In this video, I explain the control charts.
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Control charts, also known as Shewhart charts or process-behavior charts, are tools used in manufacturing and business processes to monitor how a process changes over time. They are a type of line graph that shows data points in order of occurrence. Here's a simple way to understand them:

Collect Data: You gather data from your process over a period. This could be anything like the number of defects found in manufactured parts each day.

Plot Data: You put this data on a chart with the data points connected by a line, usually over time.

Calculate Average: You calculate the average of the data you collected and draw a line at that average value on the chart.

Add Control Limits: You calculate the upper and lower control limits based on the average and the natural variability of the process and draw these as lines too. These limits represent the expected variation in data if the process is running without any special, unusual, or assignable causes of variation.

Analyze the Chart: By looking at the chart, you can see if your process is within the expected range of variation (between the control limits) or if something unusual is happening that you need to investigate.

In essence, a control chart helps you see if your process is under control and consistent, or if there are any problems you need to fix. It's like a weather map for your process: it can't predict the next storm, but it can tell you if you're currently in one.


Let's walk through an example using the control chart we just created to illustrate how it might be used in a real-world scenario:

Imagine you are overseeing the quality of light bulbs being produced in a factory. Your key metric of interest is the lifespan of these bulbs, and you want to ensure they last on average 1000 hours with a standard deviation of 100 hours. To monitor this, you measure the lifespan of a sample of bulbs each day.

Data Collection: Every day, you test 50 light bulbs and record their lifespan.

Plotting Data: The results of these daily tests are the data points you plot on the control chart.

Calculating Average and Limits: Based on historical data or initial samples, you calculate the average lifespan of the bulbs and set control limits. For our example, the average is 1000 hours, and the control limits might be 700 hours for the lower limit and 1300 hours for the upper limit (assuming a normal distribution, the control limits are set at ±3 standard deviations from the mean).

Monitoring: Each day's average lifespan is plotted on the chart. As long as the daily averages stay within the control limits, the process is considered to be in control.

Investigation and Action: If you observe any points outside the control limits (e.g., an average lifespan of 650 hours on a particular day), this signals that something may be wrong with the process. It could be due to a machine malfunction, poor quality raw material, or some other special cause. You would then investigate and take corrective action to bring the process back into control.

Continuous Improvement: Even when all the data points are within the control limits, you might observe a pattern or trend that suggests a potential improvement. For example, if the data points start trending downwards, even though they are within limits, you might want to check the process before it actually goes out of control.



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