Special control charts

The control charts discussed earlier are very useful in the diagnostic aspects of quality process improvement. They can be used to stabilize a process by identifying out-of-control situations. After the process is stabilized and brought in control, further improvement of the process can be achieved by using some special control charts such as the cumulative sum (CUSUM) control chart and the exponentially weighted moving average (EWMA) control chart. These control charts can be used when small shifts in a process are of interest. 

Special attention should be paid to one-sided deviation from the control limits, because systematic errors more often cause deviation in one direction than abnormally wide scatter. Two systematic errors of opposite sign would of course cause scatter, but it is unlikely that both would have entered at the same time. It is not necessary that the control chart be plotted in a time sequence. In any... 

The Cusum Control Chart is a very special chart from which a lot of information can be drawn. Cusum is the abbreviation for cumulative sum and means the sum of all differences from the target value. Every day the difference of the control analysis from the target value is added to the sum of all the previous ddferences. 

Applications of Control Charts Control charts serve to direct management attention toward special causes of variation in a process when they appear. In evaluating control charts, the following symptoms could indicate a process that is out-of-control ... 

Trueness or exactness of an analytical method can be documented in a control chart. Either the difference between the mean and true value of an analyzed (C)RM together with confidence limits or the percentage recovery of the known, added amount can be plotted [56,62]. Here, again, special caution should be taken concerning the used reference. Control charts may be useful to achieve trueness only if a CRM, which is in principle traceable to SI units, is used. All other types of references only allow traceability to a consensus value, which however is assumed not to be necessarely equal to the true value [89]. The expected trueness or recovery percent values depend on the analyte concentration. Therefore, trueness should be estimated for at least three different concentrations. If recovery is measured, values should be compared to acceptable recovery rates as outlined by the AOAC Peer Verified Methods Program (Table 7) [56, 62]. Besides bias and percent recovery, another measure for the trueness is the z score (Table 5). It is important to note that a considerable component of the overall MU will be attributed to MU on the bias of a system, including uncertainties on reference materials (Figures 5 and 8) [2]. 

When fewer than about 100 measurements of the same type are needed, the use of control charts becomes impractical. A few repeat measurements made within the routinely encountered range of relevant values is sufficient to estimate the repeatability of a single measurement. Difficulty arises only when a measurement type or procedure is inordinately time-consuming or costly to replicate. Relevant examples are the measurement of an unusual trace constituent in a sample of minimal size, and a lengthy isotope dilution mass-spec-trometric determination. The analyst is then required to depend on general experience of reliability of a method and would be wise to estimate the uncertainty with special care. 

The major objective in SPC is to use process data and statistical techniques to determine whether the process operation is normal or abnormal. The SPC methodology is based on the fundamental assumption that normal process operation can be characterized by random variations around a mean value. The random variability is caused by the cumulative effects of a number of largely unavoidable phenomena such as electrical measurement noise, turbulence, and random fluctuations in feedstock or catalyst preparation. If this situation exists, the process is said to be in a state of statistical control (or in control), and the control chart measurements tend to be normally distributed about the mean value. By contrast, frequent control chart violations would indicate abnormal process behavior or an out-of-control situation. Then a search would be initiated to attempt to identify the assignable cause or the. special cause of the abnormal behavior... 

Shewhart s basic conceptualization of common and special cause variation not only leads to control charts as quantitative, rational tools to guide one in knowing when (and when not ) to intervene in an industrial process to correct potential ills, but it also provides a framework for considering the question of what is the best/most consistent performance one can hope for from a particular version of a process. That is, it provides a framework for discussing process capability assessment. 

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. 

During Phase III, the process is monitored for instances of special causes. When special causes cannot be entirely eliminated, an Out-of-Control Action Plan (OCAP) can be developed for routine use by operating personnel.The OCAP comprises three features activators, checkpoints, and terminators. The activators define the conditions that signal when the OCAP must be activated, and the control chart usually performs this function. The checkpoints instruct the operator to investigate specific items as possible special causes for the out-of-control condition. Once a checkpoint has been identified as a special cause, a terminator caih for a specific action to be applied to resolve the problem. 

Control charts are an excellent analysis tool to both monitor and improve in-process performance during process development and later during production, where it is desired to follow process characteristics over time within batches or runs. The most common examples of tablet process characteristics that are measured in-process are weight, thickness, and hardness. The parameters measured need to be controllable so that adjustments can be made. During the initial runs, it is desirable to limit process adjustments to a minimum to observe the process in its natural state. Any adjustments made should be recorded and explained. Out-oflimit results should never be removed prior to performing a process capability analysis. If special cause variation is detected, then process improvements should be made to eliminate the special cause variation. 

The individual observations in sequential order are compared with control limits established from a past measurement to generate the control chart in the initial case. If the mean value x and standard deviation a of a constant quantity have been established from 15 to 30 measurements, these quantities may be regarded as valid estimates of x and action limits and these limits are usually set, and are based on the sensitivity and importance of the measurements. Special attention should be paid to one-sided deviations from control limits, because systematic errors cause deviation in one direction and may indicate an abnormally wide scatter. Therefore laboratories, production, test methods, or operator can be checked for consistency of measured results. 

DO-IT-ALL has a quality control laboratory that monitors the specific gravity of their product daily in order to discover problems with their production. They have provided us with their SOP for this work. Your supervisor has acquired their control charting information, which was certified as genuine by the FFB. We have enlisted the help of Fred Buggs, Ph.D., as our industrial consultant, and Professor A1 Cohall of DeLute University as our university consultant. Their assessment of the situation and recommendations are attached. Please pay special attention to Dr. Buggs comments, because they explain what control charts are. Good Luck ... 

The original methods have been extended in many ways. The design of control charts is always prospective, and their shape depends upon the a priori expectations of the development team. For example, when it is important to test only the tolerability of a compound, the chart can have an open top this is when it is important to the development team to detect drug toxicity early, but not efficacy. Similarly, depending upon the hypotheses under test, control charts can be rhomboidal, parallelogram-shaped, or many other shapes. White-head (1999) is the best entry to the literature on this specialized topic. 

Control chart Distinguish between special and common causes of variation. 

What A control chart is a tool for studying variation in data, distinguishing between common cause and special cause variation. 

Adjustment to reduce the variability of a stable process, that is, one whose output is dominated by common causes, will make the performance worse. Improvement of a stable process is achieved through a fundamental change in the process that results in the removal of some of the common causes. If a special cause is found and will persist for some time, for example a lot of raw material, an adjustment of the process to counteract the special cause may be helpful in the short term. The control chart is an important tool to help the operator know when an adjustment to the process is needed. 

A control chart of important measures such as costs, materieil usage, volume of production, sales and profit, and an analysis of the capability of the process (if the process is stable) communicates a realistic view of the performance of the process. Without the aid of a control chart and an understanding of the concept of common and special causes of variation, the tools for planning are mistaken for reality or the capability of the process. Workers or other managers are often asked to conform to that reality. If the salesman does not meet the forecast, his performance is unacceptable. When the production worker does not achieve the production standard, his performance is unacceptable. 

In developing the control chart method, Shewhart emphasized the importance of the economic balance between looking for special causes when they do not exist and overlooking special causes that do exist. It is also necesssary to develop rules that wiU give an acceptable economic balance for aU types of measures in a variety of systems, processes, and products. Figure 6 illustrates the impact of these two mistakes. 

The control chart provides a basis for taking action to improve a process. A process is considered to be stable when there is a random distribution of the plotted points within the control limits. For a stable process, action should be directed at identifying the important causes of variation common to aU of the points. If the distribution (or pattern) of points is not random, the process is considered to be unstable and action should be taken to learn about the special causes of variation. 

Every control chart should be associated with one or more specific objectives. The objective might be to improve the yield of the process, identify and remove special causes from a process, or establish statisticsd control so that the capahUity of the process can be determined. The objectives should be summarized on the control chart form. After a period of time, the objective may be met. The control chart should be discontinued at that time, or a new objective developed. 

The documentation of information about the process is a most important part of many control charts. This documentation includes changes in the process, identification of special causes, investigations of special causes, and other relevant process data. Flow charts and cause-and-effect diagrams can be used to identify particular notes that should be recorded. Responsibility for recording this critical information should be clearly stated. 

A plan for reaction to special causes on the chart should be established. Often a checklist of items to evaluate or a flow chart of the steps to follow is useful. The reaction plan should state the transfer of responsibility for identification of the special cause if it cannot be done at the local level. A plan for reaction to special causes on the chart should be established. Often a checklist of items to evaluate or a flow chart of the steps to follow is useful. The reaction plan should state the transfer of responsibility for identification of the special cause if it cannot be done at the local level. As an example, a reaction plan for a control chart in a laboratory to monitor a measurement system might have the following reaction plan ... 

When the initial control chart has special causes and there is a desire to use the calculated limits for analysis of data to be collected in the future. In this case, control Unfits should be recalculated after removing the data associated with the special causes. 

When improvements have been made to the process and the improvements result in special causes on the control chart. Control hmits should than be calculated for the new process. 

When the control chart remains out of control for an extended period of time (20 or more subgroups) and approaches to identify and remove the special cause(s) have been exhausted. Control limits should be recalculated to determine if the process has stabiUzed at a chffeient operating level. 

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