Mary Lindsey has recently agreed to leave her upper-level management job at a major paper…

Mary Lindsey has recently agreed to leave her upper-level
management job at a major paper manufacturing firm and return to her hometown
to take over the family machine-products business. The U.S. machine-products
industry had a strong position of world dominance until recently, when it was
devastated by foreign competition, particularly from Germany and Japan. Among
the many problems facing the American industry is that it is made up of many
small firms that must compete with foreign industrial giants. Wagner Machine
Works, the company Mary is taking over, is one of the few survivors in its part
of the state, but it, too, faces increasing competitive pressure. Mary’s father
let the business slide as he approached retirement, and Mary sees the need for
an immediate modernization of their plant. She has arranged for a loan from the
local bank, but now she must forecast sales for the next three years to ensure
that the company has enough cash flow to repay the debt. Surprisingly, Mary
finds that her father has no forecasting system in place, and she cannot afford
the time or money to install a system like that used at her previous company.
Wagner Machine Works’ quarterly sales (in millions of dollars) for the past 15
years are as follows:

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While looking at these data, Mary wonders whether they can
be used to forecast sales for the next three years. She wonders how much, if
any, confidence she can have in a forecast made with these data. She also wonders
if the recent increase in sales is due to growing business or just to
inflationary price increases in the national economy.

Required Tasks:

1. Identify the central issue in the case.

2. Plot the quarterly sales for the past 15 years for Wagner
Machine Works.

3. Identify any patterns that are evident in the quarterly
sales data.

4. If a seasonal pattern is identified, estimate quarterly
seasonal factors.

 5. Deseasonalize the data using the quarterly seasonal
factors developed.

6. Run a regression model on the deseasonalized data using
the time period as the independent variable.

7. Develop a seasonally adjusted forecast for the next three
years.

8. Prepare a report that includes graphs and analysis.

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