Impact of Sales Forecasts on Budgeting Sales forecasts Sales budget Production budget Direct labor materials and overhead budgets Cost of goods sold budget.

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Presentation transcript:

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

Figure 7-2: Comparing Trend Forecasting Methods Percent rate of change forecast Unit rate of change forecast Naïve forecast Moving average forecast Time Period Sales

Figure 7-3: Fitting a Trend Regression to Seasonally Adjusted Sales Data Y = X Sales Time Period

Forecasting with Moving Averages Actual sales Seasonally adjusted sales Two-period moving average forecast seasonally corrected Three-period moving average forecast seasonally corrected 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 = ( ) x 1.16 = ( ) x = 78.3 = 68.9 Time Periods

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

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 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 Table 7-3 Utilization of Sales Forecasting Methods of 134 Firms

Table 7-7 Calculating a Seasonal Index from Historical Sales Data Four-Year YearQuarterlySeasonal Quarter1234 Average Index ª Four-Year sales of 1268/16 = average quarterly sales ªSeasonal Index is 58.0/79.25 = 0.73

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.

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, % $2,241, % 262, % Sacramento Metro 25, % 12, % 1, % Table 7-1 Data Used to Calculate Buying Power Index

(1) (2) Production Number of Machines Market SIC Employees Used per 1000 Potential Code Industry (1000) Workers (1 x 2) 204 Grain milling Bakery Products Beverages Table 7-2 Estimating the Market Potential for Food Machinery in North Carolina

Table 7-7: Calculating a Seasonal Index from Historical Sales Data Four-year Quarterly Seasonal Quarter Average Index Four-year sales of 1268/16 = average quarterly sales Year