Seen & Heard

Explaining early exits

Here is another viewpoint to the Career Corner column, "Who’s Responsible?" (February 2013, pp. 54-55): I’ve seen early career professionals come and go within three to five years. One reason I see is the training and job development is not accelerated enough for them. Experience comes after training and job development—not advancement the next day.

The early career professionals are used to frequent changes in their lifestyles. A case in point is how often they abandon last year’s iPods for this year’s model. They want "instant pudding" solutions. When it doesn’t happen, they move on.

Another reason I see is that it takes three to five years for companies to realize the money they’ve invested in hiring isn’t paying off. Because of the lack of experience, the early career professionals find they are unable to maintain their standing in their position, so they move on to another job and start the cycle all over again.

The days of longevity in a job are long gone—along with experience and, in many cases, company loyalty. These are two commodities that successful companies require.

Tom Herr
Huntingdon Valley, PA

Time for a new outlook

I have a few comments about Figure 4 (p. 42) in the article, "In the Crosshairs" (January 2013, pp. 38-44), although you may be fully aware of what I’ll write. Setting aside the cycle time data of -105, which I don’t understand, I’ve used the other information to make some calculations. My experience indicates that the lognormal distribution describes many time events. Thus, using the mean of 41.44 and the standard deviation of 65.84, I calculate the median to be 22.07, which compares well with the median of 21.0 shown. The mode calculates to be ∼6. The expected range of the data to be ∼0.5 to ∼1,350. The max listed is 1,274.

Rudy Kittlitz
Waco, TX

Author’s response: Thank you for your thoughtful question and insightful alternative to time-based characterization.

As far as the negative time value as the minimum value of -105 in Figure 4, in my experience, I have found negative time to be a common artifact in this analysis despite no presence of such negative time in the data set.

I really appreciated your suggestion of using a lognormal distribution as a more effective method of characterization. I had not previously explored this, and this is now part of my toolbox because it resolves many limitations around time-based characterization. 

Russell Lindquist
Beloit, WI

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"What is the right way to ensure a lean Six Sigma project stays on track?"

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