Scenario: You have been given a task to create a demand forecast for the second year of sales of a premium outdoor grill. Accurate forecasts are important for many reasons, including for the company to ensure they have the materials they need to create the products required in a certain period of time. Your objective is to minimize the forecast error, which will be measured using the Mean Absolute Percentage Error (MAPE) with a goal of being below 25%. You have historical monthly sales data for the past year and access to software that provides forecasts based on five different forecasting techniques (Naive, 3-Month Moving Average, Exponential Smoothing for .2, Exponential Smooth for .5, and Seasonal) to help determine the best forecast for that particular month. Based on the given data, you will identify trends and patterns to create a more accurate forecast. Approach: Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a. Naive Forecast is 230 and the Forecast Error is -15. b. 3-Month Moving Forecast is 290 and the Forecast Error is -75. c. Exponential Smoothing Forecast for .2 is 308 and the Forecast Error is -93. d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is -64. e. Seasonal Forecast is 297 and the Forecast Error is -82. The forecast for the next month would be 230 as the Naive Forecast had the Forecast Error closest to zero with a -15. This forecasting technique was the best performing technique for that month. You do not need to do any external analysis-the forecast error for each strategy is already calculated for you in the tables below.

3- 3- Naive Month Exponential Exponential Exponential Exponential Seasonal Actual Month Period Month Naive Forecast Moving Smoothing Smoothing Smoothing Smoothing Demand Seasonal Forecast Error Moving Forecast Forecast for 2 Forecast Forecast for .5 Forecast Error Forecast .2 Error .5 Error Error Year 1 JAN 79 70 74 S 73 6 83 FEB 2 70 5 75 9 76 -15 MAR 3 79 84 7 1 77 2 -1 APR 109 70 30 81 28 77 32 80 29 106 3 MAY 5 114 10 5 23 83 31 95 19 89 26 JUN F 119 114 5 101 18 89 30 105 14 21 JUL 7 99 119 20 114 -15 95 4 112 13 12 AUG 8 119 99 24 111 8 96 23 106 13 107 12 SEPT 9 84 119 -35 112 BZ- 101 -17 113 -29 81 3 OCT 10 75 101 -26 98 23 -24 13 NOV 11 84 75 -9 93 -9 87 -15 DEC 12 75 84 81 91 -16 86 -11 98 -23 Year 2 JAN 13 75 FEB 14 MAR 15 APR 16 MAY 17 JUN 18 JUL 19 AUG 20 SEPT 21 OCT 22 NOV 23 DEC 24 Activity 1: Year 2 Forecast Forecast next Year 2 Average Actual Demand MAPE% period MAPE Month Year 1 Year 2 Seasonal Index JAN JAN 79 0.954 FEB FEB 84 0.848 MAR MAR 79 0.901 APR APR 109 1.033 MAY MAY 114 1.298 JUN JUN 119 1.219 JUL JUL 1.139 AUG AUG 119 1.113 SEPT SEPT 84 1.033 OCT OCT 75 0.848 NOV NOV 84 0.848 DEC DEC 75 0.768 Activity 2: Forecast Technique Analysis Forecasting technique that best fits the data: Select V