$$ Entrepreneurial Finance, 5th Edition Adelman and Marks 6-1 Pearson Higher Education ©2010 by...

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

Entrepreneurial Finance, 5th EditionAdelman and Marks

6-1

Pearson Higher Education©2010 by Pearson Education, Inc.Upper Saddle River, NJ 07458

Chapter 6

Forecasting and Pro Forma Financial Statements

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-2


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-3


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-4


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-5


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-6


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-7


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?

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-8


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-9


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-10


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-11


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-12


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-13


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-14


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-15


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-16


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-17


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-18


Time Series Models (continued)

Moving average model Weighted moving average model Exponential smoothing model Linear regression model

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-19



References for mathematics in forecasting:› A = Actual observation of the variable to be forecast. › F = Forecast of the variable.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-20


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-21



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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-22


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-23


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:

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-24


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

...

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-25


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-26


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-27


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:

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-28


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-29


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-30


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-31


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-32


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-33


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-34


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-35


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-36


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-37


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-38


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:

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-39


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-40


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-41


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)

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-42


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-43


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-44


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-45


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-46


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-47


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-48


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-49


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-50


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-51


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-52


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-53


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:

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-54


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-55


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-56


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

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-57


Startup Business Costs

Startup costs are those associated with getting the enterprise up and running prior to generating any sales.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-58


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.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$


6-59


Figure 6-5 GANTT CHART, BASIC ENTRY FORM

Project: Manager: Phone: Page: ___

Plan Area: Manager: Phone: of ______ PagesTIME PERIODS

TaskNO...

TASKDESCRIPTION:

TaskTime RESOURCE PHONE

$$ Entrepreneurial Finance, 5th Edition Adelman and Marks 6-1 Pearson Higher Education ©2010 by...

Documents

Transcript of $$ Entrepreneurial Finance, 5th Edition Adelman and Marks 6-1 Pearson Higher Education ©2010 by...