1. Part I THE BIG PICTURE Sales Management Resources: Estimating Potentials and Forecasting Sales

2. 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

3. Figure SMR2-1 Relations Among Market Potential, Industry Sales, and Company Sales 1 2 3 4 5 6 7 8 9 10 11 12 Company forecast Actual Forecast Custom time period Industry forecast Industry Sales Market potential Company potential BasicDemandGap CompanyDemandGap

4. Table SMR2-1 Data Used to Calculate Buying Power Index 2004 Effective Buying Income 2004 Total Retail Sales 2004 Estimated Total Population Amount ($000,000) Percentage of United States Amount ($000,000) Percentage of United States Amount ($000,000) Percentage of United States Buying Power Index Total United States $5,466,880100.00%$3,906,482100.0%292.936100.0%100.00 Atlanta Metro $ 99,6911.824%$ 69,0711.768%4.7041.606%1.7636

5. Table SMR2-2 Estimating the Market Potential for Food Machinery in North Carolina NAICCodeIndustry(1) Production Employees a (2) Number of Machines Used per 1000 Workers b Market Potential (1x2) 3112 Grain Milling 8782421.1 3122 Tobacco Mfg. 9,57115143.6 3121Beverages3,538310.6 175.3 a The production employee data are from the 2002 Economic Census of Manufacturing, Geographic Area Series, North Carolina, p. NC1 & 2. The codes are the new NAIC codes b Estimated by manufacturer from past sales data.

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 SMR2-3 Utilization of Sales Forecasting Methods of 134 Firms

7. Table SMR2-4 Calculating a Seasonal Index from Historical Sales Data a Seasonal index is 58.0/9.25 = 0.73 Quarter1234 Four-Year Quarterly Average Seasonal Index 14957537358.0 0.73 a 277988510090.01.13 39089929892.31.16 47962887876.80.97 Four year sales of 1268/16 = 79.25 average quarterly sales

8. Quarter 1234 Actual sales49779079 Naïve forecast497790 Quarter 1234 Actual sales49779079 Naïve forecast497790 Percentage forecasting error = forecast – actual actual Percentage forecasting error = 49-77 = 36% 77 NAÏVE FORECASTS AND PERCENTAGE FORECASTING ERROR

9. 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 Figure SMR2-2 Comparing Trend Forecasting Methods Sales Time Period

10. MEAN ABSOLUTE PERCENTAGE ERROR (MAPE)

11. where F t+1 = forecast for the next period S t = sales in the current period n= number of periods in the moving average CALCULATING A MOVING AVERAGE FORECAST

12. Quarter 1234 Actual sales49779079 Two-period moving average6383.5 Quarter 1234 Actual sales49779079 Two-period moving average6383.5 MOVING AVERAGE FORECASTING EXAMPLE

13. where = smoothed sales forecast for period t and the forecast for period t + 1 α= the smoothing constant S t = actual sales in period t -1 = smoothed forecast for period t – 1 CALCULATING AN EXPONENTIAL SMOOTHING FORECAST

14. Quarter 1234 Actual sales49779079 Smoothed forecast60.272.1 Quarter 1234 Actual sales49779079 Smoothed forecast60.272.1 EXPONENTIAL SMOOTHING FORECASTING EXAMPLE

15. 0 1 2 3 4 5 6 50 60 70 80 90 63.9 3.6 Y = 63.9 + 3.5 X Figure SMR2-3 Fitting a Trend Regression to Seasonally Adjusted Sales Data Sales Time Period

16. 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 FORECASTING WITH MOVING AVERAGES