Directory UMM :wiley:Public:college:Dalrymple:

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

[image:2.720.65.649.69.388.2]

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

S

al

[image:3.720.51.680.76.406.2]

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

S

al

es

Forecasting with Moving Averages

1 2 3 4 5 6

Actual sales 49 77 90 79 57 98

Seasonally adjusted sales 67 68 78 81 78 87

Two-period moving average forecast

seasonally corrected 78.3 70.1 58.0 89.8

Three-period moving average

forecast seasonally corrected 68.9 55.2 89.3

Two-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

[image:5.720.23.618.38.490.2]

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

Percentage

Percentage 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 18.7

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

[image:7.720.36.708.124.393.2]

Table 7-7 Calculating a Seasonal Index from Historical Sales Data

Four-Year

Year

Quarterly

Seasonal

Quarter

1

2

3

4

Average

Index

1

49

57

53

73

58.0

0.73ª

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

0.97

Four-Year sales of 1268/16 = 79.25 average quarterly sales

Commercial Forecasting Programs

Vendor Package

Description

Price

Applied Decision SIBYL

Eighteen distinct time series

$495

Systems

forecasting techniques.

Delphus,Inc.

The Spreadsheet Curve fitting, seasonal decomposition $79

Frecaster

exponential smoothing, regression for

monthly and quarterly data.

Delphus, Inc.

Autocast II

Built-in expert forecasting system $349

tests seasonality, outliers, trends,

patterns, and automatically selects

best forecasting model.

SmartSoftware

SmartForecastsII Expert system graphics and data

$495

Inc.

Analysis; projects sales, demand, costs,

1991 Effective 1991 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% 100.0

Sacramento Metro 25,572 0.5764% 12,414 0.5538% 1,482 0.5653% 0.5674

(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

[image:10.720.46.688.80.298.2]

[image:11.720.23.701.86.285.2]

Table 7-7: Calculating 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 0.97

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