Chapter 10 Learning Objectives

1. List and briefly explain the elements of the control process (p436).

The elements of the control process are:

Define: To define in sufficient detail what is to be controlled.

Measure: Only those characteristics that can be counted or measured are candidates for control. It is important to consider how measurement will be accomplished.

Evaluate: Management must establish a definition of out of control. The main task of quality control is to distinguish random from nonrandom variability, because nonrandom variability means that a process is out of control.

Correct: When a process is out of control, corrective action must be taken. This involves uncovering the cause of nonrandom variability and correcting it.

Monitor results: To ensure that corrective action is effective, the output of a process must be monitored for a sufficient period of time to verify that the problem has been eliminated.

2. Explain how control charts are used to monitor a process and the concepts that underlie their use (p439).

Mean and range charts are used to monitor variables. Control charts for means monitor the central tendency of a process, and range charts monitor the dispersion of a process. Mean control charts and range control charts provide different perspectives on a process. The logic of using both charts is that the mean chart picks up the shift in the process mean, but because the dispersion is not changing, the range chart fails to indicate a problem. Also, a change in process dispersion is less apt to be detected by the mean chart than by the range chart. Thus use of both charts provides more complete information than either chart alone.

P-chart and c-charts are used to monitor attributes. P-charts are used to monitor the proportion of defective items generated by a process. On the other hand, c-charts are used when the goal is to control the number of occurrences.

3. Use and interpret control charts (p442).

1. Obtain 20-25 samples. Compute the appropriate sample statistic(s) for each sample (e.g. mean).

2. Establish preliminary control limits using the formulas

3. Determine if any points fall outside the control limits.

4. If you find no out-of-control signals, assume that the process is in control. If not, investigate and correct assignable causes of variation. Then resume the process and collect another set of observations upon which control limits can be based.

5. Plot the data on the control chart and check out-of-control signals.

4. Use run tests to check for nonrandomness in process output (p447).

A run test is used to test randomness. When a process is stable or in statistical control, the output it generates will exhibit random variability over a period of time. The presence of patterns in the output indicates that assignable, or nonrandom, causes of variation exist. Therefore a run test is used to detect any patterns.

First compute the number of standard deviations, z, by which an observed number of runs differ from the expected number.

Z(test) = Observed number of runs – Expected number of runs

Standard Deviations of number of runs

For the median and up/down tests, you could also find z using these formulas:

Median: z= r – [(N/2) + 1]

√(N – 1)/4

Up and Down: z=r – [(2N – 1)/3]

√(16N – 29)/90

If either test pick up a certain pattern, the implication would be that some sort of nonrandomness is present in the data.

5. Assess process capability (p451).

The term process capability refers to the inherent variability of process output relative to the variation allowed by the design specifications. Improving process capability requires changing the process target value and/or reducing the process variability that is inherent in a process. This might involve simplifying, standardizing, making the process mistake-proof, upgrading equipment, or automating.