Statistical process controls, quality

Quality in Japan. Japanese economic prowess has been attributed variously to such quahty improvement activities as quahty circles, statistical process control (SPG), just-in-time dehvery (JIT), and zero defects (ZD). However, the real key to success hes in the apphcation of numerous quahty improvement tools as part of a management philosophy called Kaizen, which means continuous improvement (10). 

More details on statistical process control are available in several textbooks (Grant and Leavenworth, Statistical Quality Control, McGraw-HiU, New York, 1980 Montgomery, Introduction to Statistical Quality Control, Wiley, New York, 1985). 

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. 

The alternative is hexane, which because of the explosion hazard requires a more expensive type of extractor construction. After the extraction the product is dull gray. The continuos sheet is slit to the final width according to customer requirements, searched by fully automatic detectors for any pinholes, wound into rolls of about 1 m diameter (corresponding to a length of 900-1000 m), and packed for shipping. Such a continuous production process is excellently suited for supervision by modern quality assurance systems, such as statistical process control (SPC). Figures 7-9 give a schematic picture of the production process for microporous polyethylene separators. 

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. 

Berger, R.W., and Hart, T. (1986), Statistical Process Control A Guide for Implementation, ASQC Quality Press, Milwaukee, WI. 

QA, quality attributes SPC/MSPC, statistical process control/multivariate statistical process control CPPs, critical process parameters. 

It is vitally important that the multivariate nature of data related to a process be assessed to develop an understanding of a process and to assess quality. Process data together with appropriate chemometric models can provide information about (1) product quality inferentially from process conditions (2) process consistency (process signature, statistical process control) (3) analyzer reliability and (4) operational knowledge that can aid in scale-up and process transfers. ... 

Hradesky JL (1987) Productivity and Quality Improvement, A Practice Guide of Implementing Statistical Process Control McGraw Hill... 

Quality systems/statistical process control Toll chemistry Management of change Logistics Invoicing... 

In pharmaceutical technology, quality assurance of the pharmaceutical formulation is important. When a pharmaceutical formulation is produced, on-line quality monitoring and control has to be performed in order to check the quality of the outgoing products. Methodology to perform this task is Statistical Process Control (SPC) and is not included in this book. Good text books in the area of SPC exists [6-9]. In this book the focus is on off-line quality control, e.g. how to make products that are intrinsic robust against process variations. 

Section 9.2 will review traditional statistical process control/statistical quality control (SPC/SQC) techniques used in quality control. Section 9.3 will follow this review with a discussion of techniques based primarily on an experiential rule base and expert system technology. Section 9.4 will discuss control strategies that use an on-line process model a variety of models can be used in such model predictive control. Section 9.5 will discuss this variety of models. Section 9.6 will summarize this chapter and discusses future trends in the field. 

The traditional approach to quality control is to generate charts of various kinds to monitor the performance of a production unit. At a superficial level, statistical process control (SPC) and statistical quality control (SQC) [9] are terms used interchangeably to describe traditional... 

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. 

Ensure that vendors in-process controls include the use of statistical process control critical product parameters that are significant and may affect the final product quality... 

Assessment of process capability and statistical process control brings the ability to distinguish between a stable and un-stable process and provides a means to distinguish between different causes of variability, e.g., common cause, special cause, structural (e.g., seasonal), and tampering (e.g., deliberate or unintentional). Process understanding, quality by design and capability analysis can facilitate risk-based regulatory decisions for continuous improvements ... 

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. 

Smith, G. M. (2003), Statistical Process Control and Quality Improvement, 5th ed., Prentice-Hall, Upper Saddle Brook, NJ. 

Many of the quality improvement goals for implementation of PAT in the pharmaceutical industry have been achieved by companies in other industries, such as automobile production and consumer electronics, as a direct result of adopting principles of quality management. The lineage of modern quality management can be traced to the work of Walter Shewhart, a statistician for Bell Laboratories in the mid-1920s [17]. His observation that statistical analysis of the dimensions of industrial products over time could be used to control the quality of production laid the foundation for modern control charts. Shewhart is considered to be the father of statistical process control (SPC) his work provides the first evidence of the transition from product quality (by inspection) to the concept of quality processes [18,19]. 

Woodall, W H (2000), Controversies and contradictions in statistical process control, in 44th Annual Fall Technical Conference of the Chemical and Process Industries Division and Statistics Division of the American Society for Quality (Minneapolis, MN American Society for Quality), 1-10. Available http //www.asq.org/pub/jqt/past/vol32 issue4/qtec-341.pdf. 

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. 

Concurrent validation is conducted under a protocol during the course of normal production. The first three production-scale batches must be monitored as comprehensively as possible. The evaluation of the results is used in establishing the acceptance criteria and specifications of subsequent in-process control and final product testing. Some form of concurrent validation, using statistical process control techniques (quality control charting), may be used throughout the product manufacturing life cycle. 

QA = quality assurance QC = quality control SQC = statistical quality control SPC = statistical process control CIM = computer-integrated manufacturing. 

Statistical process control (SPC), also called statistical quality control and process validation (PV), represents two sides of the same coin. SPC comprises the various mathematical tools (histogram, scatter diagram run chart, and control chart) used to monitor a manufacturing process and to keep it within in-process and final product specification limits. Lord Kelvin once said, When you can measure what you are speaking about and express it in numbers, then you know something about it. Such a thought provides the necessary link between the two concepts. Thus, SPC represents the tools to be used, while PV represents the procedural environment in which those tools are used. 

If the pharmaceutical industry adopted the lessons learned in other branches (e.g., aircraft industry, automotive industry) it could realize an increased efficiency in the qualification and validation processes. To this end effort should be made to investigate statistical process control (SPC), house of quality, Deming circuits, and so on. 

In industrial plants, large numbers of process variables must be maintained within specified limits in order for the plant to operate properly. Excursions of key variables beyond these limits can have significant consequences for plant safety, the environment, product quality and plant profitability. Statistical process control (SPC), also called statistical quality control (SQC), involves the application of statistical techniques to determine whether a process is operating normally or abnormally. Thus, SPC is a process monitoring technique that relies on quality control charts to monitor measured variables, especially product quality. 

A common parameter used by quality professionals to gauge whether or not the manufacturing process and analytical methodology is within the desired state of statistical process control (SPC) is a process capability index [11], such as the Cpk value. The Cpk value is the value of the tolerance specified for the given performance characteristic divided by the actual process capability. 

Recent trends in the paper industry have had an impact on starch supplies. The implementation of statistical process control has led to more awareness that the production of high-quality paper grades depends on the use of high-quality materials. As a consequence, there is increased emphasis on acceptance specifications for starch supplies according to ISO guidelines and more rigorous testing in order to ensure consistently uniform quality. 

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. 

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. 

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