American Aerospace Inventory Case Study - Operations Resource Planning - Case Study

1344 words - 6 pages

Inventory at American Aerospace (AA)
Executive Summary (10 points each)
Overall Scarlett has a unique opportunity to help this aerospace
manufacturer improve their understanding of their operations while
helping them improve their inventory management. Through the Q,R
model she can set the optimal order point and quantity so that AA can
minimize costs while still meeting the service levels they want to
maintain. After looking at the distribution of lead times and parts
ordered we can use that data and its summary statistics to calculate
these numbers. Even a T,S model is better than pure guessing. At this
point in time AA experiences low production levels and constant stock
outs because they do not manage their inventories very well. Therefore
after implementing this Q, R system AA will improve operational
efficiencies, reduce costs, and have a low stock out level. In the long
run this will make them a better company and maximize profits.
(a) To prove that the monthly demand for MX332 follows a normal distribution, there are
rigorous and convenient methods to do it. The most popular and convenient method is to
draw a histogram to show if it follows a bell shape distribution. Since the maximum and
minimum of the order quantities from the above table are 146 and 55, respectively, let’s
set up the histogram with the cell upper bounds (i.e., bin ranges) at 50, 60, 70, 80, 90,
100, 110, 120, 130, 140, and 150 by using the histogram from Data Analysis Tools.
Show the histogram. Does it look bell shaped? Also use the Excel function to find the
sample mean, variance, and standard deviation of the monthly orders for the MX332.
It is mostly normal. There is a small left skew but is mostly bell shaped.
(b) Although the histogram does give a rough idea if the demand data follows normal
distribution, Scarlett also wants to prove it with a rigorous statistical test. The 2
goodness-of-fit test she learned from school is a perfect one. Based on the mean and
standard deviation computed in (a), Scarlett hypothesizes the monthly demand
distribution for MX332 follows the normal distribution with the mean of 100 and the
standard deviation of 20. Following the same bin ranges of the above histogram, the cells
are set up at 40-50, 50-60, 60-70, 70-80, 80-90, 90-100, 100-110, 110-120, 120-130, 130-
140, and 140-150. Help Scarlett to perform the 2 goodness-of-fit test. Do you approve
or reject the normal distribution assumption for the monthly demands of MX332?
Because our X2 is larger than the X2 df, a we reject the
assumption of normality. Please see my excel file for my analysis.
(c) Since the maximum and minimum of the lead times of the solid steel part from the above
table are 45 and 20, respectively, let’s set up the histogram with the cell upper bounds
(i.e., bin ranges) at 20, 23, 26, 29, 32, 35, 38, 41, 44, and 47 by using the histogram from
Data Analysis Tools. Show the histogram. Does it look bell shaped? Also use the Excel
function to find ...

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