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Week 7 Operations Management

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					a) Forecasting Problem Set; end of Chapter 15 Problems in Chase, Jacobs, and
Aquilano (2009):

      i) Complete problems: 6 (it is suggested that you put this information in a
      graph(s)), 20 (parts a, b, c, and e only), 27. In addition, interpret the overall
      R2 and the individual p value for each independent variable. Is this model
      useful? Why or why not?

      ii) Use Microsoft Excel for all computations. Ensure that your Excel file
      includes the associated cell computations; this information is needed in order
      to receive full credit on these problems.

      iii) Each problem is to be placed into a separate worksheet, and all problems
      are to be placed into one file. For example for this assignment, your Excel file
      should contain one worksheet named 6; another worksheet named 20; and
      another worksheet named 27.

      iv) Submit this assignment to the instructor in one Microsoft Excel file by the
      end of Module 7.
6. TS 1: Rapid rise in trend. Will
shortly be outside limits.
Forecast model not good.
TS 2: Forecast OK.
Within limits.
TS 3: Rapid rise.
Outside limits. Forecast model not good.

6 The tracking signals computed using past demand history for three different products are as

follows. Each product used the same forecasting technique.

TS 1 TS 2 TS 3
1 −2.70 1.54 0.10
2 −2.32 −0.64 0.43
3 −1.70 2.05 1.08
4 −1.1 2.58 1.74
5 −0.87 −0.95 1.94
6 −0.05 −1.23 2.24
7 0.10 0.75 2.96
8 0.40 −1.59 3.02
9 1.50 0.47 3.54
10 2.20 2.74 3.75
Discuss the tracking signals for each and what the implications are.



20 Your manager is trying to determine what forecasting method to use. Based upon the
following
historical data, calculate the following forecast and specify what procedure you would utilize.
ACTUAL ACTUAL
MONTH DEMAND MONTH DEMAND
1 62 7 76
2 65 8 78
3 67 9 78
4 68 10 80
5 71 11 84
6 73 12 85
a. Calculate the simple three-month moving average forecast for periods 4–12.
b. Calculate the weighted three-month moving average using weights of 0.50, 0.30, and 0.20
for periods 4–12.
c. Calculate the single exponential smoothing forecast for periods 2–12 using an initial forecast
( F 1 ) of 61 and an _ of 0.30.
e. Calculate the mean absolute deviation (MAD) for the forecasts made by each technique in
periods 4–12. Which forecasting method do you prefer
27 Mark Price, the new productions manager for Speakers and Company, needs to find out
which
variable most affects the demand for their line of stereo speakers. He is uncertain whether the
unit price of the product or the effects of increased marketing are the main drivers in sales
and wants to use regression analysis to figure out which factor drives more demand for their
24. a. MAD = 79.17;
TS = −2.84.
b. Forecasts were above the
mean of the actual data but
acceptable.
25. See ISM.
26. See ISM.
512 section 4 PLANNING AND CONTROLLING THE SUPPLY CHAIN
particular market. Pertinent information was collected by an extensive marketing project that
lasted over the past 10 years and was reduced to the data that follow:
SALES/UNIT ADVERTISING
YEAR (THOUSANDS) PRICE /UNIT ($000)
1996 400 280 600
1997 700 215 835
1998 900 211 1,100
1999 1,300 210 1,400
2000 1,150 215 1,200
2001 1,200 200 1,300
2002 900 225 900
2003 1,100 207 1,100
2004 980 220 700
2005 1,234 211 900
2006 925 227 700
2007 800 245 690
a. Perform a regression analysis based on these data using Excel®. Answer the following
questions
based on your results.
b. Which variable, price or advertising, has a larger effect on sales and how do you know?
c. Predict average yearly speaker sales for Speakers and Company based on the regression
results if the price was $300 per unit and the amount spent on advertising (in thousands) was
$900.
28 Assume an initial starting F t of 300 units, a trend of 8 units, an alpha of .30, and a delta of
.40.
If actual demand turned out to be 288, calculate the

				
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