248 Part 3 • Implementing Quality
FIGURE 10-8 Copier Problem Check Sheet
C 0
Problem Type
Setup routines not standardized
Missing equipment for setup
0
C
C E-4
Failure to separate internal and external tasks
Extensive machine resetting and paper change
Other
Check Sheets
Check sheets are data-gathering tools that can be used to provide data for histograms; they can be either tabular, computer based, or schematic. An example of a tabular check sheet for a Pareto chart is shown in Figure 10-8. It provides a chart for copier operators to mark each time a delay occurs in setting up new jobs. Setting up a check sheet involves the following steps: 1. Identify common defects occurring in the process. 2. Draw a table with common defects in the left column and time period across the tops of the columns (see Figure 10-8) to track the defects. 3. The user of the check sheet then places check marks on the sheet whenever the defect is encountered.
EXAMPLE 10-2
Check Sheets
Problem: A copying company wants to set up a check sheet so that it can keep track of error sources. Following are the major error types with frequencies. Solution: Figure 10-8 showed a check sheet for these data. The check sheet will be kept to moni-tor how well workers are adhering to the new procedures.
Type of Problem Setup routines are not standardized Equipment needed for setup is missing Internal and external setup tasks are not separated Extensive machine resetting and paper change are needed Other Total
Frequency Percentage_ 315 52.1% 124 20.5 87 14.4 56 9.2 23 3.8 0% 605 10
X
X
0.75 2.15 3.55 4.95 6.35 7.75 9.15 FIGURE 10-9 Histogram from Example 10-3
Chapter 10 • The Tools of Quality 249
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Histograms As shown in Figure 10-9, histograms are simply graphical representations of data in a bar format. (Note that a frequency chart is used for categorical data, whereas histograms are used for continu-ous numerical data.) Histograms are also used to observe the shape of data (see Figure 10-9). For example, how are the data in an interval scale distributed? There are several rules for developing histograms: • The width of the histogram bars must be consistent (i.e., class widths are the same where each bar contains a single class). • The classes must be mutually exclusive and all-inclusive (or collective exhaustive). • A good rule of thumb for the number of classes is given by the model 2k n
(10.1)
where n is the number of raw data values and k is the number of classes. Solving this equa-tion for k, we obtain
Using this formula, we find
EXAMPLE 10-3
k log n/log 2
Number of Observations Number of Classes 4 5 6 7 8
9 to 16 17 to 32 33 to 64 65 to 128 129 to 256
Histograms
(10.2)
problem. ’11. The Big City Cafeteria wants to determine the distribution of its sales during lunch-time. On On a given day, the manager randomly selects 40 sales from the sales register receipt. The ‘wing table shows the sales (in dollars):
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