Four-Cornered Leadership: A Framework for Making Decisions by John Roland Schultz

Author:John Roland Schultz [Schultz, John Roland]
Language: eng
Format: mobi
Publisher: Productivity Press
Published: 2013-09-02T14:00:00+00:00

Understand the Variability of Work  ◾  115

3500

3000

June

2500

May

2000

April

1500

March

1000

February

January

500

0

Product A

Product B

Product C

Product D

Figure 5.1 Cumulative bar chart, January to June sales (in thousands of dollars).

800

700

600

Product A

500

Product B

400

300

Product C

200

Product D

1000

y

June

January

February

March

April

Ma

Figure 5.2 Line chart, January to June sales (in thousands of dollars).

a line chart that compares sales for all four product lines in time- ordered sequence. Although the intent of each chart is different, the variability of sales from month to month is readily visible. Whether these ups and downs are significant or meaningful is a matter for further discussion that is addressed in the next section. Data, however, that are displayed in a chart create a visual context that reduces the tendency to make point- to- point comparisons.

The bar chart (cumulative data plot) and line chart ( time-ordered plot) tell different stories. The bar chart evaluates different categories of data using a comparable measurement; the time- related chart shows the variability of data as events occur.

116  ◾  Four-Cornered Leadership

Weekly Sales: Product A

35

30

25

20

$

15

Thousands 10

Avg = 18.8

5

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Figure 5.3 Run chart example that displays data in sequence.

The time- ordered sequence presents a running record of a process characteristic in a context that can be used to understand process health: whether process conditions are normal or whether circumstances exist for which corrective action is required.

Data that are associated with time (e.g., information associated with many business operations) can be visualized in a simple time- ordered chart that portrays process variability.

This running record is usually called a run chart. Typically, the horizontal axis is associated with time and the vertical axis depicts change in value, such as dollars. Figure 5.3 illustrates a hypothetical run chart that displays data in sequence. Run charts can monitor the performance of one or more processes over time to detect trends, shifts, or cycles.

Charted data in this example are weekly sales in thousands of dollars for an imaginary product. The amounts are recorded in the order in which sales occurred. The progression is in weeks, but depending on the application, it could be hours, days, or months. In most circumstances, this is a fairly simple technique to assess performance measures for trends or patterns. Typically, 20 to 25 data points are needed to make a meaningful interpretation. To obtain sufficient information, a plot of this nature can be started using historical data and can conclude with current data.

Time- plotted data can be measurements, such as capacity, size, or dimension. The information can be counts, such as Understand the Variability of Work  ◾  117

amounts, number of occurrences, or quantities. If, however, there were many events in a short time span (such as incoming telephone calls or time waiting on hold during a 30-minute period), then computing an average and plotting that average point would be more appropriate. By ordering data in time sequence, the extent of natural or normal variation becomes visible, as do unusual or abnormal events.

In the example in Figure 5.3, several conditions are readily evident. First,

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