Tips and Techniques for Statistics and Quality Improvement

Blog posts and articles about using Minitab software in quality improvement projects, research, and more.

Trying to solve a complex problem? You should begin by listing all suspected variables. Then, identify the few critical factors and separate them from the others that are not essential to understanding the cause. Let’s take a look at a very simple graphical tool that is very intuitive, can be used by virtually anyone, and does not require any prior statistical knowledge: the Multi-Vari Chart.

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The skills to understand and work with data — and make educated decisions based on it — are in demand and they are not going away anytime soon. Let's take a look at some new directions we've seen in the workforce over the last few years, as well as how executives are treating upskilling their teams as a top priority.
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Suppose that you plan to source a substantial amount of parts or subcomponents from a new supplier. To ensure that their quality level is acceptable to you, you might want to assess the capability levels (Ppk and Cpk indices) of their manufacturing processes and check whether their critical process parameters are fully under control (using control charts). If you are not sure about the efficiency of the supplier...

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In the mid 1940s, Taiichi Ohno established the Toyota Production System, which is primarily based on eliminating non-value-added waste. He discovered that by reducing waste and inventory levels, problems get exposed and that forces employees to address these problems. To engage the workers and therefore improve processes, Ohno developed many exercises.

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In Parts 1 and 2 of Gauging Gage we looked at the numbers of parts, operators, and replicates used in a Gage R&R Study and how accurately we could estimate %Contribution based on the choice for each.  In doing so, I hoped to provide you with valuable and interesting information, but mostly I hoped to make you like me.  I mean like me so much that if I told you that you were doing something flat-out wrong and had been...

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In Part 1 of Gauging Gage, I looked at how adequate a sampling of 10 parts is for a Gage R&R Study and providing some advice based on the results.

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"You take 10 parts and have 3 operators measure each 2 times."

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In the first part of this series, we saw how conflicting opinions about a subjective factor can create business problems. In part 2, we used Minitab's Assistant feature to set up an attribute agreement analysis study that will provide a better understanding of where and when such disagreements occur. 

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Previously, I discussed how business problems arise when people have conflicting opinions about a subjective factor, such as whether something is the right color or not, or whether a job applicant is qualified for a position. The key to resolving such honest disagreements and handling future decisions more consistently is a statistical tool called attribute agreement analysis. In this post, we'll cover how to set up...

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People frequently have different opinions. Usually that's fine—if everybody thought the same way, life would be pretty boring—but many business decisions are based on opinion. And when different people in an organization reach different conclusions about the same business situation, problems follow. 

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