Part I

THE BIG PICTURE

Sales Management Resources:

Estimating Potentials and Forecasting Sales

IMPACT OF SALES FORECASTS ON BUDGETING

Sales forecastsSales forecasts

Sales budgetSales budget

Production budgetProduction budget

Direct labor materialsDirect labor materialsand overhead budgetsand overhead budgets

Cost of goodsCost of goodssold budgetsold budget

Budgeted profitBudgeted profitand loss statementand loss statement

Sales andSales andadministrativeadministrative

expense budgetexpense budgetRevenue budgetRevenue budget

Figure SMR2-1Relations Among Market Potential, Industry Sales, and Company Sales

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

Companyforecast

ActualForecast

Custom time periodCustom time period

Industryforecast

Industry Sales

Market potential

Company potential

BasicBasicDemandDemand

GapGap

CompanyCompanyDemandDemand

GapGap

Table SMR2-1

Data Used to Calculate Buying Power Index

2004 Effective2004 Effective

Buying IncomeBuying Income2004 Total2004 Total

Retail SalesRetail Sales2004 Estimated2004 Estimated

Total PopulationTotal Population

Amount($000,00

0)

Percentage of

United States

Amount($000,00

0)

Percentage of

United States

Amount($000,00

0)

Percentage of

United States

Buying Power Index

Total Total United United StatesStates

$5,466,880

100.00%$3,906,4

82100.0% 292.936 100.0% 100.00

Atlanta Atlanta MetroMetro

$ 99,691

1.824%$

69,0711.768% 4.704 1.606% 1.7636

Table SMR2-2

Estimating the Market Potential for Food Machinery in North Carolina

NAICNAIC

CodeCode IndustryIndustry

(1)(1)

Production Production EmployeesEmployeesaa

(2)(2)

Number of Number of Machines Machines Used per Used per

1000 1000 WorkersWorkersbb

Market Market PotentialPotential

(1x2)(1x2)

31123112 Grain Grain MillingMilling 878878 2424 21.121.1

31223122 Tobacco Tobacco Mfg.Mfg. 9,5719,571 1515 143.6143.6

31213121 BeveragesBeverages 3,5383,538 33 10.6 10.6

175.3175.3

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.

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

Table SMR2-3

Utilization of Sales Forecasting Methods of 134 Firms

Table SMR2-4

Calculating a Seasonal Index from Historical Sales Data

a Seasonal index is 58.0/9.25 = 0.73

QuarteQuarterr

11 22 33 44Four-YearFour-YearQuarterly Quarterly AverageAverage

SeasonaSeasonal Indexl Index

11 4949 5757 5353 7373 58.058.0 0.730.73aa

22 7777 9898 8585 100100 90.090.0 1.131.13

33 9090 8989 9292 9898 92.392.3 1.161.16

44 7979 6262 8888 7878 76.876.8 0.970.97

Four year sales of 1268/16 = 79.25 average quarterly salesFour year sales of 1268/16 = 79.25 average quarterly sales

Quarter

1 2 3 4

Actual sales 49 77 90 79Naïve forecast 49 77 90

Quarter

1 2 3 4

Actual sales 49 77 90 79Naïve forecast 49 77 90

Percentage forecasting error = forecast – actual actual

Percentage forecasting error = 49-77 = 36% 77

NAÏVE FORECASTS ANDPERCENTAGE FORECASTING ERROR

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

Figure SMR2-2

Comparing Trend Forecasting Methods

SalesSales

Time PeriodTime Period

 

 

%100/)(

1 xn

actualactualforecastMAPE

n

i

MEAN ABSOLUTE PERCENTAGE ERROR(MAPE)

 

where

Ft+1 = forecast for the next periodSt = sales in the current periodn = number of periods in the moving average

n

SSSF ntttt

111

...

CALCULATING A MOVING AVERAGE FORECAST

Quarter

1 2 3 4

Actual sales 49 77 90 79

Two-periodmoving average 63

83.5

Quarter

1 2 3 4

Actual sales 49 77 90 79

Two-periodmoving average 63

83.5

MOVING AVERAGEFORECASTING EXAMPLE

 

where= smoothed sales forecast for period t and the forecast for period t + 1

α = the smoothing constantSt = actual sales in period t

-1 = smoothed forecast for period t – 1

11 )1( ttt SSS

tS

tS

CALCULATING AN EXPONENTIALSMOOTHING FORECAST

Quarter

1 2 3 4

Actual sales 49 77 90 79

Smoothed forecast 60.2 72.1

Quarter

1 2 3 4

Actual sales 49 77 90 79

Smoothed forecast 60.2 72.1

EXPONENTIAL SMOOTHING FORECASTING EXAMPLE

0 1 2 3 4 5 650

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

SalesSales

Time PeriodTime Period

1 2 3 4 5 6

Actual sales 49 77 90 79 57 98Seasonally adjusted sales 67 68 78 81 78 87Two-period moving average forecast seasonally corrected 78.3 70.1 58.0 89.8Three-period moving average forecast seasonally corrected 68.9 55.2 89.3Two-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 = ( 67 + 68 + 78 ) x 0.97 2 3 = 78.3 = 68.9

Time Periods

FORECASTING WITH MOVING AVERAGES