Operations and production managers often use the normal distribution as a probab
ID: 3112335 • Letter: O
Question
Operations and production managers often use the normal distribution as a probability model to forecast demand in order to determine inventory levels, manage the supply chain, control production and service processes, and perform quality assurance checks on products and services. The information gained from such statistical analyses help managers optimize resource allocation and reduce process time, which in turn often improves profit margins and customer satisfaction.
Based on your understanding of the characteristics of the normal distribution, examine the chart below. Process A standard deviation is .9, Process B standard deviation is 1.4, and the mean of both processes is 12. Contribute to our discussion by posting a response to ONE of the questions below.
Do either of the processes below fit a normal distribution? Why or why not?
Which of the processes shows more variation? What does this mean practically?
If the product specification quality limits were 12 +/- 3, which of the processes more consistently meets specification? Explain why.
If the product specification quality limits were changed to 12 +/- 6, is quality loosening or tightening? Which process would benefit the most from this change?
Are there processes at your place of employ that you believe follow a normal distribution? If so, describe one. Why do you believe it is normal?
Explanation / Answer
# Do either of the processes below fit a normal distribution? Why or why not?
the normal distribution is a function that represents the distribution of many random variables as a symmetrical bell-shaped graph. Both Process A and B are showing the symmetrical bell-shaped graph, that's why both seem to be normal. There seems to be no skewness.
# Which of the processes shows more variation? What does this mean practically?
Process B shows more variation, that means it can acquire more random values than Process A. Less standard deviation is always good for the model because it signifies that your data are closely distributed. In Process B, the standard deviation is more that means data is not closely distributed, which we can easily see in the diagram.
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