Question

Year Season Demand 2005 1 3200 2 7658 3 4420 2006 1 3654 2 8680 3 5695 2007 1 4742 2 13673 3 6640 2008 1 3486 2 13186 3 5448 2009 1 7728 2 16591 3 8236 2010 1 8526 2 14591 3 10075 Here is the historical demand data for a swiming suit from year 2005 to 2010. The retailer divides each year into three selling seasons. You are asked by the manager to construct demand forecasts for each selling season for the next two years, i.e., year 2011 and year 2012. Answer the following questions: (1) Calibrate the parameters for the moving average method (choose between N=3 and N=6), the exponential smoothing method (choose between alpha=0.3 and alpha=0.7), the winter's method (choose between alpha=0.05, beta=0.1, gamma=0.1 and alpha=0.5, beta=0.6, gamma=0.6), by using the mean squared error as the measure for forecast error. Explain your methodology. (2) Based on the calibrated parameters from (1), choose the best forecast model among the static method, the moving average method, the exponential smoothing method, and the winter's method, by using the mean squared error as the measure for forecast error. Explain your methodology. (3) Using the best model chosen from (2), construct demand forecasts for each selling season year 2011 and year 2012. Explain your methodology.

          Year Season Demand
2005 1 3200
2 7658
3 4420
2006 1 3654
2 8680
3 5695
2007 1 4742
2 13673
3 6640
2008 1 3486
2 13186
3 5448
2009 1 7728
2 16591
3 8236
2010 1 8526
2 14591
3 10075
Here is the historical demand data for a swiming suit from year 2005 to 2010. The retailer divides each year into three selling seasons. You are asked by the manager to construct demand forecasts for each selling season for the next two years, i.e., year 2011 and year 2012. Answer the following questions:
(1) Calibrate the parameters for the moving average method (choose between N=3 and N=6), the exponential smoothing method (choose between alpha=0.3 and alpha=0.7), the winter's method (choose between alpha=0.05, beta=0.1, gamma=0.1 and alpha=0.5, beta=0.6, gamma=0.6), by using the mean squared error as the measure for forecast error. Explain your methodology.
(2) Based on the calibrated parameters from (1), choose the best forecast model among the static method, the moving average method, the exponential smoothing method, and the winter's method, by using the mean squared error as the measure for forecast error. Explain your methodology.
(3) Using the best model chosen from (2), construct demand forecasts for each selling season year 2011 and year 2012. Explain your methodology.

Year Season Demand
2005 1 3200
2 7658
3 4420
2006 1 3654
2 8680
3 5695
2007 1 4742
2 13673
3 6640
2008 1 3486
2 13186
3 5448
2009 1 7728
2 16591
3 8236
2010 1 8526
2 14591
3 10075
Here is the historical demand data for a swiming suit from year 2005 to 2010. The retailer divides each year into three selling seasons. You are asked by the manager to construct demand forecasts for each selling season for the next two years, i.e., year 2011 and year 2012. Answer the following questions:
(1) Calibrate the parameters for the moving average method (choose between N=3 and N=6), the exponential smoothing method (choose between alpha=0.3 and alpha=0.7), the winter's method (choose between alpha=0.05, beta=0.1, gamma=0.1 and alpha=0.5, beta=0.6, gamma=0.6), by using the mean squared error as the measure for forecast error. Explain your methodology.
(2) Based on the calibrated parameters from (1), choose the best forecast model among the static method, the moving average method, the exponential smoothing method, and the winter's method, by using the mean squared error as the measure for forecast error. Explain your methodology.
(3) Using the best model chosen from (2), construct demand forecasts for each selling season year 2011 and year 2012. Explain your methodology.

Added by Caitlin C.

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