Zeus Computer Chips Step by Step - CHAPTER 3 Solution 2...

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CHAPTER 3 Solution 2. HISTORICAL DEMAND Month Demand Jan 12 Feb 11 Mar 15 Apr 12 May 16 Jun 15 a. Weighted moving average forecast Weights are .60, .30, .10 WMAF July = .60 * 15 +.3 *16 + .10 * 12 = 15 b. 3-period moving average forecast MAF July = (15 + 16 + 12)/3 = 43/3 = 14.33 c. Exponential smoothing forecast α = .20, ESF June = 13 ESF July = .20 * 15 + (1-.20) * 13 = 13.4 d. Simple linear regression Month x y xy x2 Jan 1 12 12 1 Feb 2 11 22 4 Mar 3 15 45 9 Apr 4 12 48 16 May 5 16 80 25 Jun 6 15 90 36 21 81 297 91 3-1
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e. Regression forecast x = 7 Y 7 = 10.8 + .7714 * 7 = 16.2 4. ZEUS COMPUTER CHIPS Deseasonalize the data Year Quarter Perio d (x) Actual Demand (y) Average of the same quarter each year Seasonal Factor Deseasonalized Demand (Y d ) x 2 x * Y d 2007 I 1 4800 3833.3 1.23 3902.4 1 3902.4 II 2 3500 2766.7 0.89 3932.6 4 7865.2 III 3 4300 3500.0 1.12 3839.3 9 11517.9 IV 4 3000 2366.7 0.76 3947.4 16 15789.6 2008 I 5 3500 1.23 2845.5 25 14227.5 II 6 2700 0.89 3033.7 36 18202.2 III 7 3500 1.12 3125.0 49 21875 IV 8 2400 0.76 3157.9 64 25263.2 2009 I 9 3200 1.23 2601.6 81 23414.4 II 10 2100 0.89 2359.6 100 23596 III 11 2700 1.12 2410.7 121 26517.7 IV 12 1700 0.76 2236.8 144 26841.6 78 37400 12.00 37392.5 650 219013 Calculate the seasonal factors and then determine the regression trend line Calculate the forecast for 2010 3-2
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Year Quarter Period Y from Regression Line Seasonal Factor Forecast (Y * seasonal factor) 2010 I 13 2023.4 1.23 2488.78 II 14 1855.3 0.89 1651.22 III 15 1687.2 1.12 1889.66 IV 16 1519.1 0.76 1154.52 5. BI-MONTHLY SALES DATA a. Plot the data b. Fit simple linear regression model to the data Month x y xy x2 January–February 1 109 109 1 March–April 2 104 208 4 May–June 3 150 450 9 July–August 4 170 680 16 September–October 5 120 600 25 November–December 6 100 600 36 January–February 7 115 805 49
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