1. Impact of Sales Forecasts on Budgeting Sales forecasts Sales budget Production budget Direct labor materials and overhead budgets Cost of goods sold budget Budgeted profit and loss statement Sales and administrative expense budget Revenue budget

2. Figure 7-2: Comparing Trend Forecasting Methods 1 2 3 4 5 0 10 20 30 40 50 Percent rate of change forecast Unit rate of change forecast Naïve forecast Moving average forecast Time Period Sales

3. Figure 7-3: Fitting a Trend Regression to Seasonally Adjusted Sales Data 0 1 2 3 4 5 6 50 60 70 80 90 63.9 3.6 Y = 63.9 + 3.5 X Sales Time Period

4. Forecasting with Moving Averages 123456 Actual sales497790795798 Seasonally adjusted sales676878817887 Two-period moving average forecast seasonally corrected78.370.158.089.8 Three-period moving average forecast seasonally corrected68.955.289.3 Two-period moving average forecastThree-period moving average forecast F 3 = ( S 1 + S 2 ) x I 3 F 4 = ( S 1 + S 2 + S 3 ) x I 4 2 3 = ( 67 + 68 ) x 1.16 = ( 67 + 68 + 78 ) x 0.97 2 3 = 78.3 = 68.9 Time Periods

5. 1 2 3 4 5 6 7 8 9 10 11 12 Figure 7-1: Relations Among Market Potential, Industry Sales, and Company Sales Company forecast Actual Forecast Custom time period Industry forecast Industry Sales Market potential Company potential Basic demand gap Company demand gap

6. Percentage Percentage of of FirmsPercentage of Firms that That Use Firms No MethodsUse Regularly Occasionally Longer Used Subjective Sales force composite 44.8% 17.2% 13.4% Jury of executive opinion 37.3 22.4 8.2 Intention to buy survey 16.4 10.4 18.7 Extrapolation Naïve 30.6 20.1 9.0 Moving Average 20.9 10.4 15.7 Percent rate of change 19.4 13.4 14.2 Leading indicators 18.7 17.2 11.2 Unit rate of change 15.7 9.7 18.7 Exponential smoothing 11.2 11.9 19.4 Line extension 6.0 13.4 20.9 Quantitative Multiple regressing 12.7 9.0 20.9 Econometric 11.9 9.0 19.4 Simple regression 6.0 13.4 20.1 Box-Jenkins 3.7 5.2 26.9 Table 7-3 Utilization of Sales Forecasting Methods of 134 Firms

7. Table 7-7 Calculating a Seasonal Index from Historical Sales Data Four-Year YearQuarterlySeasonal Quarter1234 Average Index 149575373 58.0 0.73ª 2779885 100 90.0 1.13 390899298 92.3 1.16 479628878 76.8 0.97 Four-Year sales of 1268/16 = 79.25 average quarterly sales ªSeasonal Index is 58.0/79.25 = 0.73

8. Commercial Forecasting Programs VendorPackageDescription Price Applied DecisionSIBYLEighteen distinct time series $495 Systemsforecasting techniques. Delphus,Inc.The SpreadsheetCurve fitting, seasonal decomposition $79 Frecasterexponential smoothing, regression for monthly and quarterly data. Delphus, Inc.Autocast IIBuilt-in expert forecasting system $349 tests seasonality, outliers, trends, patterns, and automatically selects best forecasting model. SmartSoftwareSmartForecastsIIExpert system graphics and data $495 Inc.Analysis; projects sales, demand, costs, revenues, time series analysis, multivariate regression.

9. 1991 Effective1991 Total Buying IncomeRetail Sales Total Population Percentage Percentage Percentage Buying Amount of United Amount of United Amount of United Power ($000,000) States ($000,000) States (000) States Index Total United States $4,436,178 100.0% $2,241,319 100.0% 262,313 100.0% 100.0 Sacramento Metro 25,5720.5764% 12,414 0.5538% 1,482 0.5653% 0.5674 Table 7-1 Data Used to Calculate Buying Power Index

10. (1) (2) Production Number of Machines Market SIC Employees Used per 1000 Potential Code Industry (1000) Workers (1 x 2) 204 Grain milling 2.3 8 18.4 205 Bakery Products 11.9 10 119.0 208 Beverages 1.9 2 3.8 141.2 Table 7-2 Estimating the Market Potential for Food Machinery in North Carolina

11. Table 7-7: Calculating a Seasonal Index from Historical Sales Data Four-year Quarterly Seasonal Quarter 1 2 3 4 Average Index 149575373 58.0 0.73 2779885100 90.0 1.13 390899298 92.3 1.16 479628878 76.8 0.97 Four-year sales of 1268/16 = 79.25 average quarterly sales Year