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where k is the distance of the control limits from the center line, expressed in standard deviation units. A common choice is k 3. This general theory of control charts was rst proposed by Dr. Walter A. Shewhart, and control charts developed according to these principles are often called Shewhart control charts. The control chart is a device for describing exactly what is meant by statistical control; as such, it may be used in a variety of ways. In many applications, it is used for online process monitoring. That is, sample data are collected and used to construct the control chart, and if the sample values of x (say) fall within the control limits and do not exhibit any systematic pattern, we say the process is in control at the level indicated by the chart. Note that we may be interested here in determining both whether the past data came from a process that was in control and whether future samples from this process indicate statistical control. The most important use of a control chart is to improve the process. We have found that, generally 1. 2. Most processes do not operate in a state of statistical control. Consequently, the routine and attentive use of control charts will identify assignable causes. If these causes can be eliminated from the process, variability will be reduced and the process will be improved.

If you did not select the correct time slot in Step 3, you can click the Start time and click a start time.

This process-improvement activity using the control chart is illustrated in Fig. 16-2. Notice that:

* Note that sigma refers to the standard deviation of the statistic plotted on the chart (i.e., deviation of the quality characteristic.

With the HTML page, the DataGrid tag can contain a columns collection. Another method of adding the EID column is by adding a template column tag in the DataGrid HTML tag. The following code is an example of the added template column. This code is similar to the DataList template code:

The control chart will only detect assignable causes. Management, operator, and engineering action will usually be necessary to eliminate the assignable cause. An action plan for responding to control chart signals is vital.

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In identifying and eliminating assignable causes, it is important to nd the underlying root cause of the problem and to attack it A cosmetic solution will not result in any real, long-term process improvement Developing an effective system for corrective action is an essential component of an effective SPC implementation We may also use the control chart as an estimating device That is, from a control chart that exhibits statistical control, we may estimate certain process parameters, such as the mean, standard deviation, and fraction nonconforming or fallout These estimates may then be used to determine the capability of the process to produce acceptable products Such process capability studies have considerable impact on many management decision problems that occur over the product cycle, including make-or-buy decisions, plant and process improvements that reduce process variability, and contractual agreements with customers or suppliers regarding product quality.

<asp:DataGrid id= DataGrid1 runat= server > <columns> <asp:TemplateColumn HeaderText= Employee ID > <itemtemplate> <%# string.Format( {0:D4} ,Container.DataItem.EID ) %> </ItemTemplate> </asp:TemplateColumn> </Columns> </asp:DataGrid>

Control charts may be classi ed into two general types Many quality characteristics can be measured and expressed as numbers on some continuous scale of measurement In such cases, it is convenient to describe the quality characteristic with a measure of central tendency and a measure of variability Control charts for central tendency and variability are collectively called variables control charts The X chart is the most widely used chart for monitoring central tendency, whereas charts based on either the sample range or the sample standard deviation are used to control process variability Many quality characteristics are not measured on a continuous scale or even a quantitative scale In these cases, we may judge each unit of product as either conforming or nonconforming on the basis of whether or not it possesses certain attributes, or we may count the number of nonconformities (defects) appearing on a unit of product.

Control charts for such quality characteristics are called attributes control charts Control charts have had a long history of use in industry There are at least ve reasons for their popularity: Control charts are a proven technique for improving productivity A successful control chart program will reduce scrap and rework, which are the primary productivity killers in any operation If you reduce scrap and rework, productivity increases, cost decreases, and production capacity (measured in the number of good parts per hour) increases 2 Control charts are effective in defect prevention The control chart helps keep the process in control, which is consistent with the do it right the rst time philosophy It is never cheaper to sort out the good units from the bad later on than it is to build them correctly initially.

Visual Studio .NET also provides a property builder that can be used to insert columns via GUI windows. The property builder can be accessed by right-clicking the DataGrid and then clicking Property Builder. Figure 6.12 shows the Property Builder screen.

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