Download Schilling Ch 7

Strategic Management of
Technological Innovation
Melissa Schilling
Chapter 7
CHOOSING INNOVATION PROJECTS
Boeing’s Sonic Cruiser
• Boeing was developing a new midsized jet, the “Sonic
Cruiser,” which would travel 15-20% faster than existing
commercial jets. It was expected to cost $10 billion to
develop.
• However, in 2002 air ticket sales were down, several
airlines faced bankruptcy, and aircraft were put into storage
to reduce capacity.
– Despite this, Boeing forecasted that the worldwide aircraft fleet
would double by 2021.
• Boeing also noted that the company needs to create a new
aircraft every 12 to 15 years or else the people with the
skills and experience will be either leave the company or
retire and the next generation of employees will not have
that knowledge passed on to them.
2
Boeing’s Sonic Cruiser
• The Sonic Cruiser was scrapped but development
of the 787 Dreamliner began and is scheduled to
fly in 2009
– 50 percent of the primary structure, including the
fuselage and wing, will be made of composite materials.
This eliminates 1,500 aluminum sheets and 40,000 50,000 fasteners.
– health-monitoring systems will be incorporated that will
allow the airplane to self-monitor and report maintenance
requirements to ground-based computer systems.
3
The Development Budget
• Most firms face serious constraints in capital and
other resources they can invest in projects.
• Firms thus often use capital rationing: they set a
fixed R&D budget and rank order projects to
support.
– R&D budget is often a percentage of previous year’s
sales.
– Percentage is typically determined through industry
benchmarking, or historical benchmarking of firm’s
performance.
4
The Development Budget
• R&D Intensity (R&D as a percent of sales) varies considerably
across and within industries.
Industry
Software & Internet
Health
R&D as a Percent of Sales
12.7%
11.2
Computing & Electronics
7.6
Technology
4.3
Aerospace & Defense
4.1
Automotive
4.1
Industrials
2.3
Consumer Products
2.1
Telecom
1.9
Chemicals & Energy
1.5
5
The Development Budget
• Top 20 Global R&D Spenders, 2004
– Microsoft’s 21% is higher than the 12.7% of the Software 7
Internet industry
– GM’s 3% is below the auto industry’s 4.1%
Company
R&D
Expenditures
($billions)
R&D as
percent of
sales
Microsoft
$7.8
21%
Pfizer
7.7
15%
Ford
7.4
DaimlerChrysler
Company
R&D
Expenditures
($billions)
R&D as
percent
of sales
GlaxoSmithKline
5.2
14%
Intel
4.8
14%
4%
Volkswagen
4.7
4%
7.0
4%
Sony
4.7
7%
Toyota
7.0
4%
Nokia
4.6
13%
General Motors
6.5
3%
Honda
4.4
5%
Siemens
6.2
7%
Samsung Electronics
4.3
6%
Matsushita Electric
5.7
7%
Novartis
4.2
15%
IBM
5.7
6%
Roche Holding
4.1
17%
Johnson & Johnson
5.2
11%
Merck
4.0
18%
6
Theory In Action
Financing New Technology Ventures
– Large firms can fund innovation internally; new start-ups
must often obtain external financing.
– In first stages of start-up and growth, entrepreneurs may
have to rely on family, friends, and credit cards.
– Start-ups might be able to obtain some funding from
government grants and loans (SBA, DOE, NASA, etc)
– If idea and management are especially promising,
entrepreneur may secure funds from “angel investors”
(typically seed stage and <$1 million) or venture
capitalists (multiple early stages, >$1 million).
• In 2005, angel investors funded approximately 50,000
ventures valued at $23.1 billion
7
Venture vs Traditional Capital
• Traditional
–
–
–
–
–
More fluid
Bears lower return
Invested based on immediate future
Concerned with past performance
Loaning bank is creditor and requires collateral
• Venture capital
–
–
–
–
–
Less fluid
Requires high return rate
Invested based on longer-run future
Concerned with product and market potential
Venture capitalist and partner are co-owners
• Venture capitalist brings credibility to the company and mentoring
Angel Funding
• The angel investor market in the first half of 2007 has shown
signs of a small retreat from the growth of the past several
years, with total investments of $11.9 billion, a decrease of
6% over the first half of 2006, (Center for Venture
Research at the University of New Hampshire
http://wsbe.unh.edu/cvr)
• A total of 24,000 entrepreneurial ventures received angel
funding in the first half of 2007, a 2% decline from the first
half of 2006.
• The number of active investors in the first half of 2007 was
140,000 individuals (8% above Q1Q2 2006) though the total
dollar size of the market and the number of investments
exhibited a slight decline from Q1Q2 2006
• Reflecting this trend is the decrease in the average deal size
by 4% over the first half of 2006 and an increase (10%) in
the number of investors per deal.
Angel Funding Sector Analysis Q1Q2 2007
• Healthcare services/medical devices/equipment and software
remained the sectors of choice, with 22% and 14%,
respectively, of total angel investments in the first half of
2007.
• This was followed closely by biotech at 10%.
• Electronics/computer hardware, IT services, retail and
industrial/energy garnered close to 10% each.
• The remaining investments were approximately equally
weighted across high tech sectors, with each having 3-5% of
the total deals.
Sector
Health
Software
Biotech
Electronics
IT Services
Retail
Industrial/Energy
Deals
22%
14%
10%
8%
7%
6%
6%
Angel Funding Analysis
• Angels continue to be the largest source of seed and start-up capital in the
United States, with 42% of the first half of 2007 angel investments in the
seed and start-up stage.
– This preference for seed and start-up investing is followed closely by
post-seed/start-up investments of 48%.
• While angels are not abandoning seed and start-up investing, it appears
that market conditions, the preferences of large formal angel alliances, and
a possible slight restructuring of the angel market are resulting in angels
engaging in more later-stage investments.
• This restructuring of the angel market has in turn resulted in fewer dollars
available for seed investments, thus exacerbating the capital gap for seed
and start-up capital in the US.
• In the first half of 2007 angels exited their investments primarily through
sale of the business (acquisitions by another firm), with 61% of the first
half 2007 exits through trade sales.
• Exits by initial public offerings represented 6% of exits and bankruptcy
occurred in 33% of the exits.
• For all these exits the average rate of return was 30-40% and roughly half
(52%) were at a profit.
Venture Capital Funding Analysis
• Venture capitalists invested $29.4 billion in 3,813 deals in
2007—marking the highest yearly investment total since 2001.
– The total invested in 2007 represents a 10.8 percent increase in dollars
and a five percent increase in deal volume over 2006.
– Much of the increase in investments over the prior year can be
attributed to record investment levels in the Clean Technology and Life
Sciences sectors as well as strong investment levels in Internet-specific
companies.
• Investments in the fourth quarter of 2007 totaled $7.0 billion
in 963 deals, marking the fourth straight quarter with
investments totaling more than $7 billion—a phenomenon not
seen since 2001.
Source http://www.nvca.org/pdf/07Q4MTRelEmbargoFINAL.pdf
VC Sector and Industry Analysis
• The Life Sciences sector (Biotechnology and Medical Device industries
together) set an all-time record for venture capital investing in 2007 with
$9.1 billion in 862 deals, compared to $7.6 billion going into 786 deals in
2006.
– The most significant growth was seen in the Medical Device industry, which
rose 40% in 2007 to $3.9 billion going into 385 deals. For the year, Life
Sciences accounted for 31% of all venture capital invested, which also
represents an all-time high.
– Life Sciences also retained its position as the number one investment sector
for 2007.
• Software investing remained relatively flat in 2007, consistent with levels
over the last five years with $5.3 billion going into 905 deals, compared
to $5.1 billion going into 920 deals in 2006.
– Despite the lack of growth, it still remained the largest single industry
category for the year both in terms of deals and dollars, edging out
Biotechnology for the top position.
13
VC Sector and Industry Analysis
• The Clean Technology sector (alternative energy, pollution
and recycling, power supplies and conservation) which
represented two of the five biggest deals of the year,
experienced significant growth in 2007 with $2.2 billion
invested in 201 deals.
– This investment level represents a 46% growth in dollars and a 57%
growth in deal volume over 2006 when $1.5 billion was
invested in 128 companies.
• Internet-specific companies received $4.6 billion in 748 deals
in 2007, an increase of 12% and 8%, respectively, over 2006
when these companies received $4.1 billion in 691 deals.
– ‘Internet-specific’ refers to a company whose business model is
fundamentally dependent on the Internet, regardless of the
company’s primary industry category. These companies accounted for
16 percent of all venture capital dollars in 2007, approximately the
14
same percentage as in 2006.
VC Sector and Industry Analysis
• The Media and Entertainment industry saw more venture
capital dollars in 2007, with $1.9 billion going into 340 deals
compared to 2006 when $1.7 billion went into 318 deals.
• Other industries that saw increases in deals and dollars during
the year include Business Products and Services, Financial
Services, IT Services, and Retailing/Distribution.
• Telecom companies saw a decrease in investment in 2007
with 290 deals receiving $2.1 billion dollars, a drop from the
$2.6 billion in 301 deals they captured in 2006.
• Other industries that experienced declines in deals and dollars
in 2007 include Healthcare Services, Semiconductors, and
Electronics/Instrumentation.
15
Total equity investments into
venture-backed companies Q1 2001—Q4 2007
16
Most active venture investors 2007
• The most active venture firms in the US closed 20 or more
deals each in 2007.
• For the third year in a row, Draper Fisher Jurvetson topped
the list of most active venture firms for the full-year,
completing 100 deals in 2007, up from the 82 deals they
participated in during 2006.
• Like last year, New Enterprise Associates and Intel Capital
rounded out the top three firms.
• Making a big move up the list was Canaan Partners; whose
50 deals completed in 2007 was 28 percent higher than the
39 deals completed in 2006. For the year, the top 10 firms
invested in eight percent of all the deals done in 2007.
17
Most active venture investors 2007
http://www.pwcmoneytree.com/MTPublic/ns/moneytree/filesource/exhibits/National_MoneyTree_full_year_Q4_2007_Final.pdf
18
Quantitative Methods for Choosing Projects
• The difference between the present value of cash inflows and the present
value of cash outflows. NPV is used in capital budgeting to analyze the
profitability of an investment or project.
• NPV compares the value of a dollar today to the value of that same dollar in
the future, taking inflation and returns into account. If the NPV of a
prospective project is positive, it should be accepted. However, if NPV is
negative, the project should probably be rejected because cash flows will also
be negative.
• For example, if a retail clothing business wants to purchase an existing store,
it would first estimate the future cash flows that store would generate, and
then discount those cash flows into one lump-sum present value amount, say
$565,000.
– If the owner of the store was willing to sell his business for less than $565,000, the
purchasing company would likely accept the offer as it presents a positive NPV investment.
– Conversely, if the owner would not sell for less than $565,000, the purchaser would not buy
the store, as the investment would present a negative NPV at that time and would,
therefore, reduce the overall value of the clothing company.
19
Quantitative Methods for Choosing Projects
• NPV = Net Present value = Present value of net cash flows
– Each cash inflow/outflow is discounted back to its PV and then
they are summed.
or shortened
t - the time of the cash flow
N - the total time of the project
r - the discount rate (the rate of return that could be earned on an investment in
the financial markets with similar risk.)
Ct - the net cash flow (the amount of cash) at time t
(for educational purposes, C0 is commonly placed to the left of the sum to
emphasize its role as the initial investment.).
20
Quantitative Methods for Choosing Projects
• Commonly used quantitative methods include discounted cash flow
methods and real options.
– Discounted Cash Flow (DCF)
• Net Present Value (NPV): Expected cash inflows are discounted and
compared to outlays.
• In Excel use the formula NPV(interest rate, cell range of cashflows)
$943.39
21
Quantitative Methods for Choosing Projects
• Internal Rate of Return (IRR): The discount rate that
makes the net present value of investment zero.
– It is an indicator of the efficiency of an investment, as
opposed to NPV, which indicates value or magnitude.
– The IRR is the annualized effective compounded return rate
which can be earned on the invested capital, i.e., the yield on
the investment.
– A project is a good investment proposition if its IRR is greater
than the rate of return that could be earned by alternate
investments (investing in other projects, buying bonds, even
putting the money in a bank account).
• Thus, the IRR should be compared to any alternate costs of
capital including an appropriate risk premium.
22
Quantitative Methods for Choosing Projects
– Mathematically the IRR is defined as any discount rate that
results in an NPV of zero of a series of cash flows.
– In general, if the IRR is greater than the project's cost of
capital, or hurdle rate (minimum rate of return that must
be met for a company to undertake a particular project),
the project will add value for the company.
23
Quantitative Methods for Choosing Projects
24
Quantitative Methods for Choosing Projects
• Strengths and Weaknesses of DCF Methods:
– Strengths
• Provide concrete financial estimates
• Explicitly consider timing of investment and time value of money
– Weaknesses
• May be deceptive; only as accurate as original estimates of cash
flows.
• May fail to capture strategic importance of project
– Technology development plays a crucial role in building and
leveraging firm capabilities and creating options for the future
• Intel’s investment in DRAM technology must have been
considered a total loss by NPV methods, however it laid the
foundation for Intel’s ability to develop microprocessors which
proved enormously profitable
• Thus, some managers and scholars have promoted the idea of
treating new product development decisions as real options
25
Quantitative Methods for Choosing Projects
• Real Options: Applies stock option model to
nonfinancial resource investments. e.g., with respect to
R&D:
– The cost of the R&D program can be considered the
price of a call option.
– The cost of future investment required to capitalize on
the R&D program (such as the cost of commercializing
a new technology that is developed) can be considered
the exercise price.
– The returns to the R&D investment are analogous to
the value of a stock purchased with a call option.
26
Quantitative Methods for Choosing Projects
• Real options are based on stock options
– A call option on a stock enables an investor to purchase the
stock at a specified price (the exercise price) in the future
• If, in the future, the stock is worth more than the exercise price,
the holder of the option will typically exercise the option by buying
the stock
– If the stock is worth more than the exercise price plus the price
paid for the original option, the option holder makes a profit
– If it is worth less, the option holder will typically choose not to
exercise the option, allowing it to expire. The amount paid for
the initial option is a loss.
• If the stock is worth more than the exercise price but not more
than the exercise price plus the amount paid for the original option,
the stockholder will typically exercise the option. The amount lost is
less than if the option were to expire.
27
Quantitative Methods for Choosing Projects
– Examples of real call options
28
Value of a call option at expiration
• The value of a call option
is zero as long as the price
of the stock remains less
than the exercise price
• If the value of the stock
rises above the exercise
price, the value of the call
rises with the value of the
stock, dollar for dollar
(thus the 45-degree angle)
29
Quantitative Methods for Choosing Projects
• Options are valuable when there is uncertainty (as in
innovation)
– Some research shows that an option approach results in better
technology investment decisions than a cash flow analysis
approach
• However, real options models have some limitations:
– Many innovation projects do not conform to the same
capital market assumptions underlying option models.
• May not be able to acquire option at small price: may require full
investment before its known whether technology will be successful.
• Value of stock option is independent of call holder’s behavior, but the
future returns of the of R&D investment can be significantly
influenced by the firm’s capabilities, complementary assets, and
strategies.
– Rather than being an observer (as in the option scenario), the
investor can be an active driver of the value of the investment
30
Qualitative Methods of Choosing Projects
• Many factors in the choice of development projects are extremely difficult (or
misleading) to quantify.
• Almost all firms thus use some qualitative methods.
– Screening Questions may be used to assess different dimensions of the
project decision including:
• Role of customer (market, use, compatibility and ease of use, distribution
and pricing)
• Role of capabilities (existing capabilities, competitors’ capabilities, future
capabilities)
• Project timing and cost (time to complete, first to market, readiness of
market, project cost, other costs)
– Can create a scoring mechanism that can weight the questions according
to importance
– Even if Boeing’s Sonic Cruiser project would not be profitable based on
quantitative analysis, it may be necessary just to pass on the skills and
experience of building an airplane to the next generation of employees.
That value is difficult to asses quantitatively but is revealed by qualitative
analysis
31
Qualitative Methods of Choosing Projects
– The Aggregate Project Planning Framework
• Managers map their R&D projects according to levels of risk, resource commitment
and timing of cash flows
32
Qualitative Methods of Choosing Projects
• Advanced R&D Projects: develop cutting-edge technologies; often no
immediate commercial application.
• Breakthrough Projects: incorporate revolutionary new technologies into
a commercial application.
• Platform Projects: not revolutionary, but offer fundamental
improvements in cost, quality and performance of a technology over
preceding generations of products.
• Derivative Projects: incremental improvements in products and/or
processes to provide a variety in design features.
– Toyota’s Camry platform offers LE, SE and XLE models to appeal to
different market segments
• Derivative projects pay off the quickest, and help service the firm’s
short-term cash flow needs. Advanced R&D projects take a long time to
pay off (or may not pay off at all), but can position the firm to be a
technological leader.
33
Qualitative Methods of Choosing Projects
– Managers then compare actual balance of projects with desired
balance of projects.
• A typical firm experiencing moderate growth might allocate 10%
of it’s R&D budget to breakthrough innovation, 30% to platform
projects and 60% to derivative projects
• A firm pursuing a more significant growth might allocate higher
percentages to breakthrough and platform projects
• A firm that needs to generate more short-term profit might
allocate a higher percentage to derivative projects
34
Qualitative Methods of Choosing Projects
– Mapping the company’s R&D portfolio encourages the firm to consider
both short-term cash flow needs and long-term strategic momentum in
its budgeting and planning
• A firm that invests heavily in in derivative products that may be
immediately commercialized with little risk may appear to have good
returns on its R&D investment in the short run, but then be unable to
compete when the market shifts to new newer technology
• A firm that invests heavily in advanced R&D or breakthrough projects may
be on the leading edge of technology but run into cash flow problems from
a lack of revenues generated from recently commercialized platform or
derivative projects
• Jack Welch, former CEO of GE – “You can’t grow long term if you can’t eat
short term. Anyone can manage short. Anyone can manage long.
Balancing those two things is what management is”
– Because the development of a new drug takes 10-15 years at a cost of $800
million and drug companies have become reliant on a few blockbuster drugs
for a significant share of their revenues, drugs firms could experience extreme
volatility in their sales revenue
35
Drug Firms’ Reliance on a Few
Blockbuster Products, 2000
36
Qualitative Methods of Choosing Projects
– Q-Sort is a simple method for ranking ideas on different
dimensions.
• Used for many diverse purposes – from identifying personality
disorders to establishing scales of customer preferences
• Individuals in a group are each given a stack of cards with an object
or idea on each card (e.g., a potential project).
• A series of project selection criteria are presented (technical
feasibility, market impact, fit with strategic intent) and, for each
criterion, the individuals sort their cards in rank order (e.g., best fit
with strategic intent) or in categories (technically feasible vs
infeasible) according to that criterion
• Individuals compare their ran ordering and use these comparions to
structure a debate about the projects
• After several rounds of sorting and debating, the group is expected
to arrive at a consensus about the best projects.
37
Combining Quantitative and
Qualitative Information
• Managers may use multiple methods in combination.
– Use quantitative methods to estimate the cash flows anticipated from
a project when balancing their R&D portfolio on a project map
• May also use methods that convert qualitative information
into quantitative form (though this has similar risks as
discussed with quantitative methods)
– Conjoint Analysis estimates the relative value individuals
place on attributes of a choice which can then be used in
development and pricing decisions.
• Individuals given a card with products (or projects) with different
features and prices.
• Individuals rate each in terms of desirability or rank them.
• Multiple regression then used to assess the degree to which an
attribute influences rating. These weights quantify the trade-offs
involved in providing different features.
38
Conjoint Analysis
• Conjoint analysis is a popular marketing research technique that marketers
use to determine what features a new product should have and how it
should be priced.
• Conjoint analysis became popular because it was a far less expensive and
more flexible way to address these issues than concept (market) testing.
• Suppose we want to market a new golf ball. We know from experience and
from talking with
• golfers that there are three important product features:
– Average Driving Distance
– Average Ball Life
– Price
• There is actually a range of feasible alternatives for each of these features:
Average
Driving
Distance
Average
Ball Life
Price
275 yards
54 holes
$1.25
250 yards
36 holes
$1.50
225 yards
18 holes
$1.75
39
Conjoint Analysis
• Obviously, the market’s “ideal” ball would be:
Average
Driving
Distance
Average
Ball Life
Price
275 yards
54 holes
$1.25
• and the “ideal” ball from a cost of manufacturing perspective would be:
Average
Driving
Distance
Average
Ball Life
Price
225 yards
18 holes
$1.75
– assuming that it costs less to produce a ball that travels a shorter distance and has a
shorter life.
• The basic marketing issue: We’d lose our shirts selling the first ball and the
market wouldn’t buy the second. The most viable product is somewhere in
between, but where? Conjoint analysis lets us find out where.
40
Conjoint Analysis
• A traditional research project might start by considering the
rankings for distance and ball life as follows:
Rank
Avg Driving
Distance
Rank
Avg Ball
Life
1
275 yards
1
54 holes
2
250 yards
2
36 holes
3
225 yards
3
18 holes
• This type of information doesn’t tell us anything that we
didn’t already know about which ball to produce.
41
Conjoint Analysis
• Now consider the same two features taken conjointly.
• The next two figures show the rankings of the 9 possible
products for two buyers assuming price is the same for all
combinations.
42
Conjoint Analysis
• Both buyers agree on the most and least preferred ball. But
as we can see from their other choices, Buyer 1 tends to
trade-off ball life for distance, whereas Buyer 2 makes the
opposite trade-off.
• The knowledge we gain in this analysis is the essence of
conjoint analysis.
43
Conjoint Analysis
• Next, let’s figure out a set of values for driving distance and a second
set for ball life for Buyer 1 so that when we add these values together
for each ball they reproduce Buyer 1's rank orders.
• Here’s one possible scheme.
– Note that we could have picked many other sets of numbers that would
have worked, so there is some arbitrariness in the magnitudes of these
numbers even though their relationships to each other are fixed.
44
Conjoint Analysis
•
Next suppose that the table below represents the trade-offs Buyer 1 is willing to
make between ball life and price.
•
Starting with the values we just derived for ball life, the next table shows a set of
values for price that when added to those of ball life reproduce the rankings for
Buyer 1 in the table above.
45
Conjoint Analysis
• We now have in the table below a complete set of values
(referred to as “utilities” or “part-worths”) that capture Buyer
1's trade-offs.
• We can now use this information to determine which ball to
produce.
46
Conjoint Analysis
• Suppose we were considering one of two golf balls shown in the
table below
• The values for Buyer 1, when added together, give us an estimate
of his preferences.
• Applying these to the two golf balls we’re considering, we get
these results
• We’d expect buyer 1 to prefer the long-life ball over the distance
ball since it has the larger total value
47
Theory In Action: Courtyard by Marriot
• Marriot used conjoint analysis to help it develop a midprice hotel line.
• First used focus groups to identify customer segments and attributes they
cared about in a hotel. These included:
– external surroundings, room, food, lounge, services, leisure activities and
security
• Then created potential hotel profiles that varied on these features and
asked participants to rate the profiles.
– For example, under the services factor was reservations
• Two levels were devised- Call the hotel directly or call an 800 reservation number
– A sample of hotel customers were given 7 cards, each containing one of the
factors above with a dollar value assigned to each level of service within a
factor. A maximum of $35 could be budgeted to creating a profile of features
• If the budget was exceeded, features had to be eliminated or a less expensive level
of services had to be chosen
• The participants set their own priorities and made their own trade-offs. This help
management understand what was important to different customer segments
48
Theory In Action: Courtyard by Marriot
• Participants werethen asked to rate each of the profiles
created
• Regression was then used to assess how different levels
of service within a specific attribute influenced customer
ratings of the hotel overall
• Based on the results, Marriott developed Courtyard
concept: relatively small hotels with limited amenities,
small restaurants and meeting rooms, courtyards, high
security, and rates of $40-$60 a night.
• By the end of 2002, there were 553 Courtyard hotels and
their average occupancy rate of 72% was well above the
industry average
49
Combining Quantitative and
Qualitative Information
• Data Envelopment Analysis (DEA) uses linear
programming to combine measures of projects based on
different units (e.g., rank vs. dollars) into an efficiency
frontier.
– Projects can be ranked by assessing their distance from
efficiency frontier.
– As with other quantitative methods, DEA results only as good as
the data utilized; managers must be careful in their choice of
measures and their accuracy.
– DEA has been applied in many situations such as:
•
•
•
•
health care (hospitals, doctors), education (schools, universities)
banks, manufacturing
benchmarking, management evaluation
fast food restaurants, retail stores
50
Data Envelopment Analysis
• Assume that there are three players (DMUs – decision making units), A,
B, and C, with the following batting statistics. Player A is a good contact
hitter, player C is a long ball hitter and player B is somewhere in
between.
– Player A: 100 at-bats, 40 singles, 0 home runs
– Player B: 100 at-bats, 20 singles, 5 home runs
– Player C: 100 at-bats, 10 singles, 20 home runs
• Analysis
– Player A: No combination of players B and C can produce 40 singles
with the constraint of only 100 at-bats. Therefore player A is
efficient at hitting singles and receives an efficiency of 1.0.
– Player B: Can be constructed as a combination of players A and C.
(For example, a 50-50 combination would yield 25 singles and 10
home runs).
– Player C: No combination of players A and B can produce his total of
20 home runs in only 100 at bats so player C is efficient
51
Data Envelopment Analysis
• Graphically, we can see that Player B is inside the optimal efficiency
frontier created by A and C and is thus operating at a sub-optimal level
– B’s efficiency can be determined by comparing him to a virtual player
formed from player A and player C.
– The virtual player, called V, is approximately 64% of player C and
36% of player A. (measure AV/AC and CV/AC.)
– The efficiency of player B is then calculated by finding the fraction of
inputs that player V would need to produce as many outputs as
player B. The efficiency of player B is OB/OV which is approximately
68%.
52