Using historic sales data create a monthly forecast using classical decomposition that is based on regression and seasonality. We are creating a monthly forecast not quarterly.

For help, review Module 4 Excel Exercise. Please note that the Module 4 Exercise is based on quarterly data. Kiana is creating a monthly forecast based on monthly data. You will create a seasonal Index for each month. Therefore, you will have seasonal indexes for Jan, Feb, March, etc. Remember the total of the month’s seasonal indexes for a year should add up to 12. You will still use a three-year average for your final seasonal index.

116 4 5 6 7 8 9 Home Insert Cut Copy Format x 3209 2778 2428 2996 2349 3767 7/1/17 3338 8/1/17 2604.5 10 9/1/17 2251.5 11 10/1/17 3246 12 11/1/17 3542 4331 2217 2/1/18 2683.5 3/1/18 2954.5 3235 Pastel - A 43 44 45 46 47 1/1/17 2/1/17 3/1/17 13 12/1/17 14 1/1/18 48 49 50 51 15 16 17 4/1/18 4/1/17 5/1/17 6/1/17 18 5/1/18 19 6/1/18 20 7/1/18 21 8/1/18 22 9/1/18 23 10/1/18 24 11/1/18 25 12/1/18 26 1/1/19 27 2/1/19 28 3/1/19 29 4/1/19 30 5/1/19 31 32 33 B 6/1/19 7/1/19 3155 8/1/19 3268 34 9/1/19 3568 35 10/1/19 4475 36 11/1/19 5248.5 37 12/1/19 5591.5 38 39 40 41 42 2459 3794 4589 3087 3112 3785 4193 5129 2674 3112.5 1 Month Demand Yearly average Seasonal Index Average SI Deseasonalized Time period 2 3 2848.5 2465 3770 3714 Draw Page Layout Calibri (Body) 11 StartHere BI U fx D B A- A Formulas Boden E Financial information Y F Data G Review SI and regression 20 View ** H + Wrap Text Current operations Start here_Individual projectV2 Merge & Center 1 Deseasonalized forecast Seasonalized forecast General $ % Future operations % > J K Regression Output