4. Assessing Quality
Overview
Quality is the degree to which products or services meet the needs of customers. Common goals for quality professionals
include reducing defect rates, manufacturing products within specifications, and standardizing delivery time.
Minitab offers many methods to help you assess quality in an objective, quantitative way. These methods include control
charts, quality planning tools, measurement systems analysis (gage R&R studies), process capability, and reliability/survival
analysis. This chapter focuses on control charts and process capability.
You can customize Minitab's control charts in the following ways:
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Update the chart after you add or change data.
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Choose how to estimate parameters and control limits.
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Display tests for special causes and historical stages.
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Customize the chart, such as adding a reference line, changing the scale, and modifying titles.
You can customize control charts when you create them or later.
With Minitab's capability analysis, you can do the following:
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Analyze process data from many different distributions, including normal, exponential, Weibull, gamma, Poisson, and
binomial.
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Display charts to verify that the process is in control and that the data follow the chosen distribution.
The graphical and statistical analyses that you performed in the previous chapter show that the Western shipping center
has the fastest delivery time. In this chapter, you determine whether the Western shipping center’s process is in control
and is capable of operating within specifications.
Create and interpret control charts
Unusual patterns in your data indicate the presence of special-cause variation, that is, variation that is not a normal part
of the process. Use control charts to detect special-cause variation and to assess process stability over time.
Minitab control charts display process statistics. Process statistics include subgroup means, individual observations,
weighted statistics, and numbers of defects. Minitab control charts also display a center line and control limits. The center
line is the average value of the quality statistic that you choose to assess. If a process is in control, the points will vary
randomly around the center line. The control limits are calculated based on the expected random variation in the process.
The upper control limit (UCL) is 3 standard deviations above the center line. The lower control limit (LCL) is 3 standard
deviations below the center line. If a process is in control, all points on the control chart are between the upper and lower
control limits.
For all control charts, you can modify Minitab’s default chart specifications. For example, you can define the estimation
method for the process standard deviation, specify the tests for special causes, and display historical stages.
Create an Xbar-S chart
Create an Xbar-S chart to assess both the mean and variability of the process. This control chart displays an Xbar chart
and an S chart on the same graph. Use an Xbar-S chart when your subgroups contain 9 or more observations.
To determine whether the delivery process is stable over time, the manager of the Western shipping center randomly
selected 10 samples for 20 days.
1. Open the data set, Quality.MTW.
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