This is an example of Exponential Smoothing with Trend Adjustment. The goal is to compute an exponentially smoothed average to adjust inventory (+/-) to account for lags. formula for Forecast and another formula for trend. Home Depot Demand for Lighting Fixtures Use an alpha of 0.5 and a beta of 0.9 Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 1. What is the Bias? 2. What is the MAD? 15 14 19 25 17 12 21 18 11 3. What is the MSE? Do you want to minimize or maximize the MSE? Why? 4. What is the MAPE? 5. What kind of conclusions can you make about the data? 6. Now change the alpha value to 0.1. How does this impact the data? Is your forecast more or less accurate and why? 7. Using the last problem as your reference data: Let's introduce a seasonality factor, assume the demand increases to 25 in Month 8 and 30 in Month 9. The uptick in construction creates a high demand for light fixtures. What happens to your data? 8. As the Inventory Manager for Home Depot, do you recommend increasing/decreasing or holding constant the last model volumes? Why or why not? What are some possible errors in accepting one method over another? 9. Distinguish the differences between column B (Initial Demand) and Column F (The Forecast Including Trend. Highlight a few key facts regarding the impact of an adjusted forecast on initial demand forecasts.)