MKT 679 Milestone Three..docx MKT 679 MKT 679 Milestone Three Southern New Hampshire

MKT 679 Milestone Three..docx    MKT 679  MKT 679 Milestone Three  Southern New Hampshire University  Compute the demand forecast for January    Three-month moving average forecast  Using exponential smoothing with smoothing parameter α = 0.3 to compute the demand forecast for January (Period 13) and assuming that the forecast for period 1 was 74, the demand forecast for January is 104.8.  Using trend-adjusted exponential smoothing with smoothing parameter α = 0.5 and trend parameter β = 0.3 to compute the demand forecast for January (Period 13), the demand forecast  for January is predicted to be 112.7 with trend 3.0 and forecast including trend, 115.7.  Calculate the forecast for each period using a regression model.  Compute the mean absolute deviation for each method used. Trend adjusted exponential smoothing mean  absolute deviation:  Three month moving mean absolute deviation:  Data analysis  As I wanted to discover if there was a relation between periods and their demand, I ran a linear regression analysis to determine this. In doing so, I was able to determine that regression intercept by using the results which allows the forecast demand for each period to be discovered. I would recommend using the trend adjusted exponential smoothing and the regression model  recasting as these methods were more accurate. In order to drive future sales based on the  forecasting model, the company should increase advertising or add in sales in the months that show a lower demand (periods 1, 2, 3, & 4). The for