Reference no: EM132089751
Pricilla Phranklin opened her new clothing store, Pricilla Phashions, in downtown Dover. Pants, skirts, blouses, and shirts have fairly consistent sales, while things like shorts, sweater and outerwear are highly seasonal. On the other hand, dresses are a little harder to predict. Pricilla is hiring you to arrive at the best forecasting method for dresses.
The following table contains three years of dress sales data:
Year 1 Year 2 Year 3
Month Demand Demand Demand
Jan 155 181 177
Feb 185 183 204
Mar 183 199 210
Apr 297 302 313
May 600 606 631
Jun 306 313 337
Jul 283 284 295
Aug 244 242 270
Sep 254 247 282
Oct 500 500 515
Nov 271 265 297
Dec 566 590 594
Total 3844 3912 4125
Using the dress data above, calculate the following:
1. Using years 2 and 3 data forecast year 3 using a simple 3 period moving average.
2. Using years 2 and 3 data forecast year 3 Calculate a forecast using the exponential smoothing method. Assume α = 0.20.
3. (a) Using the information you have for years 1 & 2, calculate a monthly index. And then (b)Using year 1 and 2 data Determine the slope and intercept of line equation using linear regression. Then calculate the new annual demand and forecast the monthly demand.
4. Using the actual Year-3 numbers to check your forecast accuracy, by using the forecasting error to calculate CFE, MSE, MAD, MAPE, and the tracking signal.
5. Finally, you are to indicate which forecasting technique is best and do an analysis of the shortcoming of each forecasting method, as indicated by the forecasting errors.