Control charts are decision-making tools. They provide an economic basis for mak

Question

Control charts are decision-making tools. They provide an economic basis for making a decision as to whether to investigate potential problems, to adjust the process, or to leave the process alone. Control charts are used in order to determine process capability and stability. Control charts allow production samples to be analyzed and compared to a list of specifications, where-in the data can be analyzed for stability within the control limits. The information gained from the charts allows the producer to know whether their process needs to be refined, changed, or left alone in order to meet specification and stability requirements.

Q: What is the first and most important attribute to control charts?

Explanation / Answer

Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior charts or statistical process control, are a statistical process control tool used to determine if a manufacturing or business process is in a state of control.

The control chart is a graph used to study how a process changes over time. Data are plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).

Control charts for variable data are used in pairs. The top chart monitors the average, or the centering of the distribution of data from the process. The bottom chart monitors the range, or the width of the distribution. If your data were shots in target practice, the average is where the shots are clustering, and the range is how tightly they are clustered. Control charts for attribute data are used singly.

When to Use a Control Chart:

1. When controlling ongoing processes by finding and correcting problems as they occur.

2. When predicting the expected range of outcomes from a process.

3. When determining whether a process is stable (in statistical control).When analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process).

4. When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.

If analysis of the control chart indicates that the process is currently under control (i.e., is stable, with variation only coming from sources common to the process), then no corrections or changes to process control parameters are needed or desired. In addition, data from the process can be used to predict the future performance of the process. If the chart indicates that the monitored process is not in control, analysis of the chart can help determine the sources of variation, as this will result in degraded process performance.A process that is stable but operating outside of desired (specification) limits (e.g., scrap rates may be in statistical control but above desired limits) needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process.

The control chart is one of the seven basic tools of quality control.Typically control charts are used for time-series data, though they can be used for data that have logical comparability (i.e. you want to compare samples that were taken all at the same time, or the performance of different individuals), however the type of chart used to do this requires consideration.

Attribute Control charts are used to regularly monitor a process to determine whether it is in control.When it is not possible to measure the quality of a product or service with continuous data,attribute data is often collected to assess its quality. The Minitab Assistant includes two widely used control charts to monitor a process with attribute data:

P chart: This chart is used when a product or service is characterized as defective or not defective. The P chart plots the proportion of defective items per subgroup. The data collected are the number of defective items in each subgroup, which is assumed to follow a binomial distribution with an unknown proportion parameter (p).

U chart or d-Chart: This chart is used when a product or service can have multiple defects and the number of defects is counted. The U chart plots the number of defects per unit. The data collected are the total number of defects in each subgroup, which is assumed to follow a Poisson distribution with an unknown mean number of defects per subgroup.

To help evaluate how well the control charts are performing, the Assistant Report Card automatically performs the following data checks:

1. Stability 2. Number of subgroups 3.Subgroup size 4. xpected Variation

The P chart and the U/d-chart depend on additional assumptions that either cannot be checked or are difficult to check.

So The most importent attribute to controlo chart is P-cahrt.

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