Activity 1: Year 2 Forecast Forecast next period Average Actual Demand Year 2 MAPE% MAPE Month Year 1 Year 2 Seasonal Index JAN JAN 80 0.954 FEB FEB 86 0.848 MAR = MAR 80 0.901 APR APR 111 1.033 MAY ÷ MAY 116 1.298 JUN JUN 122 1.219 JUL = JUL 101 1.139 AUG AUG 122 1.113 SEPT SEPT 86 1.033 OCT OCT 76 0.848 NOV 86 0.848 NOV = DEC DEC 76 0.768 Activity 2: Forecast Technique Analysis Forecasting technique that best fits the data: Seasonal Forecast ^ 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 (Naïve, 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. Naïve 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 Naïve 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. Naïve Month Period Actual Demand Naïve Forecast Error 3- Month Moving Forecast 3- Month Moving Forecast Error Exponential Exponential Exponential Exponential Smoothing Smoothing Smoothing Smoothing Forecast for .2 Forecast Forecast for 5 Forecast Seasonal Seasonal Forecast Error .2 Error .5 Error ▾ Year 1 JAN 1 80 70 10 74 6 73 7 84 -4 FEB 2 86 80 6 75 11 77 9 101 -15 MAR 3 80 86 -6 79 1 77 3 82 -2 89 -9 APR 4 111 80 31 82 29 78 MAY 5 116 111 5 92 24 85 JUN 6 122 116 6 102 20 JUL 7 101 122 -21 116 -15 AUG ∞ 8 122 101 21 113 9 SEPT 9 86 122 -36 115 -29 103 OCT 10 76 86 -10 103 -27 NOV 11 86 76 10 95 -9 DEC 12 76 86 -10 83 -7 Ç ྨ 。。 སྒ ཋ ཋ ྴ| 33 81 30 107 4 31 96 20 89 27 91 31 106 16 100 22 97 4 114 -13 89 12 98 24 108 14 110 12 -17 115 -29 83 3 100 -24 101 -25 90 -14 95 -9 89 -3 101 -15 93 -17 88 -12 99 -23 Year 2 JAN 13 76 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 period Average Actual Demand Year 2 MAPE% MAPE Month Year 1 Year 2 Seasonal Index JAN JAN 80 0.954 FEB FEB 86 0.848 MAR = MAR 80 0.901 APR APR 111 1.033 MAY ÷ MAY 116 1.298 JUN JUN 122 1.219 JUL = JUL 101 1.139 AUG AUG 122 1.113 SEPT SEPT 86 1.033 OCT OCT 76 0.848 NOV 86 0.848 NOV = DEC DEC 76 0.768 Activity 2: Forecast Technique Analysis Forecasting technique that best fits the data: Seasonal Forecast ^ 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 (Naïve, 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. Naïve 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 Naïve 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. Naïve Month Period Actual Demand Naïve Forecast Error 3- Month Moving Forecast 3- Month Moving Forecast Error Exponential Exponential Exponential Exponential Smoothing Smoothing Smoothing Smoothing Forecast for .2 Forecast Forecast for 5 Forecast Seasonal Seasonal Forecast Error .2 Error .5 Error ▾ Year 1 JAN 1 80 70 10 74 6 73 7 84 -4 FEB 2 86 80 6 75 11 77 9 101 -15 MAR 3 80 86 -6 79 1 77 3 82 -2 89 -9 APR 4 111 80 31 82 29 78 MAY 5 116 111 5 92 24 85 JUN 6 122 116 6 102 20 JUL 7 101 122 -21 116 -15 AUG ∞ 8 122 101 21 113 9 SEPT 9 86 122 -36 115 -29 103 OCT 10 76 86 -10 103 -27 NOV 11 86 76 10 95 -9 DEC 12 76 86 -10 83 -7 Ç ྨ 。。 སྒ ཋ ཋ ྴ| 33 81 30 107 4 31 96 20 89 27 91 31 106 16 100 22 97 4 114 -13 89 12 98 24 108 14 110 12 -17 115 -29 83 3 100 -24 101 -25 90 -14 95 -9 89 -3 101 -15 93 -17 88 -12 99 -23 Year 2 JAN 13 76 FEB 14 MAR 15 APR 16 MAY 17 JUN 18 JUL 19 AUG 20 SEPT 21 OCT 22 NOV 23 DEC 24

Marketing

20th Edition

ISBN:9780357033791

Author:Pride, William M

Publisher:Pride, William M

Chapter6: Target Markets: Segmentation And Evaluation

Section: Chapter Questions

Problem 17DRQ: Under what conditions might a firm use multiple forecasting methods?

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