The operations manager created a histogram showing low production on Monday between 8-9am, 11am-12pm, and 12-1pm. However, looking at histograms for the rest of the week revealed the actual problematic times were 8-9am and 1-2pm. Examining multiple data points over several days provided a more accurate understanding of the production issues than a single day alone.

2. We discussed in the last learning activity that a single
histogram only tells one part of a long data story. That full
story might be good to know if you want to make a truly
informed business decision.
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3. EXAMPLE SCENARIO: You are an operations manager for a
manufacturing company that produces widgets. In order for
your company to meet the production rate goals, your floor
needs to create 320 widgets per day. Last week’s quota was
not met, and the CEO has tasked you to find out why.

4. You create a histogram that shows the production frequency
of widgets for Monday. The graph shows that the intervals
from 8:00-9:00, 11:00-12:00, and 12:00-1:00 seem to be the
problem:
Monday Histogram
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5. But after taking this course, you recognize that you might
not be getting the full picture with just one histogram and
decide to generate four more for Tuesday through Friday:
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Thursday Histogram Friday Histogram
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6. THE REST OF THE STORY: After examining the rest of the
histograms, you discover that the actual problematic time
ranges are 8:00-9:00, 12:00-1:00, and 1:00-2:00.
Production during the interval of 11:00-12:00 on Monday
was low due to an employee training meeting, and
production improved during this time on the other days.
The dip in production between 12:00-1:00 each day is due to
lunchtime, and you determine the rate is adequate during
this time.
After identifying the problematic hour intervals of 8:00-9:00
and 1:00-2:00, you dig deeper and discover that employees
are arriving late to work and getting back late from lunch.

7. Had you relied on the histogram generated from Monday’s
production alone, you would have had some but not all of
the story.
Power in numbers (multiple histograms) gave you the
knowledge you need to appropriately identify the problem
and provide corrective action to increase productivity.
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8. LESSONS TO LEARN:
• If you had relied on one histogram, you may have
missed the main cause of the problem
• Looking at more histograms provided an more
accurate story of the data
• A single histogram only tells a part of a longer story
• Looking at all kinds of data over an extended period
of time provides useful knowledge about a process
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9. There is power in numbers!
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10. CRITICAL THINKING: Have you encountered a
situation where looking at a lot of data helped (or
looking at little data hindered)? How was there
power in numbers?
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