Production statistical control

The control chart is set up to answer the question of whether the data are in statistical control, that is, whether the data may be retarded as random samples from a single population of data. Because of this feature of testing for randomness, the control chart may be useful in searching out systematic sources of error in laboratory research data as well as in evaluating plant-production or control-analysis data. ... 

Control charts were originally developed in the 1920s as a quality assurance tool for the control of manufactured products.Two types of control charts are commonly used in quality assurance a property control chart in which results for single measurements, or the means for several replicate measurements, are plotted sequentially and a precision control chart in which ranges or standard deviations are plotted sequentially. In either case, the control chart consists of a line representing the mean value for the measured property or the precision, and two or more boundary lines whose positions are determined by the precision of the measurement process. The position of the data points about the boundary lines determines whether the system is in statistical control. 

Statistical Process Control. A properly miming production process is characterized by the random variation of the process parameters for a series of lots or measurements. The SPG approach is a statistical technique used to monitor variation in a process. If the variation is not random, action is taken to locate and eliminate the cause of the lack of randomness, returning the process or measurement to a state of statistical control, ie, of exhibiting only random variation. 

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). 

Have all production processes (final hatch and in-process parameters) heen shown to he m statistical control ... 

Process capability studies are studies conducted to obtain information about the inherent variation present in processes that are under statistical control, in order to reduce the spread of variation to less than the tolerances specified in the product specification. 

Preliminary process capability studies are those based on measurements collected from one operating run to establish that the process is in statistical control and hence no special causes are present. Studies of unpredictable processes and the determination of associated capability indices have little value. Preliminary studies should show acceptable results for special characteristics before production approval can be given. These studies and associated indices only apply to the measurement of variables and not to attributes (see below). 

It is only possible to supply parts with identical characteristics if the measurement system as well as the production processes are under statistical control. In an environment in which daily production quantities are in the range of 1,000 to 10,000 units, inaccuracies in the measurement system that go undetected can have a disastrous impact on customer satisfaction and hence profits. 

For an example of a control chart see Fig. 1.31 and Sections 4.1 and 4.8. Control charts have a grave weakness the number of available data points must be relatively high in order to be able to claim statistical control . As is often the case in this age of increasingly shorter product life cyeles, decisions will have to be made on the basis of a few batch release measurements the link between them and the more numerous in-process controls is not necessarily straight-forward, especially if IPC uses simple tests (e.g. absorption, conductivity) and release tests are complex (e.g. HPLC, crystal size). 

Figure 16 shows the histogram of the data in relation to the specifications. The x and s charts in Figure 11 show that the process is in statistical control. However, since Cp < Cpk, the process is not centered. With a Cpk value of 0.579, it is expected to have 53,711 nonconforming Pet Tabs manufactured out of one million parts in this production line. 

Problems concerning the acceptance (tolerance) of color differences (e.g., in production quality control or in computer color matching) should also be solved by mathematical statistics [1.16]. 

According to the FDA, assurance of product quality is derived from careful and systemic attention to a number of important factors, including selection of quality components and materials, adequate product and process design, and (statistical) control of the process through in-process and end-product testing. 

The reader should realize that there is no one way to establish proof or evidence of process validation (i.e., a product and process in control). If the manufacturer is certain that its products and processes are under statistical control and in compliance with CGMP regulations, it should be a relatively simple matter to establish documented evidence of process validation through the use of prospective, concurrent, or retrospective pilot and/or product quality information and data. The choice of procedures and methods to be used to establish validation documentation is left with the manufacturer. 

This approach is made possible if the process (step) is demonstrated to be under a state of statistical control. A number of tests were listed by Ekvall and Juran to learn whether or not this condition exists. One approach to validating the technique involves the comparison of the process capability curve with the tolerance limits for the product. The intent of the validation is to determine whether or not the data from the process conform to the state of statistical control. It may also be used to determine whether or not quality costs can be reduced without changing the process s status. 

Statistical process control (SPC) provides a statistical approach for evaluating processes and for improving the quality of these processes through elimination of special causes. When SPC is effectively implemented within a company, benefits can be derived through a reduced cost of manufacture, improved quality, fewer troubleshooting crises, and improved relationships with customers. Process capability is a companion tool—one that can be used once a state of statistical control is achieved—to assess the performance of a process relative to its product specifications. Process capability can be used to determine whether processes are capable of continually operating within their stated specification limits. 

In any process, regardless of how well designed or maintained, a certain amount of natural or inherent variability will exist. This natural variability has been called a stable system of chance causes. A process operating with only chance causes is said to be in statistical control, and the variation is said to be because of common causes. Statistical process control evaluates whether or not a process is in statistical control with respect to one or more process or product characteristics. 

An example of a Shewhart Chart is shown below for a hypothetical powder fill process. Five vials of product were sampled every hour and the net content of each vial was determined. The Shewhart Chart is shown in Fig. 1. The Average Chart indicates lack of statistical control at subgroups 3, 4, 9, 14, and 17. Further study of the Average Chart indicates a possible shift in the process mean at subgroup 12, and the Range Chart shows an increase at subgroup 6. Subsequent special cause investigation determined that the shift in process... 

Bringing processes into or near a state of statistical control will improve processes by making them less variable, centered closer to target, and allow the manufacturer to make a product that will more consistently meet product specifications. This benefits both the manufacturer and the consumers who use their products. The use of SPC methods to evaluate and to improve processes not only can be applied to product characteristics such as tablet weight and tablet hardness, but also to product performance measures such as consumer complaints, line down time, and industrial safety measurements. An SPC approach to process improvement can also lead to reductions in fill overages, reductions in waste, as well as reductions in batch failures. By eliminating special cause variability, it becomes easier to monitor a process to ensure that new special causes do not find their way into the process. 

The use of common pharmaceutical ingredients and the tendency to gang test assays in the laboratory may prevent many of our end-product attributes from being in strict statistical control when measured over time. Therefore it is not recommended that a validation study or Annual Product Review require that a process be in statistical control to be considered acceptable, not even for in-process parameters such as tablet weight, thickness, and hardness. Processes that are not in a state of strict statistical control can be capable of consistently meeting specifications and can be validated. However, if processes are not in statistical control, efforts should always be made to eliminate special causes and get them into as near a state of statistical control as possible. The validation and Annual Product Review data can even be helpful in determining how processes can be improved. 

As discussed earlier, processes in the pharmaceutical industry may be difficult to always get into a state of strict statistical control. Interdependencies may exist between attributes such as the weight, thickness, and hardness of in-process tablets because of product flow characteristics within the batch. Data from sequentially generated batches may be dependent because of the use of common raw materials, or being tested on the same day. But the benefits derived from using the techniques discussed in this entry to improve existing processes and to keep them imder control are many. The effort is worth the hard work required to do it. 

Control chart A plot that demonstrates statistical control of a product or a service as a function of time. 

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