developed for determining tuning parameters such as the regulatory system gain. 

Statistical process control methods are applied to preparative chromatography for the case where effluent cut points are determined by online species-specific detection. In particular, Equation 14 (with the quantity in... 

The identification of the fall off in plant output uses the same statistical process control methods as for product quality [D-4]. Usually, and certainly in the larger manufacturing units, these issues will be handled by the local plant support teams. However, sometimes output issues arise which are outside the more routine evolutionary techniques employed by the process control teams. A typical example is when the output from a process is constrained by a particular plant item. An improved piece of equipment needs to be identified and evaluated. The introduction of this equipment will usually necessitate process changes for maximum efficiency. This and similar packages of work are best done by an R D project team. 

The identification of chemical finishes on fabrics can serve several purposes. Often a fabric with an unknown finish needs to be analyzed for forensic or competitive reasons. Fabrics that do not perform as expected need to be analyzed to determine if the correct finish was applied in the proper concentration. Regular analysis of production fabrics forms a basis for process improvement through application of statistical process control methods. 

FW Faltin and WH Woodall. Some statistical process control method-s for autocorrelated data - discussion. J. Quality Technology, 23 194-197, 1991. 

Kourti, T., and J. F. MacGregor, 1996. Multivariate Statistical Process Control methods for Monitoring, Diagnosing Process and Product Performance. J. Qual. Tech. 28 409-428. 

Statistical process control methods may be used to demonstrate that a process had been validated (i.e., is in a state of control). The control chart method of analysis and presentation of data may for instance be used to document the variations that occur in the central tendency and dispersion of a set of observations relating to a specific quality characteristic. 

Montgomery, D. C., and Mastrangelo, C. M. (1991), Some Statistical Process Control Methods for Autocorrelated Data, Journal of Quality Technology, Vol. 23, No. 3, pp. 179-193. 

Safety professionals involved in quality management have had to become informed on statistical process control methods. I suggest that a broader application of those methods by our profession to measure safety performance would be beneficial. 

The measured output of an unstable process is illustrated in Figure 2. The pattern of variation is no longer as obvious as in the case of the conceptual process, yet in order to monitor and improve quality one must extract from such data, clues concerning the three types of variations. This is accomplished with the help of SPC methods. Statistical Process Control methods are a group of procedures which allows us to use statistics to analyze quality data and to identify the types of variations. One simple method is to fit a polynomial to the data. The fitted curve is then an estimate of the process drift. 

Walter A. Shewhart was a Bell Laboratories scientist and friend and mentor of Deming. Shewhart is credited with having developed a Statistical Process Control Method in the late 1920s. Thus, the origin of the PDCA concept lies in statistical process control, a methodology developed to address the need for improvement in product quality. The emphasis of the PDCA concept with respect to product quality applications is process control and continual improvement. That is also the case in ZIO. The words process and processes and the phrase continual improvement appear in ZIO over 60 times. 

Because SPC data is often non-IID and exhibits systematic time effects, i.e., process problems, traditional SPC methods are often not reliable in detecting special causes. " Use of time series based process control methods, rather than standard statistical process control methods, are thus appropriate when data... 

Manufacturing processes have been improved by use of on-line computer control and statistical process control leading to more uniform final products. Production methods now include inverse (water-in-oil) suspension polymerization, inverse emulsion polymerization, and continuous aqueous solution polymerization on moving belts. Conventional azo, peroxy, redox, and gamma-ray initiators are used in batch and continuous processes. Recent patents describe processes for preparing transparent and stable microlatexes by inverse microemulsion polymerization. New methods have also been described for reducing residual acrylamide monomer in finished products. 

Statistical Control. Statistical quahty control (SQC) is the apphcation of statistical techniques to analytical data. Statistical process control (SPC) is the real-time apphcation of statistics to process or equipment performance. Apphed to QC lab instmmentation or methods, SPC can demonstrate the stabihty and precision of the measurement technique. The SQC of lot data can be used to show the stabihty of the production process. Without such evidence of statistical control, the quahty of the lab data is unknown and can result in production challenging adverse test results. Also, without control, measurement bias cannot be determined and the results derived from different labs cannot be compared (27). 

While the single-loop PID controller is satisfactoiy in many process apphcations, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods hereafter that can be implemented via computer control, namely feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. 

Several methods have evolved to achieve, sustain, and improve quality, they are quality control, quality improvement, and quality assurance, which collectively are known as quality management. This trilogy is illustrated in Figure 2.1. Techniques such as quality planning, quality costs, Just-in-time , and statistical process control are all elements of... 

You should review the contract and the detail specifications to identify whether your existing controls will regulate quality within the limits required. You may need to change the limits, the standards, the techniques, the methods, the environment, and the instruments used to measure quality characteristics. One technique may be to introduce Just-in-time as a means of overcoming storage problems and eliminating receipt inspection. Another technique may be Statistical Process Control as a means of increasing the process yield. The introduction of these techniques needs to be planned and carefully implemented. 

Statistical process control (SPC) is an important on-line method in real time by which a production process can be monitored and control plans can be initiated to keep quality standards within acceptable limits. Statistical quality control (SQC) provides off-line analysis of the big picture such as what was the impact of previous improvements. It is important to understand how SPC and SQC operate. 

Unlike SPC techniques, standard feedback control methods such as PID-control, do exert control upon a process, in an effort to minimize y, — yk. Control in Statistical Process Control is as such not regulatory control, but a semantic means of relating SPC to quality control—a means that often leads to the hybrid term SQC. Ogunnaike and Ray [14, Sec. 28.4] offer advice on when to use SPC and when to use standard feedback control methods When the sampling interval is much greater than the process response time, when zero-mean Gaussian measurement noise dominates process disturbances, and when the cost of regulatory control action is considerable, SPC is preferred. 

One approach for using DOE on more complex processes is to do the majority of the process development on smaller, representative sections of material, such as test panels, rather than on full-scale parts, and then to scale up with a more limited experimental matrix. There is no guarantee that experience on small-scale test panels will directly translate to large parts because dimensions and thickness of the part are important variables in their own right. Another way to save on costs is to start with a satisfactory process and to continue, via careful monitoring of process variations and results, to extend the range of experience. This method is variously called statistical process control or statistical quality control. 

The PAT guidance facilitates introduction of new measurement and control tools in conjunction with well-established statistical methods such as design of experiments and statistical process control. It, therefore, can provide more effective means for product and process design and control, alternate efficient approaches for quality assurance, and a means for moving away from the corrective action to a continuous improvement paradigm. 

Each manufacturer of a packaging component sold to a drug product manufacturer should provide a description of the quality control measures used to maintain consistency in the physical and chemical characteristics of the component. These measures generally include release criteria (and test methods, if appropriate) and a description of the manufacturing procedure. If the release of the packaging component is based on statistical process control, a complete description of the process (including control criteria) and its validation should be provided. 

The production of material of a consistent quality is one of the major goals of development work. Quality problems in a product are identified by the constant monitoring and analysis of the output from the plant, using statistical process control techniques [D-4]. Some of these methods have already been mentioned in Section B, 3.4.2. The avoidance of product quality problems results in direct cost benefits and also brings about a reduction in the environmental impact of its manufacture. This is because material does not need to be reworked, recycled or sent for disposal. A reduction in the number of inferior quality batches of material leads to an increase in output from the plant. More material is produced for the same effort, with the added benefit that it can be consistently supplied to the sales warehouse or be used in consuming processes. 

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