Month Production Days Demand Forecast Demand per Day January 21 950 February 19 1,150 March 22 1,150 April 20 1,250 May 21 1,200 June 22 1,000 July 22 1,350 August 21 1,250 September 22 1,150 October 21 1,000 November 21 950 December 19 950

What factors make forecasting at Deckers particularly challenging? Howcan forecasts be made for seasonal, fashionable products for whichthere is no history file? What are the costs of over-forecasting demandfor such items? Under-forecasting?

Week    Sales1    27502    31003    32504    28005    29006    30507    33008    31009    295010    300011    320012    3150Develop a three-week moving average. A.What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) B.What is the value of MSE (mean squared error) with regard to a three-week moving average? (Make sure no decimal place and no comma. EX: 1234) C.Develop the exponential smoothing with the alpha value of 0.20. What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) D.What is the value of MSE (mean squared error) with regard to the exponential smoothing? (Make sure no decimal place and no comma. EX: 1234) E.Which method is more accurate? (1 for Moving average, 2 for Exponential Smoothing)

Month Demand Forecast Error Abs Error alpha 1 20 2 18 3 21 4 25 5 24 6 27 7 22 8 30 9 23 10 20 11 29 12 22 Mean Bias MAD (mean error) Problem 6: Maverick Jeans' demand manager decided to evaluate exponential smoothing. To maintain com- parability, she used the data from problem 6, copied below. 1 Month 2 3 Demand 20 18 21 25 4 5 24 6 7 8 9 10 11 12 27 22 30 23 20 29 22 Develop forecasts for months 5-12. Calculate the Bias and MAD. Use a starting forecast of 20 for month 4 and an alpha value of 0.2. (Note: Adjust all cell values to two decimal points.)