How to interpret defects data in Control Charts?
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What is control chart data? I’m going to explain with real-life examples from my past experience. Let’s say you have 100 orders received, and 25 orders were rejected. You have a specific metric — rejection rate. Control chart is a tool for interpreting defects data. 1. Identify critical zone: The control chart plots the control limit (CL) — area under the horizontal bar (blue bar) with respect to x-axis (rejection rate) that will not trigger the error bars (vertical bars). Control limit (
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- Defects data Defects data tells the percentage of defective products produced. When compared to a targeted design, defects in a product would not meet the desired quality standard. Defects data is essential to product quality. It helps in making quality improvement recommendations, identify product-related causes, and estimate the cost of corrective actions. Let’s understand defects data in more detail: 1.1 Defect frequency A measure of how often defects occur in a test run or production line. This is commonly called
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Today, I have a topic you may not have thought of. And this is one that has been debated for years — How to interpret defects data in Control Charts? There are so many articles and blog posts on this topic. And I am afraid to say, most of them are wrong! Why? Because most authors don’t know how to interpret defects data in Control Charts correctly. The best way to do that? By asking yourself three basic questions — are the controls as effective as the treatment? Is there a difference in control-effectiveness between the highest
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In Control Charts, defects data can be defined as the number of failures or out-of-specifications for a given production run, sample, or a product group. A defect is an imperfection in a process, product, or device that falls outside the specifications or a standard of quality. The specific criteria for defect are: 1. Identify defects. They could be single or a combination of defects; this could be a defect, which is found at a single site, or a group of defects spread across different sites, units, or components
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One way of interpreting defects data in control charts is by examining the histogram of the defects. As you can see from the chart below, the average and standard deviation of the defects were high. view it now A typical control chart has a “C” shape with an outlier on the right side (where the data is highest). As the defects increase in a sample, they move toward the “C” shape. However, this is an oversimplification of the concept. Let’s look at what this means in practice. To interpret the defects data
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In Control Charts, you use horizontal lines for different observations. The horizontal lines are known as defect lines. If defects are significant, then the lower defect line is red, and the upper defect line is green. Here’s how to interpret defects data in Control Charts: Firstly, we calculate the mean defect from the control data: Assuming all defects are of type A, where: A is an arbitrary defect level for a product line Then, we calculate the standard deviation of defects using the mean defect: We now
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In the context of data, defects are the quality deviations from the expected values. For the Control Charts, when an effect (good, bad or no good) is detected, a line or an area is drawn between the expected value (baseline) and the actual value (current observation). This line is the control chart line. The line of control is drawn so that it is at the extreme right side of the plot. The x-axis gives the time in units of days, hours, minutes or seconds. The y-axis