If you assume at this point that you are

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CA should never be considered as the only statistical figure when making any decisions because of skewness. In a data set where the vast majority of values ​​are small, enough large numbers can skew the data set and shift the value to the right. dealing with an unskewed normal distribution, your decisions will be wrong. The same scenario is true in the opposite situation, where most of the data points are large and a significant number of small numbers will skew the data to the left.

CA also has difficulty estimating sales sweden telegram database where sales occur infrequently. Here are some scenarios that illustrate the problem of using CA in sales forecasting:

Scenario A) 1 sale occurring every 10 days means that there are 3 sales in 30 days (month), 30 sales in 300 days, so 27 days a month there are no sales. The average in this situation will be 1/10 sales per day.

Scenario B) 3 sales occurring on the same day in a 30 day period (one month) means that there will be 29 days in one month with no sales (30 sales in 300 days). The average is still 1/10 sales per day.

Scenario C) 30 sales occurring once in a 300-day period means that there are 299 days without sales during that period. The average is still 1/10 of the sales per day. In all three scenarios, the calculated average is far from reality, and if you were to use this method, it would result in you either not having inventory when you needed it, or having too much inventory. Now that we've looked at the problems associated with using an average to forecast sales and profits, it's time to get more precise and see what an optimal allocation model would look like.

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