Case 16.3 Wagner Machine Works Mary Lindsey has recently agneed to leave her upper-level man- apement job at a major paper manufacturing firm and return to her hometown to take over the family machine-products business. The US. machine-products industry had a strong position of world dominance until recently, when it was devastated by foreign com- petition, particularly from Germany and Japan. Among the many problems facing the American industry is that it is made up of many small fims 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, t0o, faces increasing competitive pressure. Mary's father let the business slide as he approached retirement, and Mary sces 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 Quarter 4. Year 11,130 12.550 13,100 2001 10,490 10,005 11,058 2002 11.424 10.900 12.335 2003 12.835 11.660 13,767 12,780 13.785 14,785 16.554 19,445 2004 13,877 14.100 14,738 15.210 17.167 16,218 2005 2006 14,798 16,720 17.725 18.951 21.395 24,179 27,778 30,125 19,889 22,816 25,029 27.360 2007 18.348 2008 20,317 2009 23,335 22.548 2010 23,391 25,729 28,886 30,212 31,715 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 shown in the table. 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 mational economy. 2011 26,049 30.300 2012 33,702 27.907 31,096 2013 35,720 39,447 28.554 34.326 2014 2015 37.587 35,533 39,093 30,046 44,650 30,046 37.587 3. Identify any patterns that are evident in the quarterly sales data 4. If you have identified a seasonal pattern, estimate quarterly seasonal factors. 5. Deseasonalize the data using the quarterly seasonal factors developed. 6. Run a regression model on the descasonalized data using the time period as the independent variable. 7. Develop a seasonally adjusted forecast for the next three Required Tasks: 1. Identify the central issue in the case. 2. Plot the quarterly sales for the past 15 years for Wagner Machine Works. years. 8. Prepare a report that includes graphs and analysis.