$$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ Entrepreneurial Finance, 5th Edition Adelman and Marks 6-1 Pearson Higher Education ©2010 by Pearson Education, Inc. Chapter 6 Forecasting and Pro Forma Financial Statements
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Chapter 6
Forecasting and Pro Forma Financial Statements
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Learning Objectives
Understand the basic steps used in selecting a forecasting model.
Know how to evaluate a forecasting model. Given a business situation, choose the proper forecasting
model. Calculate a forecast using time series data.
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Learning Objectives (continued)
Explain the role that the Mean Absolute Deviation (MAD) plays in selecting a forecasting model.
Understand the relationship among a business’s revenue base, sales forecast, assets, and need for financing.
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Learning Objectives (continued)
Construct pro forma financial statements from available data on a proposed or existing business.
Apply the percentage of sales method in determining any required new financing needed for a business.
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Forecasting
A forecast is a quantifiable estimate of future demand. Forecasting in business is the process of estimating the
future demand for our products and services. Forecasting for the financial manager also requires
estimates of future interest rates.
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Forecasting Process
Determine the type of model to be used. Determine the forecast horizon. Select one or more forecasting models. Evaluate the models. Apply the chosen model. Monitor and control the model.
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Determine the Type of Model to be Used
1) Who will be using the forecast and what information do they require?
2) How relevant is historical data, and what is its availability?
3) How accurate does the forecast have to be?
4) What is the time period of the forecast?
5) How much time do we have to develop the forecast?
6) What is the cost or benefit (value) of this forecast to our company?
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Determine the Forecast Horizon Inverse relationship between forecast accuracy and time
horizon.› The longer the time horizon the more inaccurate the forecast will
be.
Time horizon should be at least as long as time period of strategic plan.
Product life cycles influence length of forecasts.› Technological product sales would have a short forecast. › Milk sales would have a long forecast.
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Select One or More Forecasting Models
Must consider the basic six questions.1) Who will be using the forecast and what information do they
require?
2) How relevant is historical data, and what is its availability?
3) How accurate does the forecast have to be?
4) What is the time period of the forecast?
5) How much time do we have to develop the forecast?
6) What is the cost or benefit (value) of this forecast to our company?
Some instances will require a combination of forecasting models.
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Evaluate the Models
Compare the accuracy of forecasting models by use of Mean Absolute Deviation (MAD)
Remember the model assists the forecaster, it does not make the decision.
Changing market and economic conditions require us to constantly evaluate our forecasting models.
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Apply the Chosen Model
Application in business is used to determine future requirement.
Application of the model and specific units of measurement used depends on the area of the business that uses the model:› Marketing wants demand of product.› Production requires units.› Finance requires dollars.› Personnel requires human resources.
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Monitor and Control the Model Model should allow us to develop controls in a three-step
process:› Determine standard for measuring progress toward forecast.› Measure actual performance against this standard.› Take corrective action.
When the forecasting model no longer allows the manager to do this, then a new forecasting model must be developed.
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Types of Forecasting Models
Judgmental models, which use qualitative methods Time series models, which use quantitative methods Causal models, which use cause-and-effect methods
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Judgmental Models
Judgmental models are qualitative and essentially use estimates based on expert opinion. › Survey of Sales Forces: most appropriate for manufacturing and
wholesale firms.› Surveys of Customers: applicable to all firms. Customers express
preference for new or modified products.
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Judgmental Models (continued)
› Historical Analogy most appropriate for firms that have several outlets. Introduction of new product which has characteristics similar to previous products.
› Market Research can include surveys, tests, and observations. Results are statistically extrapolated to develop forecasts of demand for products.
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Judgmental Models (continued)
Delphi Method uses a panel of experts to obtain a consensus of opinion. Used primarily for unique new products or processes for which no previous data exist.
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Time Series Models
Time series forecasting models normally use historical records that are readily available within the firm or industry to predict future sales. › For this reason they are often referred to as internal or intrinsic
models. › Assumption in time series forecasting is that past sales are a fairly
accurate predictor of future sales.
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Time Series Models (continued)
Moving average model Weighted moving average model Exponential smoothing model Linear regression model
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Time Series Models (continued)
References for mathematics in forecasting:› A = Actual observation of the variable to be forecast. › F = Forecast of the variable.
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Time Series Models (continued) References for mathematics in forecasting (continued):
› t = current time period. Time periods can be a measure of any time period (e.g., hour, day, month, year, decade, etc.). If time periods are measured in months and the current month is April, then t = April.
› t-1 = one time period in the past. If time is being measured in months and t is April then one time period in the past is March.
› t-2 = two time periods in the past, etc. If time is being measured in months and t is April then two time periods in the past is February.
› t+1 = one time period in the future. If time is being measured in months and t is April then one time period in the future is May.
› t+2 = two time periods in the future. If time is being measured in months and t is April then two time periods in the future is June.
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Time Series Models (continued)
References for mathematics in forecasting (continued):› = the difference between two numbers. For example AF
would be actual observation of variable minus forecast.› = Sum of several numbers, normally in a column.› n = the number of observations used in a calculation. The n for
months in a year equal 12 and n for years in a decade is 10.
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Moving Average Model
Moving average model assumes that actual sales for some recent previous time periods are the best predictor of future sales.
It assumes that each time period taken in succession has an equal influence on the prediction of future sales.
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Moving Average Model
The procedure is to obtain the arithmetic average of actual sales for several past time periods.
n
AAAF ttnt
1])1[( ...1t
246.33 3
250) 244 (245
1t
April
ttt
F
AAAF
312
For a three-month forecast of year 1 data:
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Table 6-1 Moving Average Model
Formula for Moving Average:
Year0
Year 1 Jan 1 245Feb 2 244Mar 3 250Apr 4 260 246.33 13.67 13.67May 5 265 251.33 13.67 13.67 249.75 15.25Jun 6 260 258.33 1.67 1.67 254.75 5.25Jul 7 255 261.67 (6.67) 6.67 258.75 3.75Aug 8 245 260.00 (15.00) 15.00 260.00 15.00Sep 9 240 253.33 (13.33) 13.33 256.25 16.25Oct 10 255 246.67 8.33 8.33 250.00 5.00Nov 11 265 246.67 18.33 18.33 248.75 16.25Dec 12 270 253.33 16.67 16.67 251.25 18.75
Year 2 Jan 13 250 263.33 (13.33) 13.33 257.50 7.50Feb 14 250 261.67 (11.67) 11.67 260.00 10.00Mar 15 258 256.67 1.33 1.33 258.75 0.75Apr 16 267 252.67 14.33 14.33 257.00 10.00May 17 273 258.33 14.67 14.67 256.25 16.75Jun 18 278 266.00 12.00 12.00 262.00 16.00Jul 19 260 272.67 (12.67) 12.67 269.00 9.00Aug 20 256 270.33 (14.33) 14.33 269.50 13.50Sep 21 255 264.67 (9.67) 9.67 266.75 11.75Oct 22 270 257.00 13.00 13.00 262.25 7.75Nov 23 275 260.33 14.67 14.67 260.25 14.75Dec 24 283 266.67 16.33 16.33 264.00 19.00
Year 3 Jan 25 276.00 270.75|A-F|= 62.00 255.33 232.25n= 21 21 20MAD= 2.95 12.16 11.61
Absolute Deviation |A-F|
4 Month Moving
Average
Moving Average = Forecast
Time Period
Actual Sales
3 Month Moving
AverageMonth
Actual Numerical Deviation = Actual -Forecast
Absolute Deviation |A-F|
n
AAAF ttnt
t
1])1[(
1
...
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Mean Absolute Deviation
Mean absolute deviation (MAD) is a tool used to measure the forecasting error of a model. › MAD is simple to calculate and provides us with a method of
determining which weights, or alpha, to choose for our model and which model is most appropriate for predicting sales.
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Mean Absolute Deviation (continued)
› Absolute deviation is the absolute difference between forecasted sales and actual sales.
› The absolute value of any number is positive and is represented mathematically by vertical lines drawn on either side of the number or formula.
F-Adeviation Absolute
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Average Arithmetic Deviation vs. Average Absolute Deviation
The average arithmetic deviation is the actual difference between two numbers:
17.02
34.0
2
13.33)(13.67deviation arithmetic Average
5.132
27
2
13.3313.67deviation absolute Average
The average absolute deviation using the same numbers:
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Mean Absolute Deviation (continued)
Mean absolute deviation (MAD) is the measure of the overall forecast error. › MAD represents the average difference between our forecast and
actual sales data.
n
FAMAD
nMAD
sales Forecastsales Actual
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210
220
230
240
250
260
270
280
290
Year1 J an
Feb Mar Apr May J un J ul Aug Sep Oct Nov Dec Year2
J an
Feb Mar Apr May J un J ul Aug Sep Oct Nov Dec Year3
J an
Year and Month
Sal
es i
n U
nit
s
Actual
Forecast Sales
Figure 6-1Plot of Actual Sales and Forecast Sales
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Weighted Moving Average Model
Weighted moving average model assumes that the closest time period is a more accurate predictor of future sales than previous time periods.› Previous time periods do have some influence on future sales.› Forecaster will assign weights to the time periods based on his or
her judgment.
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Weighted Moving Average Model (continued)
The sum of the weights normally equal one.› If the forecaster does not want to use a sum of one, then we sum
the weights and use this sum as the denominator in our equation. › The value of each weight is based on how much of an influence
the forecaster believes the corresponding time period has on overall sales.
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Weighted Moving Average Model (continued)
The formula for this method, using three months, is as follows:
W
AWAWAWF ttt
t31221
1
Using weights of 0.1, 0.3 & 0.6 with actual sales of 245,244, & 250 we solve as follows:
Year 1Ft+1 = (W1)(At22) + (W2)(At21) + (W3)(At)
F Apr = (0.1)(245) + (0.3)(244) + (0.6)(250)
F Apr = 247.70
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Year MonthTime
PeriodActual Sales
W1=0.1, W2=0.3, W3=0.6 |A-F|
W1=0.25, W2=0.35, W3=0.40 |A-F|
W1=4 W2=5 W3=8 |A-F|
0Year 1 Jan 1 245
Feb 2 244Mar 3 250Apr 4 260 247.70 12.30 246.65 13.35 247.06 12.94May 5 265 255.40 9.60 252.50 12.50 253.29 11.71Jun 6 260 262.00 2.00 259.50 0.50 260.00 0.00Jul 7 255 261.50 6.50 261.75 6.75 261.47 6.47Aug 8 245 257.50 12.50 259.25 14.25 258.82 13.82Sep 9 240 249.50 9.50 252.25 12.25 251.47 11.47Oct 10 255 243.00 12.00 245.50 9.50 245.00 10.00Nov 11 265 249.50 15.50 247.25 17.75 248.24 16.76Dec 12 270 259.50 10.50 255.25 14.75 256.18 13.82
Year 2 Jan 13 250 267.00 17.00 264.50 14.50 265.00 15.00Feb 14 250 257.50 7.50 260.75 10.75 259.41 9.41Mar 15 258 252.00 6.00 255.00 3.00 254.71 3.29Apr 16 267 254.80 12.20 253.20 13.80 253.76 13.24May 17 273 262.60 10.40 259.60 13.40 260.35 12.65Jun 18 278 269.70 8.30 267.15 10.85 267.71 10.29Jul 19 260 275.40 15.40 273.50 13.50 273.94 13.94Aug 20 256 266.70 10.70 269.55 13.55 268.35 12.35Sep 21 255 259.40 4.40 262.90 7.90 262.35 7.35Oct 22 270 255.80 14.20 256.60 13.40 256.47 13.53Nov 23 275 264.10 10.90 261.25 13.75 262.29 12.71Dec 24 283 271.50 11.50 268.25 14.75 268.82 14.18
Year 3 Jan 25 279.30 276.95 277.59|A-F| = 218.90 244.75 234.94n = 21.00 21.00 21.00MAD = 10.42 11.65 11.19
Table 6-2 Weighted Moving Average Model
Formula for weighted moving average:
W
AWAWAWF tttt
t
3122
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Exponential Smoothing Model
Exponential smoothing model uses a smoothing constant, alpha (), as an adjustment in determining the forecast. › A smoothing constant is a value assigned by the forecaster to
adjust the forecast based on the forecaster’s assumption of the relationship between sales in one time period and sales in the next time period.
› Alpha can have any value between 0 and 1; however, alpha is normally 0.1, 0.2, or 0.3.
› The higher the value of alpha, the greater the emphasis given to sales for the current time period.
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Exponential Smoothing Model (continued)
With exponential smoothing we must begin with an assumed rather than an actual forecast.
))(1()(1 ttt FAF The formula is
)()(1 tttt FAFF or
90.244
5.2204.24)245)(9.0(4.24)245)(1.01()244)(1.0(
))(1()(
1Year
1
March
March
ttt
F
F
FAF
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Year |A F| |A F| |A F|
0Year 1 Jan 1 245
Feb 2 244 245.00 245.00 245.00Mar 3 250 244.90 5.10 244.80 5.20 244.75 5.25Apr 4 260 245.41 14.59 245.84 14.16 246.06 13.94May 5 265 246.87 18.13 248.67 16.33 249.55 15.45Jun 6 260 248.68 11.32 251.94 8.06 253.41 6.59Jul 7 255 249.81 5.19 253.55 1.45 255.06 0.06Aug 8 245 250.33 5.33 253.84 8.84 255.04 10.04Sep 9 240 249.80 9.80 252.07 12.07 252.53 12.53Oct 10 255 248.82 6.18 249.66 5.34 249.40 5.60Nov 11 265 249.44 15.56 250.73 14.27 250.80 14.20Dec 12 270 250.99 19.01 253.58 16.42 254.35 15.65
Year 2 Jan 13 250 252.89 2.89 256.86 6.86 258.26 8.26Feb 14 250 252.60 2.60 255.49 5.49 256.20 6.20Mar 15 258 252.34 5.66 254.39 3.61 254.65 3.35Apr 16 267 252.91 14.09 255.11 11.89 255.49 11.51May 17 273 254.32 18.68 257.49 15.51 258.36 14.64Jun 18 278 256.19 21.81 260.59 17.41 262.02 15.98Jul 19 260 258.37 1.63 264.07 4.07 266.02 6.02Aug 20 256 258.53 2.53 263.26 7.26 264.51 8.51Sep 21 255 258.28 3.28 261.81 6.81 262.38 7.38Oct 22 270 257.95 12.05 260.45 9.55 260.54 9.46Nov 23 275 259.16 15.84 262.36 12.64 262.90 12.10Dec 24 283 260.74 22.26 264.89 18.11 265.93 17.07
Year 3 Jan 25 262.97 268.51 270.20|A-F| = 233.54 221.36 219.80n = 22.00 22.00 22.00MAD = 10.62 10.06 9.99
Formulas for Exponential Smoothing Model:
Table 6-3 Exponential Smoothing Model
Time Period
Actual Sales (A)
Forecast for =0.1
Forecast for =0.2
Forecast for =0.25Month
tttt
ttt
FAFF
FAF
1
1 1
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Linear Regression Model
Linear regression uses a statistical method known as least squared regression.› Four areas of variation:
– Seasonal variation is caused by the predictable shopping habits of our customers.
– Trend variation is variation caused by growth or decline in demand for our product or service over time.
– Cyclical variation is caused by general economic factors that affect our industry.
– Noise is random variation in our data that is not explained by the preceding factors.
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Linear Regression Model (continued)
Linear regression is used to determine two factors: › The slope of the regression line› The intercept of the regression line
.bxay Basic formula for the regression line is:
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Regression line defined.bxay
Where:y is the dependent variable. A dependent variable is one that relies on other variables for its value. x is the independent variable. An independent variable is one that does not depend on other variables for its value. In forecasting models, x is often a time period.a is the y intercept. The y intercept is the value of y when x equals 0.b is the slope of the regression line.
2 1
2 1
y yb
x x
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SCATTER DIAGRAM
200
210
220
230
240
250
260
270
280
290
0 5 10 15 20 25 30
TIME PERIOD IN MONTHS
SALES IN $(000)
Figure 6-2
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FIGURE 6-3 Sales Chart with Regression Line
210
220
230
240
250
260
270
280
290
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
TIME IN MONTHS
SA
LE
S IN
$(0
00)
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Year Month x2
xyYear 1 JAN 1 245 1 245
FEB 2 244 4 488 MAR 3 250 9 750 APR 4 260 16 1,040 MAY 5 265 25 1,325 JUN 6 260 36 1,560 JUL 7 255 49 1,785 AUG 8 245 64 1,960 SEP 9 240 81 2,160 OCT 10 255 100 2,550 NOV 11 265 121 2,915 DEC 12 270 144 3,240
Year 2 JAN 13 250 169 3,250 FEB 14 250 196 3,500 MAR 15 258 225 3,870 APR 16 267 256 4,272 MAY 17 273 289 4,641 JUN 18 278 324 5,004 JUL 19 260 361 4,940 AUG 20 256 400 5,120 SEP 21 255 441 5,355 OCT 22 270 484 5,940 NOV 23 275 529 6,325 DEC 24 283 576 6,792
SUMS ( 300 6,229 4,900 79,027
y=a+bx
Time Period
(x)
Actual Sales
(y)
Using the formulas below , w e substitute from Table 6-3 above and obtain an intercept (a) of 246.8841 and a slope (b) of 1.0126
Table 6-4 Calculation of the Regression Line
0126.1600,27
948,27
000,90600,117
700,868,1648,896,1
)300()900,4)(24(
)229,6)(300()027,79)(24(
)(xn
yx-xyn=b
8841.246600,27
000,814,6
)000,90()600,117(
)100,708,23()100,522,30(
)300()900,4)(24(
)027,79)(300()229,6)(900,4(
)(
x=a
222
222
2
x
xxn
xyxy
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y= a + bxa= 246.8841b= 1.0126
Year Month
0 246.88Year 1 Jan 1 245 247.90 0.99 245
Feb 2 244 248.91 0.98 244Mar 3 250 249.92 1.00 250Apr 4 260 250.93 1.04 260May 5 265 251.95 1.05 265Jun 6 260 252.96 1.03 260Jul 7 255 253.97 1.00 255
Aug 8 245 254.98 0.96 245Sep 9 240 256.00 0.94 240Oct 10 255 257.01 0.99 255Nov 11 265 258.02 1.03 265Dec 12 270 259.04 1.04 270
Year 2 Jan 13 250 260.05 0.96 250Feb 14 250 261.06 0.96 250Mar 15 258 262.07 0.98 258Apr 16 267 263.09 1.01 267May 17 273 264.10 1.03 273Jun 18 278 265.11 1.05 278Jul 19 260 266.12 0.98 260
Aug 20 256 267.14 0.96 256Sep 21 255 268.15 0.95 255Oct 22 270 269.16 1.00 270Nov 23 275 270.17 1.02 275Dec 24 283 271.19 1.04 283
Year 3 Jan 25 272.20 0.97 265Feb 26 273.21 0.97 265Mar 27 274.22 0.99 272Apr 28 275.24 1.03 282May 29 276.25 1.04 288Jun 30 277.26 1.04 288Jul 31 278.27 0.99 276
Aug 32 279.29 0.96 268Sep 33 280.30 0.94 265Oct 34 281.31 1.00 281Nov 35 282.33 1.02 289Dec 36 283.34 1.04 296
Table 6-5, Forecast of sales, including seasonal adjustment.
Time x
Seasonal Ratio
(A)/(F)
Seasonal Forecast of
Sales
Regression Forecast (F)
y = a + bxActual Sales (A) Jan-01 - Dec-02
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Regression Forecast with Seasonal Factors Included
220
240
260
280
300
320
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38
Time in Months
Sa
les
in
($
00
0)
Actual sales first 24 Months, forecast sales months 25-36
Regression Line
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Causal Models
Causal models are also known as external or exogenous models. › Causal models take into account variables in the general economy
that affect the revenue obtained by a company. › Causal models can be simple or very complex.› Most of them require multiple regression analysis, which is
normally beyond the scope of a small business manager.
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Practical Sales Forecasting for Startup Businesses
Steps to take for a sales forecast:› Listing what you know:
– Expertise, experience, knowledge of charges and fees.
– Previous revenue and cost information based on experience.› Research similar companies via EDGAR competing company
annual reports, or industry-specific publications.› List three types of expenses:
– Startup
– Fixed
– Variable› Develop a revenue forecast
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Forecast of Revenue for a Startup
Table 6-6 Forecast of Revenue for a Startup Service or Trade Busines
Hours per day
Days per Week
Billed Hourly Rate
Hourly Wage
Overtime Wage
Number of
Workers
Weeks Worked per Year
Best Case 10 6 65 30 45 1 50Worst Case 4 4
Best CaseWorst Case Best Case
Worst Case
Gross Revenue 3,900$ 1,040$ 195,000$ 52,000$ Labor Cost 2,100 480 105,000 24,000 Material Cost - - Gross Profit 1,800$ 560$ 90,000$ 28,000$
Weekly Annually
Note: All wages over 40 hours per week are paid at an hourly wage of 1.5 times the hourly wage.
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Pro Forma Financial Statements
A pro forma financial statement is a projected statement based on the forecast.
The three basic pro forma statements are:› Pro forma income statement› Pro forma cash budget› Pro forma balance sheet
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Sales 200,000$ 250,000$ COG 100,000 125,000$ Gross Profit 100,000$ 125,000$ Operating Expenses Rent 24,000 24,000 Utilities 3,000 3,600 Salaries 60,000 63,000 Insurance 2,400 3,000 Depreciation 5,000 7,000 Equipment 4,500 7,800 Total Operating Expenses 98,900$ 108,400$ Operating Profit 1,100$ 16,600$ Interest Expense 5,000 6,800 Net Profit (3,900)$ 9,800$
Table 6-7, Pro Forma Income Statement
Income Statements for Time Periods Indicated
Actual Sales for
2007
Pro Forma Sales for
2008
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October November December January February March April May June
Sales 17,625$ 20,250$ 22,500$ 13,000$ 14,500$ 14,000$ 19,000$ 23,000$ 24,500$
Current Month Collection at 30% of Sales 5,288 6,075 6,750 3,900 4,350 4,200 5,700 6,900 7,350
Outstanding Current Month Accounts Receivable (AR) 12,338 14,175 15,750 9,100 10,150 9,800 13,300 16,100 17,150
60% of AR Collected Month Follow ing Sale 7,403 8,505 9,450 5,460 6,090 5,880 7,980 9,660
40% of AR Collected in 2nd Month Follow ing Sale 4,935 5,670 6,300 3,640 4,060 3,920 5,320
Total Receipts 20,190$ 19,020$ 16,110$ 13,930$ 15,640$ 18,800$ 22,330$
Accounts Payable (AP) Previous Month 10,125$ 11,250$ 6,500$ 7,250$ 7,000$ 9,500$ 11,500$
Operating Expenses 8,450 8,450 8,450 8,450 8,450 8,450
Interest Payments 567 567 567 567 567 567
Total Payments 20,267$ 15,517$ 16,267$ 16,017$ 18,517$ 20,517$
Total Receipts 19,020$ 16,110$ 13,930$ 15,640$ 18,800$ 22,330$
Total Payments 20,267 15,517 16,267 16,017 18,517 20,517
Net Cash Flow (1,247)$ 593$ (2,337)$ (377)$ 283$ 1,813$
Net Cash Flow (1,247)$ 593$ (2,337)$ (377)$ 283$ 1,813$
Beginning Cash Balance 4000 4,000$ 4,000$ 4,000 4,000 4,000
Total Cash Balance 2,753$ 4,593$ 1,663$ 3,623 4,283 5,813
Monthly Loan or (Repayment) 1,247 (593) 2,337 377 (283) (1,813)
Cumulative Loan Balance 9000 10,247 9,654 11,991 12,368 12,085 10,272
Ending Cash Balance 4,000$ 4,000$ 4,000$ 4,000$ 4,000$ 4,000$ 4,000$
Note: Shaded rows, Sales and Outstanding Current Month Accounts Receivable do not represent cash flow.
Monthly Cash Payments
Monthly Cash Budget
Cash Budget With Borrowing and Repayment
Note: Shaded row s, Sales and Outstanding Current Month Accounts Receivable and cumulative loan balance do not represent cash f low .
Table 6-8 Pro Forma Cash Budget
Monthly Cash Receipts
Actual Sales 2007 Pro Forma Sales and Cash Receipts 2008
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Actual for 2007
Pro Forma for 2008
Current Assets Cash 4,000$ 8,406$ Accounts Receivabe 21,420 28,500 Inventory 15,000 18,750 Total Current Assets 40,420$ 55,656$ Fixed Assets Net Machinery & Equipment 30,000 43,000
Total Assets 70,420$ 98,656$
Liabilities and Owner's EquityCurrent Liabilities Accounts Payable 11,250$ 15,000$ Salaries Payable 2,500$ 2,625$ Notes Payable 9,000 Total Current Liabilities 22,750$ 17,625$ Long-Term Liabilities Long-Term Loan 30,000$ 50,000$
Total Liabilities 52,750$ 67,625$ Owner's Equity 17,670 31,031
Total Liabilities & Owner's Equity 70,420$ 98,656$
Table 6-9 Pro Forma Balance Sheet
Balance Sheets for Year Ending December 31
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Pro Forma Balance Sheet Using Percentage of Sales
Percentage of sales method is based on the fact that assets and liabilities historically vary with sales. › Thus any increase in sales will cause a subsequent buildup in both
assets and liabilities. › Both profit margins and dividend (owner) payout ratios determine
the amount of internal financing that can be applied to support increased asset buildup.
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Pro Forma Balance Sheet Using Percentage of Sales (continued)
Balance Sheet Entry in $Percentage of Sales
Actual Net Sales in $$4,000
Percentage of Sales 0.02 or 2%$200,000
The basic formula for percentage of sales is:
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Sales taken from Table 6-7 200,000$ 250,000$
Actual sales for
2005
Percentage of 2005 sales
Pro Forma sales for
2006Current Assets Cash 4,000$ 2.00% 5,000$ Accounts Receivabe 21,420 10.71% 26,775 Inventory 15,000 7.50% 18,750
Total Current Assets 40,420$ 20.21% 50,525$ Fixed Assets Net Machinery & Equipment 30,000 15.00% 37,500
Total Assets 70,420$ 35.21% 88,025$
-$ Liabilities and Owner's Equity -$ Current Liabilities -$ Accounts Payable 11,250$ 5.63% 14,063$ Salaries Payable 2,500 1.25% 3,125 Notes Payable 9,000 4.50% 11,250
Total Current Liabilities 22,750$ 11.38% 28,438$ Long-Term Liabilities Long-Term Loan 30,000$ 15.00% 37,500$
Total Liabilities 52,750$ 26.38% 65,938$ Owner's Equity 17,670 8.84% 22,088
Total Liabilities & Owner's Equity 70,420$ 35.21% 88,025$
Table 6-10 Pro Forma Balance Sheet using percentage of sales method
Balance Sheets for Year Ending December 31
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Using Percentage of Sales to Determine New Financing
The following is used based on pro forma balance sheet (Table 6-9):
2 Required Financing ( )( )(1 Owner Payout)
$70,420 $52,750 Required Financing ($250,000-$200,000) ($250,000 $200,000)
$200,000 $200,000
Assets LiabilitiesSales Sales S P
Sales Sales
3,900($250,000) (1 0.66)
$200,000
Required Financing ($50,000)(0.3521)-($50,000)(0.2638)-($250,000)(0.0195)(0.34)
Required Financing $17,605 $13,190 $1,657.50
Required Financing $6,072.50
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Monitoring and Controlling the Business
Monitoring in finance is normally accomplished by use of budgets and pro forma financial financial statements.› Most small businesses require only two budgets for monitoring
and controlling purposes, the capital budget and the cash budget.
Controlling process requires three steps:› Establishing a standard› Comparing actual performance to the standard› Taking corrective action if necessary
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Startup Business Costs
Startup costs are those associated with getting the enterprise up and running prior to generating any sales.
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Gantt Chart
A Gantt chart, Figure 6-5, shows all tasks that have to be performed and the time that it takes to accomplish these tasks.
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Figure 6-5 GANTT CHART, BASIC ENTRY FORM
Project: Manager: Phone: Page: ___
Plan Area: Manager: Phone: of ______ PagesTIME PERIODS
TaskNO...
TASKDESCRIPTION:
TaskTime RESOURCE PHONE