Sales forecasts Sales budget Production budget Revenue budget
Sales forecasts Sales budget Production budget Revenue budget Direct labor materials and overhead budgets Sales and administrative expense budget Cost of goods sold budget Budgeted profit and loss statement Impact of Sales Forecasts on Budgeting
50 40 Percent rate of change forecast Sales 30 Unit rate of change forecast 20 Naïve forecast Moving average forecast 10 0 1 2 3 4 5 Time Period Figure 7 -2: Comparing Trend Forecasting Methods
90 Sales 80 3. 6 70 Y = 63. 9 + 3. 5 X 63. 9 60 50 0 1 2 3 4 Time Period Figure 7 -3: Fitting a Trend Regression to Seasonally Adjusted Sales Data 5 6
Forecasting with Moving Averages Time Periods Actual sales Seasonally adjusted sales Two-period moving average forecast seasonally corrected Three-period moving average forecast seasonally corrected Two-period moving average forecast F 3 = ( S 1 + S 2 ) x I 3 2 = ( 67 + 68 ) x 1. 16 2 = 78. 3 1 2 3 4 5 6 49 67 77 68 90 78 79 81 57 78 98 87 78. 3 70. 1 58. 0 89. 8 68. 9 55. 2 Three-period moving average forecast 89. 3 F 4 = ( S 1 + S 2 + S 3 ) x I 4 3 = ( 67 + 68 + 78 ) x 0. 97 3 = 68. 9
Market potential Industry forecast Basic demand gap Industry Sales Company potential Company forecast Company demand gap Actual Forecast 1 2 3 4 5 6 7 8 9 10 11 12 Custom time period Figure 7 -1: Relations Among Market Potential, Industry Sales, and Company Sales
Table 7 -3 Utilization of Sales Forecasting Methods of 134 Firms Methods Subjective Sales force composite Jury of executive opinion Intention to buy survey Extrapolation Naïve Moving Average Percent rate of change Leading indicators Unit rate of change Exponential smoothing Line extension Quantitative Multiple regressing Econometric Simple regression Box-Jenkins Percentage of Firms that Use Regularly Percentage of Firms That Use Occasionally Percentage of Firms No Longer Used 44. 8% 37. 3 16. 4 17. 2% 22. 4 10. 4 13. 4% 8. 2 18. 7 30. 6 20. 9 19. 4 18. 7 15. 7 11. 2 6. 0 20. 1 10. 4 13. 4 17. 2 9. 7 11. 9 13. 4 9. 0 15. 7 14. 2 11. 2 18. 7 19. 4 20. 9 12. 7 11. 9 6. 0 3. 7 9. 0 13. 4 5. 2 20. 9 19. 4 20. 1 26. 9
Table 7 -7 Calculating a Seasonal Index from Historical Sales Data Quarter 1 Year 2 3 4 Four-Year Quarterly Average 1 49 57 53 73 58. 0 2 77 98 85 100 90. 0 3 90 89 92 98 92. 3 4 79 62 88 78 76. 8 Four-Year sales of 1268/16 = 79. 25 average quarterly sales ªSeasonal Index is 58. 0/79. 25 = 0. 73 Seasonal Index 0. 73ª 1. 13 1. 16 0. 97
Commercial Forecasting Programs Vendor. Package Description Applied Decision SIBYL Systems Price Eighteen distinct time series forecasting techniques. $495 Delphus, Inc. The Spreadsheet Curve fitting, seasonal decomposition $79 Frecaster exponential smoothing, regression for monthly and quarterly data. Delphus, Inc. Autocast II Smart. Software Inc. Smart. Forecasts. II Expert system graphics and data $495 Analysis; projects sales, demand, costs, revenues, time series analysis, multivariate regression. Built-in expert forecasting system tests seasonality, outliers, trends, patterns, and automatically selects best forecasting model. $349
Table 7 -1 Data Used to Calculate Buying Power Index 1991 Effective Buying Income Amount ($000, 000) Total United States $4, 436, 178 Sacramento Metro 25, 572 1991 Total Retail Sales Percentage of United States Percentage Amount of United ($000, 000) States 100. 0% 0. 5764% $2, 241, 319 12, 414 100. 0% 0. 5538% Total Population Percentage Amount of United (000) States 262, 313 1, 482 100. 0% 0. 5653% Buying Power Index 100. 0 0. 5674
Table 7 -2 Estimating the Market Potential for Food Machinery in North Carolina SIC Code 204 205 208 Industry Grain milling Bakery Products Beverages (1) Production Employees (1000) 2. 3 11. 9 (2) Number of Machines Used per 1000 Workers 8 10 2 Market Potential (1 x 2) 18. 4 119. 0 3. 8 141. 2
Table 7 -7: Calculating a Seasonal Index from Historical Sales Data Year Quarter 1 2 3 4 1 49 57 53 73 2 77 98 85 100 3 90 89 92 98 4 79 62 88 78 Four-year sales of 1268/16 = 79. 25 average quarterly sales Four-year Quarterly Average Seasonal Index 58. 0 90. 0 92. 3 76. 8 0. 73 1. 16 0. 97
- Slides: 11