Alternatives to Sales Budgeting Process

Impact of Sales Forecasts on BudgetingImpact of Sales Forecasts on Budgeting

Sales forecasts

Sales budget

Production budget

Direct labor materialsand overhead budgets

Cost of goods sold budget

Budgeted profit and lossstatement

Sales andadministrativeexpense budget

Revenue budget

Figure 7-2Figure 7-2:: Comparing Trend Forecasting MethodsComparing Trend Forecasting Methods

1 2 3 4 50

10

20

30

40

50

Percent rate of change forecast

Unit rate of change forecast

Naïve forecast

Moving average forecast

Time Period

Sal

es

Fitting a Trend Regression to Fitting a Trend Regression to Seasonally Adjusted Sales Data Seasonally Adjusted Sales Data

0 1 2 3 4 5 650

60

70

80

90

63.9

3.6

Y = 63.9 + 3.5 X

Sale

s

Time Period

Forecasting with Moving AveragesForecasting with Moving Averages

1 2 3 4 5 6

Actual sales 49 77 90 79 5798Seasonally adjusted sales 67 68 78 81 7887Two-period moving average forecast seasonally corrected 78.3 70.1 58.089.8Three-period moving average forecast seasonally corrected 68.9 55.289.3Two-period moving average forecast Three-period moving average

forecast

F3 = ( S1 + S2 ) x I3 F4 = ( S1 + S2 + S3 ) x I4

2 3

= ( 67 + 68 ) x 1.16 = 78.3 = ( 67 + 68 + 78 ) x 0.97 = 68.9 2 3

Time Periods

1 2 3 4 5 6 7 8 9 10 11 12

Relations Among Market Potential, Industry Sales, Relations Among Market Potential, Industry Sales, and Company Salesand Company Sales

Companyforecast

ActualForecast

Custom time period

Industryforecast

Industry Sales

Market potential

Company potential

Basicdemandgap

Companydemandgap

PercentagePercentage of of Firms Percentage of Firms that That Use Firms No

Methods Use 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 18.7Extrapolation 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 20.9Quantitative 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

Utilization of Sales Forecasting Methods of 134 FirmsUtilization of Sales Forecasting Methods of 134 Firms

2001 Effective 2001 Total Buying Income Retail 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% 100.0Sacramento Metro 25,572 0.5764% 12,414 0.5538% 1,482 0.5653% 0.5674

Data Used to Calculate Buying Power IndexData Used to Calculate Buying Power Index

(1) (2) Production Number of Machines Market

NAIC Employees Used per 1000 PotentialCode Industry (1000) Workers (1 x 2)

204 Grain milling 2.3 8 18.4205 Bakery Products 11.9 10 119.0 208 Beverages 1.9 2 3.8

141.2

Estimating the Market Potential for Estimating the Market Potential for Food Machinery in North CarolinaFood Machinery in North Carolina

Calculating a Seasonal Index from Historical Sales DataCalculating a Seasonal Index from Historical Sales Data

Four-year Quarterly Seasonal

Quarter 1 2 3 4 Average Index

1 49 57 53 73 58.0 0.73 2 77 98 85 100 90.0 1.13 3 90 89 92 98 92.3 1.16 4 79 62 88 78 76.8 0.97Four-year sales of 1268/16 = 79.25 average quarterly sales

Year