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Whys

One uses the above knowledge and skill to find the real reason for the positive situation or a bad situation. This is called a ”Why.”

A Why is that basic outness found which will lead to a recovery of statistics. An outness is something that has been done wrong or incorrectly or is missing completely.

Statistics are the measurement of the number or amount of something compared to an earlier number or amount of the same thing. Statistics measure the volume or quality of production of some activity. Any activity can be measured by a statistic that shows the production of that activity.

For example, the number of sales in a shoe store have gone down. Someone finds a reason or Why for this decline in shoe sales. They correct what they have found. Two weeks later the shoe sales statistic (number of shoes sold) in the store has improved and has fully recovered from the decline. This shows that the reason or Why for the number of shoes sold that was found was correct.

A wrong Why is the incorrectly identified outness which when applied does not lead to recovery.

In the shoe store sales example above, suppose that after a couple of weeks of finding the “ Why ” and trying to “correct ” it, the statistic of number of shoe sales went down even further. The “ Why ” that was found did not lead to a recovery of the shoe sales statistic. It was a wrong Why.

A mere explanation is a “ Why ” given as the Why that does not make possible any recovery.

Again, using the example of the shoe store: A Why for the declining shoe sales is given: “The statistics went down because of rainy weather that week.” So? So do we now turn off rain? This explanation does not result in any raised statistic.

Another mere explanation might be: “The staff became overwhelmed that week” meaning they were affected strongly by something. So an order might be given ”Don’t overwhelm the staff” as a possible ”solution” by a manager. BUT THE STATISTICS WOULDN’T RECOVER. So the explanation given could not be called a Why. It did not lead to a recovery of statistics.

The real Why, the correct one, when found and corrected leads straight back to improved statistics.

A wrong Why, corrected, will further lower statistics.

A mere explanation does nothing at all and things continue to do poorly.

Here is another example: The statistics of an area were down. Investigation showed that there had been sickness two weeks before. The report came in: “The statistics were down because people were sick.” This was a mere explanation, giving a ”reason,” but it solved nothing. What might be done now? Maybe this explanation is accepted as the correct Why. An order is then given, ”All people in the area must get a medical exam and unhealthy workers will not be accepted. Unhealthy workers will be fired.” Because this is a correction to a wrong Why, the statistics of the activity that measure its productivity go badly down. So that isn’t it. Looking further the real Why is found: The boss in the area gives orders to the wrong people which, when followed, then hurt their individual production and their production statistics go down.

To correct this, the area is organized, the boss is trained rapidly so he knows what is going on and doesn’t keep issuing wrong orders. As a result, the production of the area improves and the statistics measuring the production of the area do recover and increase even more. The correct Why led to a statistic recovery.

Here is another example: The statistics are down in a school. The number of students completing their studies in the expected time is way down. An investigation is done which gives a mere explanation: “The students were all busy with sports.” So school management says ”No sports for the students!” Now the statistic of student completions goes down again. A new investigation comes up with a wrong Why: “The students are being taught wrongly.” School management fires the dean, the official in charge of the students and teachers. Now the statistic goes down steeply.

Finally, a further, more competent investigation occurs. It turns out that there were 140 students and only the dean and one instructor! And the dean had other duties! So the dean is returned to his job and two more instructors are hired, making three instructors. Statistics at the school go up very high because a right Why has been found.

Management and organizational disasters and successes are all explained by these three types of Why. An arbitrary—a false order or datum entered into a situation—is probably just a wrong Why kept in place by a law or rule. And if kept that way, an activity operating under this false order or datum will have a lot of trouble and the statistics that measure its production will stay very low.

So if you want to correctly handle a bad situation, you must understand logic so you can find the correct Why and you must really be alert and correct a wrong Why.

In world banking, where inflation regularly occurs, the various finance regulations or laws are probably just one long series of wrong Whys. The value of the money and its usefulness to the citizen can deteriorate to such an extent that a whole philosophy can be built up. This occurred in the ancient city of Sparta. Lycurgus, a Greek lawmaker, invented money made of iron that nobody could lift, so it couldn’t be used. He was trying to get rid of ”money evils” in Sparta. That stopped the use of money entirely and in place of money was nothing but nonsense.

Organizational troubles are greatly worsened by using mere explanations (which lead to no improvement) or wrong Whys (which cause further lowering of statistics). Organizational improvements come from finding the real Why and correcting it.

The test of the real Why is ”When it is corrected, do the statistics that measure the production of an area recover? ” If they do, then the Why was correct. And any other corrective order given but based on a wrong Why would have to be cancelled quickly.

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