Impact of Sales Forecasts on Budgeting

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Transcript Impact of Sales Forecasts on Budgeting

Alternatives to Sales
Budgeting Process
Sales forecasts
Sales budget
Production budget
Revenue budget
Direct labor materials
and overhead budgets
Sales and
administrative
expense budget
Cost of goods sold budget
Budgeted profit and loss
statement
Impact of Sales Forecasts on Budgeting
50
40
Percent rate of change forecast
30
Sales
Unit rate of change forecast
20
Naïve forecast
Moving average forecast
10
0
1
2
3
4
5
Time Period
Figure 7-2:
Comparing Trend Forecasting Methods
90
Sales
80
3.6
70
Y = 63.9 + 3.5 X
63.9
60
50
0
1
2
3
4
5
Time Period
Fitting a Trend Regression to
Seasonally Adjusted Sales Data
6
Forecasting with Moving Averages
Actual sales
98
Seasonally adjusted sales
87
Two-period moving average forecast
seasonally corrected
89.8
Three-period moving average
forecast seasonally corrected
89.3
Two-period moving average forecast
F3 = ( S1 + S2 ) x I3
2
= ( 67 + 68 ) x 1.16 = 78.3
2
1
Time Periods
2
3
4
5
49
77
90
79
57
67
68
78
81
78
78.3
70.1
58.0
68.9
55.2
Three-period moving average
forecast
F4 = ( S1 + S2 + S3 ) x I4
3
= ( 67 + 68 + 78 ) x 0.97 = 68.9
3
6
Market potential
Industry
forecast
Basic
demand
gap
Industry Sales
Company potential
Company
forecast
Company
demand
gap
Actual
Forecast
1
2
3
4
5
6
7
8
9
10
11
12
Custom time period
Relations Among Market Potential, Industry Sales,
and Company Sales
Utilization of Sales Forecasting Methods of 134 Firms
Methods
Subjective
Sales force composite
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
Percentage of
Firms that
Use Regularly
Percentage
of Firms
That Use
Occasionally
Percentage of
Firms No
Longer Used
44.8%
37.3
16.4
17.2%
22.4
10.4
13.4%
8.2
18.7
30.6
20.9
19.4
18.7
15.7
11.2
6.0
20.1
10.4
13.4
17.2
9.7
11.9
13.4
9.0
15.7
14.2
11.2
18.7
19.4
20.9
12.7
11.9
6.0
3.7
9.0
9.0
13.4
5.2
20.9
19.4
20.1
26.9
Data Used to Calculate Buying Power Index
2001 Effective
Buying Income
Amount
($000,000)
Total United States $4,436,178
Sacramento Metro
25,572
2001 Total
Retail Sales
Total Population
Percentage
of United
States
Percentage
Amount
of United
($000,000)
States
Percentage
of United
States
Buying
Power
Index
100.0%
0.5764%
$2,241,319
12,414
100.0%
0.5653%
100.0
0.5674
100.0%
0.5538%
Amount
(000)
262,313
1,482
Estimating the Market Potential for
Food Machinery in North Carolina
NAIC
Code
204
205
208
Industry
Grain milling
Bakery Products
Beverages
(1)
Production
Employees
(1000)
2.3
11.9
1.9
(2)
Number of Machines
Used per 1000
Workers
8
10
2
Market
Potential
(1 x 2)
18.4
119.0
3.8
141.2
Calculating a Seasonal Index from Historical Sales Data
Year
Quarter
1
2
3
4
1
49
57
53
73
2
77
98
85
100
3
90
89
92
98
4
79
62
88
78
Four-year sales of 1268/16 = 79.25 average quarterly sales
Four-year
Quarterly
Average
Seasonal
Index
58.0
90.0
92.3
76.8
0.73
1.13
1.16
0.97