For your final case project, you are required to prepare a ‘pitch deck’ to support the launch of a new product or service that addresses a current problem or issue but also has direct connection to the health care industry. The overarching intent of this presentation is to obtain funding from a commercial or private financier. Thus, the final product should be professional, clear, appropriately structured, logical, and concise.)
Copyright 2018. Health Administration Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
C AS E
PAC I F I C H E A LT H C A R E (A )
BOND VALUATION
13
Pacific Healthcare is an investor-owned hospital chain that owns and
operates nine hospitals in Washington, Oregon, and Northern California.
Marcia Long, a recent graduate of a prominent health administration program,
has just been hired by Washington Medical Center, Pacific’s largest hospital.
Like all new management personnel, Marcia must undergo three months
of intensive indoctrination at the system level before joining the hospital.
Marcia began her indoctrination in January 2018. Her first assignment
at Pacific was to review its latest annual report. This was a stroke of luck
for Marcia because her father owned several bonds issued by Pacific, and
she was especially interested in whether her father had made a good investment. To glean more information about the bonds, she examined Note E
to Pacific’s consolidated financial statements, which lists the company’s
long-term debt obligations, including its first mortgage bonds, installment
contracts, and term loans. Exhibit 13.1 contains information on four of the
first-mortgage bonds listed in Pacific’s annual report. All four bonds pay
interest semiannually. (For more information on bond ratings, see Standard
& Poor’s website at www.standardandpoors.com or the Moody’s Investors
Service website at www.moodys.com.)
Pacific’s chief financial officer, Hugo Welsh, found out about Marcia’s
interest in the firm’s debt financing. “Because you are so interested in our
financial structure,” he said, “I want you to do the bond valuation and make
a presentation to our executive committee.” Hugo then asked Marcia to
perform the calculations required to complete exhibit 13.2 and to think
about some of the questions that members of the committee might ask her
about the numbers.
© Fou nd at ion of ACHE, 2018. Repr oduc t i o n w i t h o u t p e r mi s s i o n i s p r o h i b i t e d .
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AN: 1792719 ; George Pink.; Gapenski’s Cases in Healthcare Finance, Sixth Edition
Account: s4264928.main.eds
89
90
EXHIBIT 13.1
Pacific Healthcare:
Long-Term Bonds
EXHIBIT 13.2
Pacific Healthcare:
Long-Term Bond Stated
(Nominal) Yields
C ase s in Health care F in a n c e
Face
Current
Par
Coupon
Maturity
Years to
Bond
Amount
Price
Value
Rate
Date
Maturity
Rating
$ 48,000,000
$ 800.00
$1,000
4.50%
12/31/2018 5
A+
$ 32,000,000
$ 865.49
$1,000
8.25%
12/31/2028
15
A+
$100,000,000
$1,220.00
$1,000
12.625%
12/31/2038
25
A+
$ 64,000,000
$ 747.48
$1,000
7.375%
12/31/2038
25
A+
Annual
2018
2018
1/1/2019
2018
2018
Face
Yield to
Coupon
Current
Expected
Capital Gain
Total
Amount
Maturity
Payments
Yield
Price
Yield
Yield
$ 48,000,000
$ 32,000,000
$100,000,000
$ 64,000,000
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CHAPTER
FORECASTING
9
Learning Objectives
Copyright 2019. Health Administration Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
After reading this chapter, students will be able to
•
•
•
•
articulate the importance of a good sales forecast,
describe the attributes of a good sales forecast,
apply demand theory to forecasts, and
use simple forecasting tools appropriately.
Key Concepts
•
•
•
•
•
Making and interpreting forecasts are important jobs for managers.
Forecasts are planning tools, not rigid goals.
Sales and revenue forecasts are applications of demand theory.
Changes in demand conditions usually change forecasts.
Good forecasts should be easy to understand, easy to modify, accurate,
transparent, and precise.
• Forecasts combine history and judgment.
• Assessing external factors is vital to forecasting.
9.1 Introduction
Making and interpreting forecasts are important jobs for managers. Sales
forecasts are especially important because many decisions hinge on what the
organization expects to sell. Pricing decisions, staffing decisions, product
launch decisions, and other crucial decisions are based on the organization’s
revenue and sales forecasts.
Inaccurate or misunderstood forecasts can hurt businesses. The organization can hire too many workers or too few. It can set prices too high or
too low. It can add too much equipment or too little. At best, these sorts of
forecasting problems will cut into profits; at worst, they may drive an organization out of business.
137
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
The consequences of bad or misapplied forecasts are particularly serious in healthcare. For example, underestimating the level of demand in the
short term may result in stock shortages at a pharmacy or too few nurses
on duty at a hospital. In both cases, the healthcare organization will suffer
financially and, more important, put patients at risk. It will suffer because the
costs of meeting unexpected demand are high and because the long-term
consequences of failing to meet patients’ needs are significant. The best outcome in this case will be unhappy patients; the worst outcome will be that
physicians stop referring patients to the organization.
Overestimating sales can also have serious long-term effects. A hospital
may add too many beds because its census forecast was too high. This surplus
will depress profits for some time because the facility will have hired staff and
added equipment to meet its overestimated forecast, and the costs of hiring and paying new employees and buying new equipment will substantially
exceed actual sales profits. In extreme cases, bad forecasts may drive a firm
out of business. A facility that borrows heavily in anticipation of higher sales
that do not materialize may be unable to repay those debts. Bankruptcy may
be the only option.
Sales and revenue forecasts are applications of demand theory. The factors that change sales and revenues also change demand. The most important
influences on demand are the price of the product, rivals’ prices for the product, prices for complements and substitutes, and demographics. Recognizing
these influences can simplify forecasting considerably because it focuses our
attention on tracking what has changed.
9.2 What Is a Sales Forecast?
A sales forecast is a projection of the number of units (e.g., bed days, visits,
doses) an organization expects to sell. The forecast must specify the time
frame, marketing plan, and expected market conditions for which it is valid.
A forecast is a planning tool, not a rigid goal. Conditions may change.
If they do, the organization’s plan needs to be reassessed. Good management
usually involves responding effectively to changes in the environment, not
forging ahead as though nothing has shifted. In addition, fixed sales goals
create incentives to behave opportunistically (that is, for employees to try
to meet their goals instead of the organization’s goals). For example, sales
staff may harm the organization by making overblown claims of a product’s
effectiveness to meet their sales goals, even though their actions will harm the
company in the long run. Alternatively, sales managers may bid on unprofitable managed care contracts just to meet goals.
Whenever possible, a sales forecast should estimate the number of
units expected to be sold, not revenues. The number of units to be sold
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determines staffing, materials, working capital, and other needs. In addition,
costs often vary unevenly with volume. A small reduction in volume may save
an entire shift’s worth of wages (thereby avoiding considerable cost), or an
increase in sales may incur a small cost increase if it requires no additional
staff or equipment.
The dollar volume of sales can vary in response to factors that do not
affect the resources needed to produce, market, or service the sales. Discounts and price increases are examples of such factors. Revenues can vary
even though neither volume nor costs change. Finally, managers can easily
forecast revenue given a volume forecast. In general, managers should build
their revenue estimates on sales volume estimates.
Good forecasts have five attributes. They should be
1. easy to understand,
2. easy to modify,
3. accurate (i.e., they contain the most probable actual values),
4. transparent about how variable they are, and
5. precise (i.e., they give the analyst as little wiggle room as possible).
These attributes often conflict. Managers may need to underplay how imprecise simple forecasts are because their audience is not prepared to consider
variation. As Aven (2013) points out, many decision makers are more comfortable working with a single, precise estimate, even though it may be inaccurate. Precision and accuracy always conflict because a more precise forecast
(80 to 85 visits per day) will always be less accurate than a less precise forecast
(70 to 95 visits per day). Offering decision makers several precise scenarios is
usually a good compromise. For example, busy decision makers generally can
use a forecast such as “Our baseline forecast is 82 visits per day for the next
three months, our low forecast is 75 visits per day, and our high forecast is
89 visits per day.”
Case 9.1
Forecasting Supply Use
More and more healthcare institutions seek to
reduce costs while increasing the quality of care.
Accurate forecasts of the use of medical supplies represent an important
element of this effort. Overordering supplies drives up costs, and underordering supplies also can drive up costs and compromise care.
(continued)
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Case 9.1
The stakes can be high. Caldwell Memorial Hospital, a 110-bed hospital in North Carolina, saved
$2.62 million in less than six months by consolidating and eliminating excess supplies (Belliveau 2016). The hospital used
a Lean approach to inventory management, which involves streamlining
and simplifying the inventory and ordering systems.
In addition, a number of hospitals have expanded their use of justin-time inventory management (Green 2015). This method reduces, but
does not eliminate, the need for forecasting accuracy. Some supplies
are highly specialized and are used intermittently, so they must be
ordered well in advance. The savings can be substantial. Mercy Hospital in Chicago was able to reduce its inventory by 50 percent using
just-in-time inventory management (Green 2015).
(continued)
Discussion Questions
• What share of hospital costs do supplies represent?
• Why would overordering supplies drive up costs?
• Why would underordering supplies drive up costs?
• Can you offer examples of Lean inventory management? Does it
work well?
• Can you offer examples of just-in-time inventory management?
Does it work well?
• Can you offer examples of supplies that have to be available at all
times?
• What are the main challenges to making accurate forecasts of
supply use in hospitals?
• How would you forecast supply use in the emergency department?
Why?
• How would you forecast supply use in hospital clinics? Why?
• Would you use judgment in making these forecasts? Why?
• Would you use statistical models in making these forecasts? Why?
• How are supply chain forecasts different for hospitals than for
retail? For manufacturing?
9.3 Forecasting
All forecasts combine history and judgment. History is the only real source
of data. For example, sales can be forecasted only on the basis of data on past
sales of a product, past sales of similar products, past sales by rivals, or past
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sales in other markets. History is an imperfect guide to the future, but it is
an essential starting point.
Judgment is also essential. It provides a basis for deciding what data
to use, how to use the data, and what statistical techniques, if any, to use.
In many cases (e.g., introductions of new products or new competitive situations), managers who have insufficient data will have to base their forecasts
mainly on judgment.
As mentioned in section 9.2, a forecast must specify the time frame,
marketing plan, and expected market conditions for which it is valid. Changes
in any of these factors will change the forecast.
A forecast applies to a given period. Extrapolating to a longer or
shorter period is risky; conditions may change. The time frame varies according to the forecast’s use. For example, a staffing plan may need a forecast
for only the next few weeks. Additional staff can be hired over a longer time
horizon. In contrast, budget plans usually need a forecast for the coming
year. Organizations usually set their budgets a year in advance on the basis
of projected sales. Strategic plans usually need a forecast for the next several
years. Longer forecasts are generally less detailed and less reliable, but managers know to take these factors into account when they develop and use them.
Forecasts should be as short term as possible. A forecast for next
month’s sales will usually be more accurate than forecasts for the distant
future, which are likely to be less accurate because important facts will have
changed. Your competitors today are likely to be your competitors in a month.
Your competitors in two years are likely to be different from your competitors
today, so a forecast based on current market conditions will be poor.
Marketing plan changes will influence the forecast. A clinic that
increases its advertising expects visits to increase. A forecast that does not
consider this increase will usually be inaccurate. Increasing discounts to pharmacy benefits managers should result in increased sales for a pharmaceutical
firm. Again, a forecast that does not account for additional discounts will usually be deficient. Any major changes in an organization’s marketing efforts
should change forecasts. If they do not, the organization should reassess the
usefulness of its marketing initiatives.
Changes in market conditions also influence forecasts. For example, a
major plant closing would probably reduce a local plastic surgeon’s volume.
Plant employees who had intended to undergo plastic surgery may opt to
delay this elective procedure, and prospective patients who work for similar
plants may defer discretionary spending in fear that they too may lose their
jobs. Alternatively, a hospital closure will probably cause a competing hospital
to forecast more inpatient days. Historical data have limited value in projecting such an effect if a similar closure has not occurred in the past. Approval
of a new drug by the Food and Drug Administration should cause a pharmaceutical firm to forecast a decrease in sales for its competing product. This
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
percentage
adjustment
An adjustment
that increases
or decreases the
average of the
past n periods.
(The adjustment
is essentially a
best guess of what
is expected to
happen in the next
year.)
moving average
The unweighted
mean of the
previous n data
points.
seasonalized
regression
analysis
A least squares
regression that
includes variables
to identify
subperiods (e.g.,
weeks) that
historically have
had above- or
below-trend sales.
sort of change in market conditions is familiar, and the firm’s marketing staff
will probably draw on experience to predict the loss.
Analysts routinely use three forecasting methods: percentage adjustment, moving averages, and seasonalized regression analysis. If the
data are adequate and the market has not changed too much, seasonalized
regression analysis is the preferred method. However, whether the data are
adequate and whether the market has changed too much are judgment calls.
Percentage adjustment increases or decreases the last period’s sales
volume by a percentage the analyst deems sensible. For example, if a hospital had an average daily census of 100 the previous quarter, and an analyst
expects the census to fall an average of 1 percent per quarter, a reasonable
forecast would be a census of 99. Because of its simplicity, managers often
use percentage adjustment; however, this simplicity is also a shortcoming.
In principle, a manager could choose an arbitrary percentage adjustment.
Without some requirement that percentage adjustments be well justified,
this approach may not yield accurate forecasts. For example, a manager
might justify a request for a new position based on a forecast that average
daily census will increase by 5 percent, even though the average daily census
had been falling for the last 14 quarters. In addition, percentage adjustment
does not allow for seasonal effects. (Seasonal effects are systematic tendencies for particular days, weeks, months, or quarters to have above- or belowaverage volume.)
Demand theory can be used to add rigor to percentage adjustments.
For example, if the price of a product has changed, an estimate of the percentage change in sales can be calculated by multiplying the percentage
change in price by the price elasticity of demand. So, if an organization has
chosen to raise prices by 3 percent and faces a price elasticity of demand of
−4, sales will drop by 12 percent. Similar calculations can be used if the price
of a substitute, the price of a complement, or consumer income has changed.
The moving-average method uses the average of data from recent
periods to forecast sales. This method works well for short-term forecasts,
although it tends to hide emerging trends and seasonal effects. Exhibit 9.1
shows census data and a one-year moving average for a sample hospital.
Exhibit 9.1 also illustrates the calculation of a seasonalized regression
format. Excel was used to estimate a regression model with a trend (a variable that increases in value as time passes) and three quarter indicators. The
variable Q1 has a value of 1 if the data are from the first quarter; otherwise,
its value is 0. Q2 equals 1 if the data are from the second quarter, and Q3
equals 1 if the data are from the third quarter. For technical reasons, the average response in the fourth quarter is represented by the constant. A negative
regression coefficient for trend indicates that the census is in a downward
trend. The results also show that the typical third-quarter census is smaller
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C hap ter 9 : Forec asting
Quarter
Census
1
First
Second
Third
Trend
99
1
0
0
1
2
109
0
1
0
2
3
101
0
0
1
3
4
107
0
0
0
4
5
104
104.0
1
0
0
5
6
116
105.3
0
1
0
6
7
100
107.0
0
0
1
7
8
106
106.8
0
0
0
8
9
103
106.5
1
0
0
9
10
107
106.3
0
1
0
10
11
90
104.0
0
0
1
11
12
105
101.5
0
0
0
12
13
102
101.3
1
0
0
13
14
94
101.0
0
1
0
14
15
98
97.8
0
0
1
15
16
104
99.8
0
0
0
16
17
99
99.5
1
0
0
17
18
105
98.8
0
1
0
18
19
94
101.5
0
0
1
19
20
102
100.5
0
0
0
20
21
100
100.0
1
0
0
21
22
Moving Average
143
EXHIBIT 9.1
Census Data
for a Sample
Hospital
100.3
Seasonalized Regression Model
Coefficient
t-statistic
Intercept
108.811
40.90
R2 = 0.55
First quarter
−3.968
−1.53
F(4,20) = 4.98
Second quarter
0.732
0.27
p = 0.01
Third quarter
−8.534
−3.16
Trend
−0.334
−2.16
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
mean absolute
deviation
The average
absolute
difference between
a forecast and the
actual value. (It is
absolute because
it converts both 9
and −9 to 9. The
Excel function
=ABS( ) performs
this conversion.)
EXHIBIT 9.2
An Overview of
the Forecasting
Process
than average because the coefficient for Q3 is large, negative, and statistically
significant.
The forecast based on seasonalized regression analysis is calculated as
follows: 108.811 + (−0.334 × 22) + 0.732. Here, 108.811 is the estimate of
the constant, −0.334 is the estimate of the trend coefficient, 22 is the quarter
to which the forecast applies, and 0.732 is the estimate of the Q2 coefficient.
Therefore, the seasonalized forecast is 102.2, slightly higher than the forecast
based on the moving average. Overall the seasonalized forecast is a little more
accurate than the one-year moving average. The mean absolute deviation
for the regression is 2.3 for periods 5 through 21, and the mean absolute
deviation for the moving average is 4.0.
Exhibit 9.2 shows an overview of the forecasting process. The main
message of this exhibit is that a forecast is one part of the overall product
management process. In addition, the forecast will change as managers’
Assess internal and external factors.
Develop an initial forecast.
Develop an initial marketing strategy and then modify the forecast
and marketing strategy until they are consistent.
Monitor sales, internal factors, external factors,
and the marketing strategy.
Modify the forecast and marketing strategies as needed.
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Naïve
Forecast
Two-Period MovingAverage Forecast
Month
Sales
February
189
March
217
189
April
211
217
203
May
239
211
214
June
234
239
225
July
243
234
236.5
145
EXHIBIT 9.3
Simple
Forecasting
Techniques:
Naïve and
Moving-Average
Forecasts
assessments of relevant internal factors (e.g., cost and quality), external factors (e.g., the competitive environment and payment levels), and the marketing plan change.
A naïve forecast uses the value for the last period as the forecast for
the next period—in other words, a 0 percent adjustment forecast. Exhibit
9.3 shows an example of a naïve forecast. A moving-average forecast uses the
average of the last n values, where n is the number of preceding values used in
the forecast. For example, the first entry in the Two-Period Moving-Average
Forecast column in exhibit 9.3 equals (189 + 217) ÷ 2, or 203.
To compare forecasting techniques, analysts sometimes use the mean
absolute deviation, which is the average of the forecast’s absolute deviations
from the actual value. (When using the absolute deviation, it does not matter if a value is higher or lower than the actual value; all the deviations are
positive numbers.) For April through July, the naïve forecast in exhibit 9.3
has a mean absolute deviation of 12.0, and the two-period moving-average
forecast has a mean absolute deviation of 12.1. From this perspective, the
naïve forecast performs a little better.
These (and other) mechanistic forecasting methods do not allow managers to explore how changes in the environment are likely to affect sales.
How would changes in insurance coverage change sales? Naïve forecasts and
moving-average forecasts are little help in such situations.
9.4 What Matters?
Assessment of external factors (i.e., factors beyond the organization’s control) is vital to forecasting. General economic conditions are a prime example.
Expected inflation and interest rates are good indicators of the state of the
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
economy. Local market conditions, such as business rents and local wages,
also play an important role.
Government actions also can have a major impact on healthcare firms.
For example, changes in Medicare rates affect most healthcare firms. Alternatively, regulations can have a significant effect on costs. Expansion of Medicaid eligibility can have major effects on some hospitals and minor effects
on others. Keep in mind that these sorts of changes will also affect most of
your competitors, but forecasters would be ill advised to ignore changes in
government policy.
The plans of key competitors must also be considered. Closure of a
competing clinic or hospital can increase volume significantly and quickly.
Introduction of a generic drug can have a dramatic effect on a pharmaceutical manufacturer. Changes in competitors’ pricing policies can have a major
impact on sales.
Technological change is always an important issue. If a rival gains a
technological advantage, your sales can drop sharply. For example, if a rival
introduces minimally invasive coronary artery bypass graft surgery, admissions to your cardiac unit will probably drop significantly until you adopt
similar technology. In other cases, your own advances may affect sales of
substitute products. For example, introduction of highly reliable magnetic
resonance imaging may sharply reduce the demand for conventional colonoscopy. Keep in mind, however, that if you do not introduce technologies
that add value for your customers, someone else will. A decision not to
introduce an attractive product because it will cannibalize sales is usually
a mistake.
Finally, although markets usually change slowly, differences in general
market characteristics (e.g., median income and percentage with insurance
coverage) may be important in forecasting sales of a new product.
Assessment of internal factors (i.e., factors within an organization’s
control) is also vital to forecasting. For example, existing production may
limit sales, or production may have limited sales in the past. If so, changes in
capacity or productivity need to be considered. Changes in the availability of
resources and personnel can also have a powerful effect on sales. For many
healthcare organizations, the entry or exit of a key physician can dramatically
shape volume. In addition, changes in the size, support, composition, and
organization of the sales staff can affect sales dramatically. For instance, a
small drug firm may experience a large increase in sales if one of its products
is marketed by a larger firm’s sales staff.
Failures or improvements in key systems can also have dramatic effects
on sales. Breakdowns in a clinic’s phone or scheduling system may drive away
potential customers. Fixing the phone system, in contrast, might be the most
effective marketing campaign the clinic ever launched.
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Case 9.2
147
Mistakes to Avoid When Making
Forecasts
Business plans require a sales forecast. Scott Fishman, the CEO of
Envisage, sees three common mistakes in business plans (Fishman
2015):
• They forecast “hockey stick” revenue growth.
• They forecast smoothly rising trend lines.
• They lack convincing evidence of market size.
A “hockey stick” forecast—a revenue graph shaped like a hockey
stick—involves limited revenues initially followed by explosive growth.
It is a potentially effective sales technique to use in discussions with
executives and investors because it suggests that the business opportunity might be extremely valuable.
In contrast, smoothly rising trend lines do not seem plausible from
an economic standpoint. The number of customers and their consumption of any product is typically finite. Furthermore, any true blockbuster
product will attract competition.
Every new product faces a complex environment: features and
benefits, competitive environment, regulatory conditions, payment
models, distribution, pricing, market positioning, and so forth. A genuinely new product will have multiple unknowns in its market. If there
are no unknowns, it is not really a new product. A convincing forecast
demands market research, an honest recognition of what is not known,
and a strategy for resolving some of the unknowns.
Discussion Questions
• What is problematic about a “hockey stick” forecast?
• Can you find an example of a product that displayed “hockey stick”
revenue growth?
• What is problematic about a forecast with a smoothly rising trend
line?
• Can you find an example of a product that displayed smoothly rising
revenue growth?
• From an economic point of view, what is implausible about
smoothly rising trend lines?
(continued)
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Case 9.2
• C
an you find an example of a product that
wildly underperformed early forecasts?
• Can you find an example of a product that
wildly overperformed early forecasts?
• What external factors might cause below-forecast sales? Aboveforecast sales?
• What internal factors might cause below-forecast revenues? Aboveforecast revenues?
• What are examples of new products with uncertain prospects in
healthcare?
(continued)
9.5 Conclusion
Making and interpreting forecasts are important tasks for healthcare managers. Not only are most crucial decisions based on sales forecasts, but also the
consequences of overestimating or underestimating demand can be catastrophic. Overestimating demand can put the financial future of an organization at risk, whereas underestimating demand can compromise the care of
patients and harm the organization’s reputation.
Analysts should apply demand theory to their sales forecasts to better
recognize changes. Demand theory limits what analysts need to consider: the
price of the product, the price of substitutes, and the price of complements.
The key idea of demand theory is that the out-of-pocket price drives most
consumer demand. The amount the consumer has to pay depends largely
on the terms of the insurance contract. Is the product covered? What is the
required copayment? Changes in the answers to these two questions can shift
sales sharply. The same concerns affect the prices of substitutes. The most
important substitutes are similar products offered by rivals, but other products that meet some of the same needs should also be considered.
Demographic factors are important. Population size, income per
capita, the age distribution of the population, the ethnic makeup of the
population, and the insurance coverage of the population are some examples.
Although vital, demographic factors tend to be stable in the short term.
Demographics are much more important in long-range forecasts.
“Prediction is very difficult, especially if it’s about the future.” This
saying, noted in chapter 4, reveals a core truth about forecasting: You often
will be wrong. Knowing that, a shrewd manager will make decisions that can
be modified as conditions change. The shrewd manager will also know which
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149
data are likely to be the most problematic or most variable and will monitor
those data carefully.
Management decisions require sales forecasts. Off-the-cuff forecasts
often fail to consider key factors and can lead to risky decisions. Imperfect forecasts can be used to make decisions as long as you recognize that
your predictions will sometimes be wrong and you structure your decisions
accordingly.
Exercises
9.1 The table lists visits for each of the four clinics operated by your
system. You anticipate that volumes will increase by 4 percent next
year. Forecast the number of visits for each clinic, and explain what
assumptions underlie your forecasts. For example, are you sure that
all the clinics can serve additional clients?
Period
Clinic 1
Clinic 2
Clinic 3
Clinic 4
Total
This year
16,640
41,600
24,960
33,280
116,480
Next year
?
?
?
?
121,139
9.2 Your data for the clinics in exercise 9.1 suggest that clinic 2 is
operating at capacity and is highly efficient. Its output is unlikely to
increase. Furthermore, clinic 4 has unused capacity but is unlikely
to attract additional patients. How would these facts change your
answer to the question in exercise 9.1? Continue to assume that
overall volume will rise to 121,139.
9.3 You estimate that the price elasticity of demand for clinic visits
is −0.25. You anticipate that a major insurer will increase the
copayment from $20 to $25. This insurer covers 40,000 of your
patients, and those patients average 2.5 visits per year. What is your
forecast of the change in the number of visits?
9.4 A major employer has just added health insurance coverage for its
employees. Consequently, 5,000 of your patients will pay a $30
copayment rather than the list price of $100 per visit. These patients
average 2.2 visits per year. You believe the price elasticity of demand
is between −0.15 and −0.35. What is your forecast of the change in
the number of visits?
9.5 The following table shows data on asthma-related visits. Is there
evidence that these visits vary by quarter? Can you detect a trend?
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
A powerful test would be to run a multiple regression in Excel. (To
do this, you will need the free Analysis ToolPak for your version of
Excel. Microsoft [2018] offers guidance on how to load and use the
Analysis ToolPak.) To test for quarterly differences, create a variable
called Q1 that equals 1 if the data are for the first quarter and 0
otherwise, a variable called Q2 that equals 1 if the data are for the
second quarter and 0 otherwise, and a variable called Q4 that equals
1 if the data are for the fourth quarter and 0 otherwise. (Because
you will accept the default, which is to have a constant term in your
regression equation, do not include an indicator variable for Quarter
3.) Also create a variable called Trend that increases by 1 each
quarter.
Year
Q1
Q2
2014
Q3
Q4
1,513
1,060
2015
1,431
1,123
994
679
2016
1,485
886
1,256
975
2017
1,256
1,156
1,163
1,062
2018
1,200
1,072
1,563
531
2019
1,022
1,169
9.6 Your marketing department estimates that Medicare urology visits
equal 5 − (1.0 × C) + (−6.5 × TO) + (5 × TR) + (0.01 × Y). Here,
C denotes the Medicare copayment (now $20), TO is waiting
time in your clinic (now 30 minutes), TR is waiting time in your
competitor’s clinic (now 40 minutes), and Y is per capita income
(now $40,000).
a. How many visits do you anticipate?
b. Medicare’s allowed fee is $120. What revenue do you anticipate?
c. What might change your forecast of visits and revenue?
9.7 Because of fluctuations in insurance coverage, the average price paid
out of pocket (P) by patients of an urgent care center varied, as the
table shows. The number of visits per month (Q) also varied, and
an analyst believes the two are related. The analyst also thinks the
data show a trend. Run a regression of Q on P and Period to test
these hypotheses. Then use the estimated parameters a, b, and c
and the values of Month and P to predict Q (number of visits). The
prediction equation is Q = a + (b × Month) + (c × P).
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Month
1
2
3
P
$21 $18
Q
193 197 256
4
5
$15 $24 $18
179
231
6
7
$21 $18
8
9
10
11
12
$15 $20 $19 $24
$20
214 247 273 223 225
198
151
211
9.8 Use the data in exercise 9.7 to answer these questions:
a. Calculate the naïve estimator, which is Qt = Qt − 1.
b. Calculate the two-period moving-average forecast.
c. Calculate the mean absolute deviation for the regression forecast,
the naïve forecast, and the two-period moving-average forecast.
d. Which forecast seems to perform the best? Why?
9.9 Sales data are displayed in the table.
Month
Sales
Month
Sales
February
224
January
260
March
217
February
284
April
211
March
280
May
239
April
271
June
234
May
302
July
243
June
286
August
238
July
297
September
243
August
301
October
251
September
309
November
259
October
314
December
270
a. Calculate the naïve estimator, which is Salest = Salest − 1.
b. Calculate the two-period and three-period moving averages.
c. Calculate the mean absolute deviation for each of the forecasting
methods.
9.10 A pharmaceutical company produces a sinus medicine. Monthly sales
(in thousands of doses) for the past three years are shown in the
table on the next page.
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Jan
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Dec
6,788 8,020
1,848
410
586 2,260 2,232
8,018
9,384
6,916 5,698
6,940
9,136
7,420
3,350
1,998
1,972
3,572 4,506 10,474
13,358
8,232
9,628
7,826
3,528
2,126
2,070
3,762
14,040 8,646 8,634 10,782
4,754
11,010
8,218 10,248
a. Develop a regression model that allows for trend and seasonal
components. Obtain the Excel output for this model.
b. Calculate a two-period moving-average forecast.
c. Compare the mean absolute deviations for these approaches.
d. Use one of these models to forecast sales for each month of
year 3.
References
Aven, T. 2013. “On How to Deal with Deep Uncertainties in a Risk Assessment and
Management Context.” Risk Analysis 33 (12): 2082–91.
Belliveau, J. 2016. “How a Small Hospital Developed Lean Supply Chain Management.”
RevCycle Intelligence. Published September 6. https://revcycleintelligence.com
/news/how-a-small-hospital-developed-lean-supply-chain-management.
Fishman, S. 2015. “3 Mistakes to Avoid When Forecasting the Market for Your
Medical Device.” Med Device Online. Published September 21. www.med
deviceonline.com/doc/mistakes-to-avoid-when-forecasting-the-market-for
-your-medical-device-0001.
Green, C. 2015. “Hospitals Turn to Just-in-Time Buying to Control Supply Chain
Costs.” Healthcare Finance. Published May 6. www.healthcarefinancenews
.com/news/hospitals-turn-just-time-buying-control-supply-chain-costs.
Microsoft. 2018. “Use the Analysis ToolPak to Perform Complex Data Analysis.”
Accessed September 18. https://support.office.com/en-us/article/use-the
-analysis-toolpak-to-perform-complex-data-analysis-6c67ccf0-f4a9-487c-8dec
-bdb5a2cefab6.
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CHAPTER
FINANCIAL FORECASTING
14
Learning Objectives
Copyright 2020. AUPHA/HAP Book.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
After studying this chapter, readers should be able to
• describe in general terms the overall planning process for
businesses,
• use the constant growth method to forecast a business’s financial
statements, and
• discuss the various methods used to forecast income statement
items and balance sheet accounts.
Introduction
In chapter 13, we saw how managers conduct analyses to assess a business’s
financial condition. Now, we consider the planning actions managers can take
to exploit a business’s strengths and overcome its weaknesses as they seek to
meet its goals and objectives. Healthcare managers are vitally concerned with
a business’s projected financial statements and with the effects of alternative
operating policies on these statements. An analysis of such effects is the key
ingredient of financial planning. However, a good financial plan cannot by
itself ensure that a business will meet its goals; the plan must be backed up
by a financial control system, both to make sure that the plan is carried out
properly and to facilitate rapid adjustments if economic and operating conditions change from those built into the plan.
Strategic Planning
Financial plans, which are founded on financial forecasts, are developed in
the framework of the business’s overall strategic plan. Thus, we begin our
discussion with an overview of strategic planning. Note that strategic planning in healthcare organizations is an important and complex managerial
responsibility, and most schools offer entire courses on the subject. Our
549
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Account: s4264928.main.eds
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purpose here is to acquaint you with some basic concepts directly related to
financial forecasting.
Mission Statement
An important part of any strategic plan is the mission statement, which defines
the overall purpose of the organization. The mission can be defined specifically or in general terms. For example, an investor-owned medical equipment
manufacturer might state that its corporate mission is “to increase the intrinsic value of the firm’s common stock.” Another might say that its mission
is “to maximize the growth rate of earnings and dividends per share while
minimizing risk.” Yet another might state that its principal goal is “to provide
state-of-the-art diagnostic systems at the lowest attainable cost in order to
maximize benefits to our customers, employees, and stockholders.”
The mission statements of not-for-profit businesses are normally stated
in different terms, although competition in the health services sector forces
all businesses, regardless of ownership, to operate in a manner consistent with
financial viability. For an example of a not-for-profit mission statement, consider the mission statement of Bayside Memorial Hospital, a not-for-profit
acute care hospital:
Bayside Memorial Hospital, along with its medical staff, is a recognized, innovative healthcare leader dedicated to meeting the needs of the community. We strive
to be the best comprehensive healthcare provider in our service area through our
commitment to excellence.
This mission statement provides Bayside’s managers with an overall framework for development of the hospital’s goals and objectives.1
Corporate Goals
The mission statement contains the general philosophy and approach of the
organization, but it does not provide managers with specific operational
goals. Corporate goals set forth specific achievements for management to
attain. Corporate goals generally are qualitative in nature, such as “keeping the firm’s research and development efforts at the cutting edge of the
industry.” Multiple goals are established and revised over time as conditions
change.
Bayside divides its corporate goals into five major areas:
1. Quality and customer satisfaction
–– To make quality performance the goal of each employee
–– To be recognized by our patients as the provider of choice in our
market area
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C hap ter 14 : F inanc ial Forec asting
–– To identify and resolve areas of patient dissatisfaction as rapidly as
possible
2. Medical staff relations
–– To identify and develop timely channels of communication among
all members of the medical staff, management, and board of
directors
–– To respond in a timely manner to all medical staff concerns brought
to the attention of management
–– To make Bayside a more desirable location to practice medicine
–– To develop strategies to enhance the mutual commitment of the
medical staff, administration, and board of directors for the benefit
of the hospital’s stakeholders
–– To provide the highest-quality, most cost-effective medical care
through a collaborative effort of the medical staff, administration,
and board of directors
3. Human resources management
–– To be recognized as the customer service leader in our market area
–– To develop and manage human resources to make Bayside the most
attractive work location in our market area
4. Financial performance
–– To maintain a financial condition that permits us to be highly
competitive in our market area
–– To develop the systems necessary to identify inpatient and outpatient
costs by unit of service
5. Health systems management
–– To be a leader in applied technology based on patient needs
–– To establish new services and programs in response to patient needs
–– To be at the forefront of electronic health record technology
Of course, these goals occasionally conflict. When they do, Bayside’s senior
managers have to make judgments regarding which takes precedence.
Corporate Objectives
Once a business has defined its mission and goals, it must develop specific
objectives designed to help it achieve its stated goals. Corporate objectives are
generally quantitative in nature. For example, they may specify a target market share, a target return on equity, a target earnings per share growth rate, or
a target economic value added. Furthermore, the extent to which corporate
objectives are met is commonly used as a basis for managers’ compensation.
To illustrate corporate objectives, consider Bayside’s financial performance
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goal of maintaining a financial condition that permits the hospital to be
highly competitive in its market area. These objectives are tied to that goal
in 2019:
• To exceed the hospital’s current 5.8 percent operating margin by 2022
• To exceed the hospital’s current 7.5 percent total margin by 2022
• To increase the hospital’s debt ratio to the range of 35 percent to 40
percent by 2024
• To maintain the hospital’s liquidity as measured by the current ratio in
the range of 2.0 to 2.5
• To increase fixed asset utilization as measured by the fixed-assetturnover ratio to 1.5 by 2024
Corporate objectives give managers precise targets to shoot for. These objectives must support the organization’s mission and goals and must be chosen
carefully so that they are challenging yet attainable.
SELF-TEST
QUESTIONS
1. Briefly describe the nature and use of the following corporate
planning tools:
a. Mission
b. Goals
c. Objectives
2. Why do financial planners need to be familiar with the business’s
strategic plan?
Operational Planning
Whereas strategic planning provides general guidance along with specific
goals and objectives, operational planning provides a road map for executing
a business’s strategic plan. The key document in operational planning is the
business’s operating plan, which contains the detailed guidance necessary to
meet corporate objectives. Operating plans can be developed for any time
horizon. Most firms use a five-year horizon, so the term five-year plan has
become common. In a five-year plan, the plans are most detailed for the first
year, and each succeeding year’s plan becomes less specific.
To get a better feel for operational planning, consider exhibit 14.1,
which contains Bayside’s annual planning schedule. This schedule shows that,
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C hap ter 14 : F inanc ial Forec asting
553
for Bayside and most other organizations, the planning process is essentially
continuous. Next, exhibit 14.2 outlines the key elements of the hospital’s
five-year plan, including an expanded section for finance. A full outline would
require several pages, but the outline given provides some insight into the
format and content of a five-year plan.
For Bayside, much of the planning function takes place at the department level, with technical assistance from the marketing, planning, and
financial staffs. Larger businesses with divisions begin the planning process
at the divisional level. Each division has its own mission and goals as well
as objectives designed to support its goals, and these plans are consolidated
to form the corporate plan. A common practice in many health systems is
using these plans as a way to measure managers’ success during the year;
these plans typically take the form of a balanced scorecard, which is a tool
that aligns high-level strategy with day-to-day operations measured by key
performance indicators.
Months
Action
April–May Marketing department analyzes national and local
economic factors likely to influence Bayside’s patient
volume and reimbursement rates. At this time, a
preliminary volume forecast is prepared for each service
line.
EXHIBIT 14.1
Bayside
Memorial
Hospital:
Annual Planning
Schedule
June –July Operating departments prepare new project (capital
budgeting) requirements as well as operating-cost
estimates on the basis of the preliminary volume
forecast.
August–September Financial analysts evaluate proposed capital expenditures
and department operating plans. Preliminary forecasted
financial statements and cash budgets are prepared with
emphasis on Bayside’s sources and uses of funds and on
forecasted financial condition.
October–November All previous input is reviewed, and the planning, financial,
and departmental staffs draft the hospital’s five-year
plan. Any new information developed during the planning
process “feeds back” into earlier actions.
December The hospital’s executive committee approves the fiveyear plan and submits it to the board of directors for final
approval.
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EXHIBIT 14.2
Bayside
Memorial
Hospital: Partial
Five-Year Plan
Outline
SELF-TEST
QUESTIONS
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Organizational mission and goals
Organizational objectives
Projected business environment
Organizational strategies
Summary of projected business results
Service line plans
Functional area plans
A. Marketing
B. Operations
C. Finance
1. Current financial condition analysis
2. Capital investments and financing
a. Capital budget
b. Financial plan
3. Financial operations
a. Overall policy
b. Cash budget
c. Cash and marketable securities management
d. Inventory management
e. Revenue cycle management
f. Short-term financing
g. Long-term financing
4. Budgeting and control (first year only)
a. Revenue budget
b. Expense budge
c. Operating budget
d. Control procedures
5. Financial forecast
a. Pro forma financial statements
b. Projected financial condition analysis
1. What is the purpose of a business’s operating plan?
2. What is the most common time horizon for operating plans?
3. Briefly describe the contents of a typical operating plan.
Financial Planning
One of the key elements of operational planning is financial planning, which
includes financial forecasting, the focus of this chapter.
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C hap ter 14 : F inanc ial Forec asting
The financial planning process can be broken down into the following
five steps:
1. Create sets of forecasted financial statements to analyze the effects of
alternative operating assumptions on the firm’s financial condition.
These statements can also be used to monitor operations after the plan
has been finalized and put into effect. Rapid awareness of deviations
from plans is essential to a good control system, and such a system is
essential to organizational success in a changing world.
2. Determine the specific financial requirements needed to support each
alternative set of operating assumptions. These financial requirements
must include funds for new facilities and renovations as well as for
inventory and receivables buildups, for research and educational
programs, and for major marketing campaigns.
3. Forecast the financing sources to be used over the next five years to
support each alternative set of operating assumptions. This forecast
involves estimating the funds that will be generated internally
(primarily retentions) as well as those that must be obtained from
external sources (primarily contributions and debt financing). Any
constraints on operating plans imposed by financial limitations should
be incorporated into the plans; examples include expected market
conditions or restrictions in debt covenants that limit the availability of
new debt financing.
4. Assess the projected financial implications of each alternative set
of operating assumptions, including feasibility. To accomplish this,
financial condition analysis (as described in chapter 13) is applied—but
now to forecasted data as opposed to historical data.
5. Choose the operating alternative that will best meet the organization’s
goals and objectives. The assumptions inherent in this alternative
provide the basis for the firm’s base case financial plan, which
constitutes chapter 7.c of Bayside’s operating (five-year) plan (see
exhibit 14.2). The most critical part of the financial plan is based on
forecasted financial statements, but the plan also contains guidance
relative to accounting procedures and other financial functions.
Although our focus in this section is on financial planning, note that it
is equally (or more) important to monitor the financial status of the business
over time to make sure that the plan chosen is working out as expected. Of
course, procedures must be in place for adjusting the base case plan if the
forecasted economic conditions do not materialize. For example, if Bayside’s
forecast on Medicare and Medicaid reimbursement used to develop the base
case five-year plan proves to be too high or too low, the correct amounts
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must be recognized and reflected in operational and financial plans as rapidly
as possible.
SELF-TEST
QUESTION
1. What are the five steps of the financial planning process?
Revenue Forecasts
On the web at:
ache.org/HAP/
PinkSong8e
The starting point, and most critical element, in the financial forecast is the
revenue forecast. The reason revenue forecasts play such an important role is
that all other elements of the financial forecast stem from the revenue forecast. If the revenue projection is erroneous, the rest of the financial forecast
will be suspect.
Revenue forecasts can be done in two ways: from the top or from
the bottom. When businesses forecast from the top, they examine historical
trends in aggregate (organizational) revenues and use them as the basis for
forecasting future revenues. When businesses forecast from the bottom, they
forecast revenues for individual services and then aggregate them to create
the organizational forecast. Most large organizations use both methods and
resolve inconsistencies as the last step in the process. In this way, the best
possible forecasts are made.
Forecasting from the Top
When businesses forecast from the top, the revenue forecast generally starts
with a review of organizational revenues over the past five to ten years, often
expressed in graph form such as that in exhibit 14.3. The first part of the
graph shows actual total operating revenues for Bayside from 2014 through
2018. Over these five years (four growth periods), total operating revenues
(net patient service revenue plus premium revenue plus other revenue) grew
from $86,477,000 to $112,050,000, or at a compound annual growth rate
of 6.7 percent. Alternatively, a time-series regression can be applied to total
operating revenue. We used a spreadsheet to perform a log-linear regression
on all five years of operating revenue data, for a resulting annual growth rate
of 6.9 percent.2 However, Bayside’s revenue growth rate accelerated in the
second half of the historical period, primarily as a result of new capacity added
in 2016. Furthermore, a new, aggressive marketing program was instituted
in late 2017 that resulted in a growth rate in operating revenues in 2018 of
more than 11 percent.
On the basis of the recent trends in operating revenues, anticipated
service introductions, and forecasts of local competition and reimbursement
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C hap ter 14 : F inanc ial Forec asting
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EXHIBIT 14.3
Bayside
Memorial
Hospital:
Historical
and Projected
Revenues (in
thousands of
dollars)
2014
2015
2016
2017
2018
2019
(Projected)
Year
Total Operating Revenue
2014
2015
2016
2017
2018
2019
$ 86,477
90,568
95,351
102,015
112,050
(projected) 124,376
trends, Bayside’s planning group projects a growth rate of 11 percent for
2019, which produces a total operating revenue forecast of $124,376,000.
It is very important to recognize that the operating revenue forecast
is driven by two elements: changes in volume (utilization) and changes in
reimbursement rates. Whereas volume changes tend to have a large impact
on facilities and staffing requirements and hence costs, reimbursement rate
changes, unless they are either substantial or come as a result of a changing
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payer mix, do not have much of an effect on operating variables such as
facilities and labor requirements. Thus, it is important for managers to
recognize whether operating revenue changes are a result of changes in
volume, which indicates that the business is experiencing real changes in
patient services, or a result of reimbursement effects, which may have little
or no impact on operations.
If Bayside’s volume forecast is off the mark, the consequences can be
serious. First, if the market for a particular service expands more than Bayside has expected and planned for, the hospital will not be able to meet its
patients’ needs. Potential customers will end up going elsewhere for services,
and Bayside will lose market share and perhaps miss a major opportunity. On
the other hand, if its projections are overly optimistic, Bayside could end up
with too much capacity, which means excess facilities, equipment, inventory,
and staff. This excess would mean low turnover ratios, high costs of labor
and depreciation, and possibly layoffs. All of these factors would result in
low profitability, which could degrade the hospital’s ability to compete in the
future. If Bayside had financed the unneeded expansion primarily with debt,
its problems would, of course, be compounded. Thus, an accurate volume
forecast is critical to the well-being of any healthcare provider.
Finally, note that the operating revenue forecast, like virtually any
forecast, is actually the expected value of a probability distribution of possible revenues. Because any forecast is subject to a greater or lesser degree of
uncertainty, for financial forecasting purposes we are often just as interested
in the degree of uncertainty inherent in the forecast (e.g., its standard deviation) as we are in the expected value.
Forecasting from the Bottom
To begin forecasting operating revenue from the bottom, Bayside divides
its services into four major groups: (1) inpatient, (2) outpatient, (3) ancillary, and (4) other. Each of these categories is broken down into individual
services; for example, neurosurgery is one of the services that is part of the
overall inpatient services revenue forecast.
Next, the level of population growth and disease trends are forecasted;
for example, analysts predict the population growth in the hospital’s service
area and any disease patterns that will affect the number of neurosurgeries
performed. For an illustration, consider the data obtained from a state health
agency, which show that 523 neurosurgeries were performed in Bayside’s
service area in 2018. With a service area population of 756,508 in 2018, the
neurosurgery rate in the service area was 69.1 per 100,000 people. With a
population forecast of 788,700 for 2019, Bayside’s managers predict that
(788,700 ÷ 100,000) × 69.1 = 545 neurosurgeries will be performed in its
service area.
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C hap ter 14 : F inanc ial Forec asting
Bayside’s managers then look at the competitive environment. They
consider such factors as the hospital’s inpatient and outpatient capacities, its
competitors’ capacities, and new services or service improvements that Bayside or its competitors might institute. For example, Bayside performed 127
neurosurgeries in 2018, so it had 24.3 percent of the neurosurgery market
in that year. With an additional neurosurgeon now on the staff, increased
marketing, and new managed care contracts, the hospital expects to increase
its market share to 30 percent. Thus, Bayside’s forecast for neurosurgeries in
2019 is 0.30 × 545 = 164.
Bayside’s managers then consider the impact of the hospital’s pricing
strategy and reimbursement trends on the demand for services. For example,
does the hospital have plans to raise neurosurgery charges to boost profit
margins or to lower charges to gain market share and use excess capacity? Any
potential impact of such pricing changes on neurosurgery volume must be
worked into the forecasts. Because Bayside has reimbursement and utilization
data on its neurosurgeries, it can easily convert the estimate of the number
of procedures into a revenue estimate. The end result is a utilization and
revenue forecast for neurosurgeries.
Bayside creates a volume and revenue forecast for each individual service and then aggregates these forecasts by service group. Independently, the
hospital forecasts operating revenues by service group using the procedures
discussed in the previous section. The aggregate forecast based on individual
service forecasts is then compared with the service group forecasts.
Differences are reconciled, and the resultant revenue forecast for the
hospital is then compared with the from-the-top forecast described earlier.
Further refinement is often necessary, but the end result is a total operating revenue forecast for the hospital, broken down by major groups and by
individual services.
1. What are two approaches to the total operating revenue forecast?
2. Discuss some factors that must be considered when developing an
operating revenue forecast.
3. Why is it necessary for planners to distinguish between volume
changes and reimbursement changes?
SELF-TEST
QUESTIONS
Creating Forecasted Financial Statements
The revenue forecast provides a starting point from which to create a business’s projected financial statements, which sometimes are called pro forma
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financial statements, or just pro formas. Many techniques are used to create
the pro formas, most of which are too complex or too detailed to discuss
here. Thus, we focus more on concepts than on providing a cookbook
approach to financial statement forecasting. We begin by discussing a conceptual framework for financial statement forecasting. Then, we consider some
issues inherent in the forecasting process.
SELF-TEST
QUESTION
1. What is the starting point from which forecasted financial
statements are created?
Constant Growth Forecasting
On the web at:
ache.org/HAP/
PinkSong8e
The constant growth method—also called the percentage of revenues method or,
more commonly, percentage of sales method—is a simple technique for creating
pro forma financial statements. Although this method has limited value in practice, it provides an excellent introduction to the forecasting process and lays the
groundwork for understanding the more complex methods used in practice.
Assumptions
The constant growth method is based on two assumptions: (1) most income
statement items and balance sheet accounts are tied directly to revenues,
and (2) the current levels of most income statement items and balance sheet
accounts are optimal for the current volume of services provided. The basic
premise is that as revenues increase or decrease, so will most income statement items and balance sheet accounts. Furthermore, the changes in items
and accounts will be proportional to the change in revenues. Given this
premise, we assume that most income statement items and balance sheet
accounts will grow at the same rate—the rate of revenue growth.
Of course, revenue changes can be a result of either volume changes
or reimbursement rate changes, which typically are driven by inflation. In
most situations, revenue changes are a result of both factors. For example,
Bayside’s 11 percent increase in total operating revenues projected for 2019
might be a result of a projected 6 percent increase in the volume of services provided and a 5 percent inflationary increase in reimbursement rates.
Because many of the income statement items and balance sheet accounts are
affected by volume and inflation changes, many financial statement variables
would be expected to also increase by 11 percent. Variables that are tied to
only volume or inflation would be expected to increase at a lower 6 percent
or 5 percent rate. However, the constant growth method illustration that
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C hap ter 14 : F inanc ial Forec asting
follows assumes that all financial statement variables related to revenues are
influenced by both volume and inflationary changes.
Illustration
We illustrate the constant growth method with Bayside, whose 2018 financial
statements are given in column 1 of exhibits 14.4 and 14.5. We explain the
other columns of these tables when we discuss the forecast for 2019.
To begin the process, we assume (contrary to fact) that Bayside operated its fixed assets (property and equipment) at full capacity to support the
$112,050,000 in total operating revenue in 2018—that is, the hospital had
no excess beds or outpatient facilities.3 Because we are assuming no excess
capacity, if volume is to increase in 2019, Bayside will need to increase its
fixed assets along with its current assets.
If, as projected, Bayside’s total operating revenue increases to
$124,376,000, what will its pro forma 2019 income statement and balance
sheet look like, and how much external financing will the hospital require
to support operations in 2019? The first step in using the constant growth
method to forecast the business’s financial statements is to identify income
statement items and balance sheet accounts that are assumed to vary directly
with revenues. For illustrative purposes, the increased operating revenue
forecast for 2019 is expected to bring corresponding increases in all of the
income statement items except interest expense—that is, operating costs and
administrative expenses are assumed to be tied directly to total operating revenue, but interest expense is a function of financing decisions. Furthermore,
nonoperating income is also assumed to grow at the same rate.
Under such naive assumptions, the 2019 first-pass forecasted (or pro
forma) income statement is constructed as follows:
• Place the forecasted constant growth rate—11.0 percent—in column 2
of exhibit 14.4 for all items expected to increase with revenues. Items
calculated in the forecasted income statement (such as total operating
costs) and items not expected to increase proportionally with revenues
(such as interest expense) have “NA” (not applicable) in column 2.
• Forecast the 2019 first-pass pro forma amounts by multiplying each
applicable 2018 value by the growth rate. For example, the 2019
forecast for nursing services expenses is $58,285,000 × 1.11 =
$64,696,000. Note that we generated the forecast with a spreadsheet
model, so some of the amounts shown in the financial statements may
be slightly different from those obtained by using a calculator. Also,
note that the format of the income statement was modified slightly
from that used in chapter 13 to place all of the revenue (income) items
at the top of the statement.
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11.0
11.0
11.0
11.0
11.0
11.0
11.0
NA
NA
$ 58,285
5,424
13,198
11,427
10,250
1,320
4,130
1,542
$105,576
$ 8,572
Net income
NA
11.0%
11.0
NA
$112,050
2,098
$ 114,148
Growth
Rate (%)
(2)
Total operating revenue
Nonoperating income
Total revenues
Expenses:
Nursing services
Dietary services
General services
Administrative services
Employee health and welfare
Malpractice insurance
Depreciation
Interest expense
Total expenses
2018
(1)
$ 9,685
$ 64,696
6,021
14,650
12,684
11,378
1,465
4,584
1,542
$117,020
$124,376
2,329
$126,704
First
Pass
(3)
EXHIBIT 14.4
Bayside Memorial Hospital: Historical and Projected Income Statements (in thousands of dollars)
$ 9,407
$ 64,696
6,021
14,650
12,684
11,378
1,465
4,584
1,820
$117,297
$124,376
2,329
$126,704
Second
Pass
(4)
2019 Projections
$ 9,385
$ 64,696
6,021
14,650
12,684
11,378
1,465
4,584
1,842
$117,320
$124,376
2,329
$126,704
Third
Pass
(5)
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$ 2,263
4,000
21,840
3,177
$ 31,280
$145,158
25,160
$ 119,998
$ 151,278
$ 4,707
5,650
2,975
2,150
$ 13,332
$ 28,750
1,832
$ 30,582
$107,364
$ 151,278
Cash
Short-term investments
Accounts receivable
Inventories
Total current assets
Gross property and equipment
Accumulated depreciation
Net property and equipment
Total assets
Accounts payable
Accrued expenses
Notes payable
Current portion of long-term debt
Total current liabilities
Long-term debt
Capital lease obligations
Total long-term liabilities
Net assets (equity)
Total liabilities and net assets
2018
(1)
11.0
11.0
11.0
11.0
NA
NA
11.0
NA
NA
NA
NA
11.0
11.0
11.0
11.0
NA
11.0
NA
NA
Growth
Rate (%)
(2)
$ 5,225
6,272
3,302
2,387
$ 14,799
$ 28,750
2,034
$ 30,784
$117,049
$162,631
$166,102
$ 2,512
4,440
24,242
3,526
$ 34,721
$ 161,125
29,744
$ 131,381
First
Pass
(3)
EXHIBIT 14.5
Bayside Memorial Hospital: Historical and Projected Balance Sheets (in thousands of dollars)
$ 5,225
6,272
3,302
2,387
$ 14,799
$ 32,221
2,034
$ 34,255
$ 116,771
$165,824
$ 166,102
$ 2,512
4,440
24,242
3,526
$ 34,721
$ 161,125
29,744
$ 131,381
Second
Pass
(4)
2019 Projections
$ 5,225
6,272
3,302
2,387
$ 14,799
$ 32,499
2,034
$ 34,533
$ 116,749
$166,080
$166,102
$ 2,512
4,440
24,242
3,526
$ 34,721
$ 161,125
29,744
$ 131,381
Third
Pass
(5)
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• Some items marked NA, such as interest expense, are carried over into
2019 at their 2018 values. We know that the interest expense in 2019
will be larger than in 2018 if Bayside has to borrow additional funds,
but we cannot predict the amount of interest increase until the firstpass financial statements have been completed. The remaining income
statement items marked NA, such as total expenses, are calculated by
adding or subtracting other forecasted items.
• When the first-pass income statement is completed (column 3 in
exhibit 14.4), we see that the projected net income is $9,685,000.
Note that an 11 percent increase in net income would be $8,572,000
× 1.11 = $9,515,000. The forecasted amount is somewhat greater than
an 11 percent increase because interest expense was held at its 2018
level.
Let’s turn to the balance sheet. Because we assumed that Bayside was
operating at full capacity in 2018, fixed assets as well as current assets must
increase if revenues are to increase. More cash will be needed for transactions,
receivables will be higher, additional inventory must be stocked, new facilities
must be added, and so on.4
To construct the first-pass pro forma balance sheet contained in column 3 in exhibit 14.5, we proceed as follows:
• All balance sheet accounts that are expected to increase with revenues
are forecasted in the same way as in the income statement. For
example, consider the cash account. The 2019 forecast is created by
multiplying the 2018 value by the growth rate, so $2,263,000 × 1.11
= $2,512,000, which is shown in column 3 of exhibit 14.5.
• The forecasted 2019 depreciation expense from the income statement
is added to the 2018 accumulated depreciation account on the
balance sheet to obtain the 2019 accumulated depreciation forecast:
$4,584,000 + $25,160,000 = $29,744,000.
• The long-term debt value initially is held at its 2018 value—
$28,750,000. However, as explained in the next section, we assume
that any external financing required in 2019 will be obtained by issuing
more long-term debt. Alternatively, any excess funds generated would
be used to retire long-term debt. In effect, long-term debt is the
“plug” variable in this illustration. It will be adjusted in the second and
third passes to make the balance sheet balance.
• To forecast the equity amount, add the net income projected for 2019,
all of which must be retained in the business, to the 2018 balance sheet
equity amount: $9,685,000 + $107,364,000 = $117,049,000.
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C hap ter 14 : F inanc ial Forec asting
Finally, fill in the missing values in column 3 by adding or subtracting as
necessary.
The projected 2019 asset accounts sum to $166,102,000. This sum
is less than an 11 percent increase because accumulated depreciation, which
is a contra (negative) asset account, increased by about 18 percent. Thus,
to support a revenue increase of 11 percent, Bayside must increase its assets
from $151,278,000 to $166,102,000. The projected liability and equity
accounts sum to $162,631,000. Again, this sum is less than an 11 percent
increase because (1) long-term debt was held at its 2018 level and (2) the
equity account increased by less than 11 percent.
At this point, the balance sheet does not balance: Assets total
$166,102,000, while only $162,631,000 of liabilities and equity is projected.
Thus, we have a shortfall, or external financing requirement, of $3,471,000.
This amount will have to be raised externally by bank borrowings and selling securities. The organization could also change operating variables—such
as charges (revenues) or expenses—to generate more net income and hence
more retained earnings.
The External Financing Plan
Assuming no change in operating variables, Bayside can use short-term
notes payable, long-term debt, increased solicitations (contributions), or a
combination of these sources to make up the $3,471,000 shortfall. Ordinarily, Bayside would base this choice on its target capital structure, the relative costs of different types of securities, maturity matching considerations,
its ability to increase contributions above the forecasted level, and so on.
The decision as to how this shortfall will be financed is called the external
financing plan.
Our simplistic forecast assumes that Bayside will raise the required
external funds by issuing additional long-term debt. Because Bayside is
financing permanent assets, its use of long-term debt to meet external
financing needs indicates that it is taking the matching approach to its debt
maturity structure (see chapter 10). However, the use of additional debt
capital will change the first approximation income statement for 2019 as
set forth in column 3 of exhibit 14.4 because more debt will lead to higher
interest expense. Bayside’s managers are forecasting that new long-term
debt will carry an interest rate of 8 percent. Thus, $3,471,000 of new longterm debt will increase the interest expense projected for 2019 by 0.08 ×
$3,471,000 = $278,000.
The projected income statement and balance sheet, including financing feedback effects, are shown in column 4 (second pass) of exhibits 14.4
and 14.5. We see that although $3,471,000 was added to Bayside’s liabilities,
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the hospital is still $166,102,000 − $165,824,000 = $278,000 short in meeting its external financing requirement. This new, but much smaller, shortfall
is a result of the added interest expense; $278,000 of new interest expense
decreases net income by a like amount. Hence, the equity balance falls to
$117,049,000 − $278,000 = $116,771,000.
The process can be repeated yet again by adding an additional
$278,000 of external (long-term debt) financing to create a third-pass
income statement and balance sheet. As shown in column 5 (third pass)
of exhibits 14.4 and 14.5, the projected equity balance would be further
reduced by additional interest requirements, but the balance sheet would
be closer to being in balance because more long-term debt would be
added to the liabilities side. Successive iterations would continue to reduce
the discrepancy. If the budget process were computerized, an exact solution could be quickly reached. Even if the process is stopped after just a
few iterations, the projected statements would generally be very close to
being in balance. They would certainly be close enough for practical purposes, given the large element of uncertainty inherent in the projections
themselves.
The base case pro forma financial statements, along with the corresponding financial condition analysis discussed in chapter 13, are then
reviewed by Bayside’s executive committee for consistency with the hospital’s financial objectives. Generally, the committee will make changes to the
initial assumptions that will result in a new set of pro forma financial statements, which are then analyzed and reviewed until the forecast is finalized.
The forecasting process undertaken by Ann Arbor Health Care, a
for-profit hospital, is similar to that performed by Bayside. The only real
difference is that a for-profit business uses stock rather than fund financing.
This fact presents three complications. First, Ann Arbor may pay dividends,
so net income must be reduced by the forecasted dividend payment to find
the amount of capital that is retained in the firm and, hence, flows to the
balance sheet. Second, Ann Arbor has the option of issuing common stock to
meet its external financing needs. Third, the financing feedback effect must
be expanded to include the additional dividend payments, if required, on any
new common stock issued.
Finally, note that forecasted financial statements must be checked for
internal consistency; that is, accumulated depreciation on the balance sheet
must be consistent with the depreciation expense shown on the income
statement, and the equity reported on the balance sheet must be consistent
with the retentions shown on the income statement. It is imperative that pro
forma statements recognize the dependencies between the income statement
items and balance sheet accounts.
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C hap ter 14 : F inanc ial Forec asting
1. Briefly describe the mechanics of the constant growth forecasting
method.
2. Why is the external financing plan so important in the planning
process?
3. Do you think most healthcare businesses use the constant growth
method to develop pro forma financial statements, or do you think
they use some other methodology?
567
SELF-TEST
QUESTIONS
Factors That Influence the External Financing
Requirement
The external financing requirement is one of the key pieces of information
gleaned from the forecasted financial statements. If the business is unable to
fund this requirement, it must alter its plans for the future. The six factors
that have the greatest influence on the external financing requirement are
(1) projected revenue growth rate, (2)
capacity utilization, (3) capital intensity,
(4) profitability, (5) dividend policy (for
Sustainable Growth Rate
investor-owned businesses), and (6) ability
to attract contribution capital (for not-forThe maximum growth rate that a business can
sustain without requiring external financing is
profit firms). In this section, we discuss
called the sustainable growth rate. In Bayside’s
each of these factors in detail.
case, this rate is 8.6 percent, which we estimated
The faster Bayside’s revenues are
using a spreadsheet forecasting model by findforecasted to grow, the greater the extering the revenue growth rate that Bayside could
nal financing need. The reasoning here is
achieve with no external financing. At growth
that increases in revenues normally require
rates of 8.6 percent or less, Bayside will need
no external financing; all required funds can be
increases in assets because growing revobtained by spontaneous increases in current
enues typically imply growing volumes or
liability accounts plus retained earnings, and the
inflationary pressures. If revenues are not
hospital will even generate surplus capital. Howprojected to grow, no new assets will be
ever, if Bayside’s projected revenue growth rate is
needed, but any projected asset increases
8.7 percent or greater, it must seek outside financrequire financing of some type. Some of
ing, and the greater the projected growth rate, the
greater its external financing requirement will be.
the required financing will come from
Although there are formulas that can be used to
spontaneously generated liabilities, such as
estimate the sustainable growth rate, it is easier
accruals. Also, assuming a positive profit
(and potentially more accurate) to use the finanmargin (and for investor-owned firms, a
cial forecasting model for this purpose.
payout ratio of less than 100 percent), the
firm will generate some retained earnings.
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If the revenue growth rate is low enough, spontaneously generated
funds plus retained earnings will be sufficient to support the asset growth.
However, if the growth rate exceeds a certain level, external financing will be
needed. If management foresees difficulties in raising this capital—perhaps
because it has no more debt capacity—then the feasibility of the firm’s expansion plans may have to be reconsidered.
Capacity Utilization
In determining Bayside’s external financing requirement for 2019, we
assumed that the hospital’s fixed assets were being fully used. Thus, any significant increase in revenues would require an increase in fixed assets. What
would happen if Bayside had been operating its fixed assets at less than full
capacity? Assume that Bayside’s managers consider 90 percent occupancy to
be full capacity. Because the hospital had 57.9 percent occupancy in 2018,
it was operating at 57.9 ÷ 90 = 64% of capacity. Under this condition, fixed
assets could remain constant until revenues reach the level at which fixed
assets are being fully used. This level is defined as capacity sales and is calculated as follows:
Key Equation 14.1: Capacity Sales
Utilization rate (% of capacity) =
Actual revenue
,
Capacity sales
so
Capacity sales =
Actual revenue
.
Utilization rate
Because Bayside had been operating at 64 percent of capacity, its
capacity sales without any new fixed assets would be $112,050,000 ÷
0.64 = $175,078,125. In reality, Bayside can easily increase its operating
revenue to $124,376,000 with no increase in fixed assets. Thus, its external
financing requirement would decrease by $161,125,000 − $145,158,000 =
$15,967,000 (the projected increase in gross property and equipment), and
when Bayside’s actual utilization rate is considered, its forecast would show
surplus capital in 2019.
Capital Intensity
The amount of assets required per dollar of sales (total assets/sales) is
often called the capital intensity ratio, which is the reciprocal of the
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C hap ter 14 : F inanc ial Forec asting
total-asset-turnover ratio. Capital intensity has a major effect on the
amount of external capital required to support any level of sales growth. If
the capital intensity ratio is low, such as for home health care businesses,
revenues can grow rapidly without using much outside capital. However,
if the firm is capital intensive, such as a hospital, even a small growth in
volume might require a great deal of outside capital if the firm is operating
at full capacity.
Profitability
Profitability is also an important determinant of external financing requirements—the higher the profit margin, the lower the external financing
requirement, other factors held constant. Bayside’s profit (total) margin in
2018 was 7.5 percent. Suppose its profit margin increased to 10 percent
through higher reimbursements and better expense control. This increase
would cause net income—and hence retained earnings—to increase, which
in turn would decrease Bayside’s need for external financing.
Dividend Policy
For investor-owned firms, dividend policy also affects external capital
requirements. If Ann Arbor foresees difficulties in raising external capital
when it forecasts its 2019 financial statements, it might want to consider a
reduction in its dividend payout ratio. However, before making this decision,
management should consider the possible negative effects of a dividend cut
on stock price.
Ability to Attract Contribution Capital
One of the major sources of equity financing for not-for-profit businesses
is contribution capital. Unrestricted contributions are listed as revenues
on the income statement in the year they become available for use in the
organization; hence, they increase forecasted equity and decrease the need
for external financing. Clearly, organizations that are able to raise large
amounts of charitable contributions are able to grow without using as
much external debt financing as organizations that obtain few contributions. For this reason, many organizations operate an affiliated foundation,
whose sole purpose is to raise funds for the organization through events
and funding campaigns. Note that the earnings on some restricted contributions (endowments) typically are also available to help fund a business’s
asset growth.
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SELF-TEST
QUESTION
1. How do the following factors affect the external financing
requirement?
a. Projected revenue growth rate
b. Capacity utilization
c. Capital intensity
d. Profitability
e. Dividend policy (for investor-owned firms)
f. Ability to attract contribution capital (for not-for-profit firms)
Problems with the Constant Growth Method
For the constant growth method to produce accurate forecasts, each item
and account that is assumed to grow with revenues must increase at the same
rate as revenues. Unfortunately, such a situation rarely exists. Here are some
of the problems with the constant growth approach that are encountered in
real-world forecasting.
Price-Driven Revenue Growth
Earlier we emphasized that revenue growth can occur as a result of volume or
pricing (reimbursement) changes. If revenue growth occurs solely as a result
of reimbursement rate changes that were not caused by inflation, there will
be no direct impact on some income statement items (e.g., labor expenses) or
on some balance sheet items (e.g., inventories, payables, fixed asset requirements). Because the constant growth method ties most items and accounts
directly to dollar revenues, it can produce misleading forecasts when noninflationary reimbursement rate changes—rather than volume changes—are
driving the revenue forecast.
Of course, if the reimbursement changes were made because of inflation effects, there will likely be an inflationary impact on costs. However, in
most cases, the effect of inflation will not be neutral—that is, the impact will
differ across items and accounts.
Economies of Scale
There are economies of scale in the use of many kinds of assets, and when
they occur, the asset growth rates are less than volume growth rates. For
example, healthcare businesses typically need to maintain base stocks of different inventory items, even when volume levels are low. As volume expands,
so do inventories. But inventories tend to grow less rapidly than volume
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C hap ter 14 : F inanc ial Forec asting
does, so the use of a constant growth rate would overstate the amount of
inventory required.
Lumpy Assets
In many industries, practical considerations dictate that a business must add
fixed assets in large, discrete units. For example, in the hospital field, it is not
economically feasible to add, say, five beds, so when hospitals expand capacity, they typically do so in relatively large increments. When capacity volume
is reached, even a small increase in volume would require a hospital to significantly increase its fixed assets, so a small projected volume increase can bring
with it a very large increase in fixed asset requirements.
Suboptimal Relationships
All of the asset projections in a forecast should be based on target, or optimal,
relationships between revenues and assets, not on the relationships that actually exist. For example, in 2018 Bayside had $3,177,000 in inventories. Our
constant growth forecast projected inventories of $3,526,000 in 2015. The
projection assumed that the current inventory level was optimal for the actual
revenues realized. However, if the 2018 inventory level was suboptimal—say,
too large—it might be possible to grow revenues by 11 percent with no
increase in inventories. Conversely, if the inventory level was too small in
2018, the actual level of inventories required in 2019 would be greater than
the forecast.
If any of the problems noted here are encountered in practice (and
many of them are), the simple constant growth method should not be used.
Rather, other techniques must be used to forecast asset and liability levels
and the resulting external financing requirement. Some of these methods are
discussed in the following section.
1. Describe several conditions under which the constant growth
method can produce questionable results.
2. Do these conditions often exist in real-world forecasting?
SELF-TEST
QUESTIONS
Real-World Forecasting
We have emphasized that the constant growth method is not used in actual
forecasting situations. The overall approach of first forecasting the firm’s
income statement, then its balance sheet, then its external financing requirement, and so on, is used, but techniques other than constant growth are used
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571
572
G a p en s k i’s U n d e r s ta n d i n g H e a l th c a re F inanc ial Managem ent
to forecast the specific income statement items and balance sheet accounts.
In this section, we discuss four forecasting techniques commonly used in
practice: (1) simple linear regression, (2) curvilinear regression, (3) multiple
regression, and (4) specific item forecasting.
Simple Linear Regression
Simple linear regression often is used to estimate asset requirements. For
example, consider Bayside’s inventories and total operating revenue over the
past five years and the regression plot shown in exhibit 14.6. The estimated
regression equation, as found using a spreadsheet, is as follows (in thousands
of dollars):
Inventories = $1,372 + (0.0160 × Total operating revenue).
The plotted points are close to the regression line. In fact, the correlation coefficient between inventories and sales is +0.99, which indicates that
there is a strong linear relationship between these two variables. Why might
this be the case for Bayside? According to the economic ordering quantity
(EOQ) model (discussed in chapter 15), inventories should increase with
the square root of revenues, which will cause the regression to be nonlinear
(the true regression line would rise at a decreasing rate). However, Bayside
has greatly expanded its number of service lines over the past decade, and
the base stocks associated with these new services have caused inventories to
increase. Also, inflation has had a similar impact on revenues and inventory
levels. These three influences—(1) economies of scale in existing services, (2)
base stocks for new services, and (3) inflationary effects—offset each other,
and the result is the observed linear relationship between inventories and
sales.
We can use the estimated relationship between inventories and revenues to forecast the 2015 inventory level. Because 2015 total operating revenue is projected at $124,376,000, 2015 inventories should be $3,362,000:
Inventories = $1,372 + (0.0160 × $124,376)
= $1,372 + $1,990
= $3,362.
This forecast is $3,526,000 − $3,362,000 = $164,000 less than our
earlier forecast based on the constant growth method. The difference occurs
because the constant growth method assumes that the ratio of inventories
to revenues remains constant—or, in other words, the regression line passes
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C hap ter 14 : F inanc ial Forec asting
573
EXHIBIT 14.6
Bayside
Memorial
Hospital: Linear
Regression on
Inventories (in
thousands of
dollars)
Inventories
($)
Total Operating
Revenue ($)
Year
Total Operating Revenue
Inventories
2014
$86,477
$2,752
2015
90,568
2,838
2016
95,351
2,896
2017
102,015
2,981
2018
112,050
3,177
Inventories = $1,372 + (0.0160 × Total operating revenue).
Note: Table values were calculated on a spreadsheet; rounding differences will occur if a calculator is used.
through the origin. However, as seen in exhibit 14.6, the ratio actually
declines because the inventory regression line does not pass through the
origin.
We can run linear regressions on all the items on the income statement
and all the accounts on the balance sheet that need to be forecasted to determine items and accounts that produce a high correlation (i.e., have a strong
linear relationship) and therefore may be forecasted using this technique.
Then, we can use these relationships in exhibits 14.4 and 14.5 in place of the
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574
G a p en s k i’s U n d e r s ta n d i n g H e a l th c a re F inanc ial Managem ent
constant growth rates to create new pro forma financial statements based on
linear regressions.
Curvilinear Regression
Simple linear regression is based on the assumption that a straight-line relationship exists between a particular variable and revenues, or some other variable. Although linear relationships between financial statement variables and
revenue frequently do exist, these relationships often assume other forms.
For example, if the EOQ relationship had dominated the inventory–revenue
relationship, the correct plot of inventory versus revenue would be a concave
curve rather than the straight line shown in exhibit 14.6. If we forecasted the
inventory level needed to support revenue growth using a linear relationship,
our forecast would be too high.
In their databases, healthcare businesses have historical data in the
aggregate and by division, service line, and so on. They also have or can easily obtain certain types of data for other firms in the sector. These data can
be analyzed using software based on advanced statistical techniques (1) to
determine whether a relationship is curvilinear or linear and (2) to estimate
the curvilinear relationship, should one exist. Once the best…
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