Decisions involving capital expenditures often require managers to weight the costs and benefits of different options related to the same goal or project. For instance, deciding whether to replace, repair, or do nothing to existing equipment is a capital expenditure decision that involves calculations, projections, and deliberations. Managers must be able to quantitatively analyze different options for capital expenditures to make the best decisions for their organization.
For this Assignment, review the information in the scenario posted in the entry titled Week 3 Assignment located in the Doc Sharing link. You will utilize the information in this week’s Resources and media to make a recommendation in regard to a capital expenditure. There is also an Excel template provided in the same entry that you may find helpful in completing this Assignment.
The Assignment:

Part 1: Prepare a spreadsheet using Excel or a similar program in which you compute the following for each proposed location.

Accounting rate of return on investment
Payback
Net present value
Internal rate of return

Note: Be sure to view the media for this week before starting this Assignment.

Part 2: Utilizing Word or another word processing software program, prepare a written report for the Board of Directors. The intended audience is clear from the salutation and the language used throughout the report.

Include a detailed and thorough explanation of the conclusion you reached regarding the feasibility of each proposal supported by the calculations prepared in Part 1.
Explain at least five non-financial items (e.g., culture, language, etc.), which may impact the perceived desirability of each location.
Select the one location you recommend the Board invest in. Explain your rationale in precise and detailed language.
ABC Home Appliances, Inc.
ABC Home Appliances, Inc. is considering expanding its international presence. It sells 25% of
all the toaster ovens sold in the United States but only 3% of the toaster ovens sold outside of the
United States. The organization believes that it can sell more of its product if it has a production
facility located overseas. Estimates concerning two possible locations, Tianjin, China and
Shenzhen, China follow:
Possible Location
Tianjin, China
Initial cash outlay
Useful life
Net cash inflows excluding depreciation
The cost of capital
Tax rate
$5,500,000
20 years
$1,350,000
9%
40%
Shenzhen,
China
$4,500,000
20 years
$1,400,000
9%
40%
Week 3 Template
Given Information
Initial cash outlay
Useful life
Operating income
The cost of capital
Tax rate
Tianjin net cash flow
Operating income
Depreciation
Net Income before taxes
Tax, 40%
Net Income
Net cash flow
Tianjin, China
$
5,500,000
20
1,350,000
9%
40%
Net Income
$
–
Average net income and average book value of investment
Year
Net Income
1
$
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Average
$
Tianjin
Cash Flow
$
–
Avg BV of Investment
#DIV/0!
a.
Average rate of return on investment
Type the formula here. Put the answer in D49
b.
Payback period
Type the formula here. Put the answer in D53
c.
years
Net present value – using table page 126
Amount
Initial investment
Annual net cash flow for 20 years
Net present value
Using MS Excel:
Initial investment
PV of Annual net cash flow for 20 years
Net present value
d.
Internal rate of return
Factor
Present value
$
–
$
$
–
Using MS Excel:
Period
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Given Information
Shenzhen, China
Initial cash outlay
Useful life
Operating income
The cost of capital
Tax rate
Shenzhen net cash flow
Operating income
Depreciation
Net Income before taxes
Tax, 40%
Net Income
Net cash flow
$
Net Income
$
4,500,000
20
1,400,000
9%
40%
Cash Flow
–
$
–
Average net income and average book value of investment
Year
Net Income Avg BV of Investment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Average
#DIV/0!
Shenzhen
a.
Average rate of return on investment
Type the formula here. Put the answer in I49
b.
Payback period
Type the formula here. Put the answer in I53
c.
years
Net present value – using table page 126
Amount
Initial investment
Annual net cash flow for 20 years
Net present value
Using MS Excel:
Initial investment
PV of Annual net cash flow for 20 years
Net present value
e.
Internal rate of return
Factor
Present value
$
–
$
$
–
Using MS Excel:
Period
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
#NUM!
The Five Traps of
Performance
Measurement
Jude Buffum
| by Andrew Likierman
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I
of Frasier, the television sitcom that
follows the fortunes of a Seattle-based psychoanalyst, the eponymous hero’s brother gloomily summarizes a task ahead: “Difficult and boring – my
favorite combination.” If this is your reaction to the
challenge of improving the measurement of your
organization’s performance, you are not alone. In
my experience, most senior executives ?nd it an
onerous if not threatening task. Thus they leave it
to people who may not be natural judges of performance but are ?uent in the language of spreadsheets. The inevitable result is a mass of numbers
and comparisons that provide little insight into
a company’s performance and may even lead to
decisions that hurt it. That’s a big problem in the
current recession, because the margin for error is
virtually nonexistent.
IN AN EPISODE
So how should executives take ownership of performance assessment? They need to ?nd measures,
qualitative as well as quantitative, that look past
this year’s budget and previous results to determine
how the company will fare against its competitors in
the future. They need to move beyond a few simple,
easy-to-game metrics and embrace an array of more
sophisticated ones. And they need to keep people on
their toes and make sure that today’s measures are
not about yesterday’s business model.
In the following pages I present what I’ve found
to be the ?ve most common traps in measuring performance and illustrate how some organizations
have managed to avoid them. My prescriptions
aren’t exhaustive, but they’ll provide a good start.
In any event, they can help you steal a march on
rivals who are caught in the same old traps.
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The Five Traps of Performance Measurement
trap
Measuring
Against Yourself
The papers for the next
regular performance assessment are on your
desk, their thicket of
numbers awaiting you.
What are those numbers?
Most likely, comparisons
of current results with a
plan or a budget. If that’s
the case, you’re at grave risk of falling into the ?rst
trap of performance measurement: looking only
at your own company. You may be doing better
than the plan, but are you beating the competition? And what if the estimates you’re seeing were
manipulated?
To measure how well you’re doing, you need
information about the benchmarks that matter
most – the ones outside the organization. They will
help you de?ne competitive priorities and connect
executive compensation to relative rather than absolute performance – meaning you’ll reward senior
executives for doing better than everyone else.
The trouble is that comparisons with your competitors can’t easily be made in real time – which is
precisely why so many companies fall back on measurements against the previous year’s plans and
budgets. You have to be creative
about how you ?nd the relevant
data or some proxy for them.
One way is to ask your cusIN BRIEF
tomers. Enterprise, the car-rental
» Most senior executives ?nd
company, uses the Enterprise
performance measurement
Service Quality Index, which
dif?cult if not threatening, and
measures customers’ repeat purthey’re reluctant to engage with
chase intentions. Each branch
it in a meaningful way. As a result,
of the company telephones a
companies routinely fall into
random
sample of customers
?ve traps.
and asks whether they will use
» Speci?cally, they use themEnterprise again. When the index
selves rather than competitors
goes up, the company is gaining
as benchmarks, focus on past
market
share; when it falls, cusindicators of success, overvalue
tomers
are
taking their business
numbers at the expense of qualielsewhere.
The
branches post retative measures, set easy-to-game
metrics, and cling to systems that
sults within two weeks, put them
have outlived their usefulness.
next to pro?tability numbers on
monthly financial statements,
» This article addresses each of
and factor them into criteria for
the traps in turn, offering advice
promotion
(thus aligning sales
about how to avoid them and examples of organizations that have
goals and incentives).
successfully done so.
Of course you have to make
sure you don’t annoy your cus-
IDEA
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tomers as you gather data. Think about how restaurant managers seek feedback about the quality
of their service: Most often they interrupt diners’
conversations to ask if everything is OK; sometimes
they deliver a questionnaire with the bill. Either approach can be irritating. Danny Meyer, the founder
of New York’s Union Square Hospitality Group, gets
the information unobtrusively, through simple observation. If people dining together in one of his
restaurants are looking at one another, the service
is probably working. If they’re all looking around
the room, they may be wowed by the architecture,
but it’s far more likely that the service is slow.
Another way to get data is to go to professionals
outside your company. When Marc Effron, the vice
president of talent management for Avon Products,
was trying to determine whether his company was
doing a good job of ?nding and developing managers, he came up with the idea of creating a network of talent management professionals. Started
in 2007, the New Talent Management Network has
more than 1,200 members, for whom it conducts
original research and provides a library of resources
and best practices.
trap
Looking Backward
Along with budget figures, your performance
assessment package almost certainly includes
comparisons between
this year and last. If so,
watch out for the second
trap, which is to focus
on the past. Beating last
year’s numbers is not the
point; a performance measurement system needs
to tell you whether the decisions you’re making
now are going to help you in the coming months.
Look for measures that lead rather than lag the
pro?ts in your business. The U.S. health insurer Humana, recognizing that its most expensive patients
are the really sick ones (a few years back the company found that the sickest 10% accounted for 80%
of its costs), offers customers incentives for early
screening. If it can get more customers into early or
even preemptive treatment than other companies
can, it will outperform rivals in the future.
The quality of managerial decision making is
another leading indicator of success. Boards must
assess top executives’ wisdom and willingness to
listen. Qualitative, subjective judgments based on
independent directors’ own experience with an ex-
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ecutive are usually more revealing than a formal
analysis of the executive’s track record (an unreliable predictor of success, especially for a CEO) or
his or her division’s ?nancial performance. (See
“Evaluating the CEO,” by Stephen P. Kaufman, HBR
October 2008.)
It may sound trite, but how the company presents itself in official communications often signals
the management style of top executives. In August
2006 the Economist reported that Arijit Chatterjee
and Donald Hambrick, of Pennsylvania State University, had devised a narcissism index on which to
rate 105 company bosses, based on the
prominence of the CEO’s photo in the
annual report, his or her prominence in
press releases, the frequency of the ?rst
person singular in interviews with the
CEO, and his or her compensation relative to that of the ?rm’s second-highestpaid executive.
Finally, you need to look not only at
what you and others are doing but also
at what you aren’t doing. The managers
of one European investment bank told
me that they measure performance by
the outcomes of deals they’ve turned
down as well as by the outcomes of deals they’ve
won. If the ones they’ve rejected turn out to be lemons, those rejections count as successes. This kind of
analysis seems obvious once stated, but I’ve noticed
a persistent bias in all of us to focus on what we do
over what we don’t do. Good management is about
making choices, so a decision not to do something
should be analyzed as closely as a decision to do
something.
ers to include personal data, and in many cases the
employees who provided the service watch them
?ll out the forms. How surprised should you be
if your employees hand in consistently favorable
forms that they themselves collected? Bad assessments have a tendency to mysteriously disappear.
Numbers-driven companies also gravitate toward the most popular measures. If they’re looking
to compare themselves with other companies, they
feel they should use whatever measures others use.
The question of what measure is the right one gets
lost. Take Frederick Reichheld’s widely used Net
At one investment bank, if the deals that
managers have rejected turn out to be
lemons, those rejections count as successes.
trap
Putting Your Faith
in Numbers
Good or bad, the metrics
in your performance assessment package all
come as numbers. The
problem is that numbersdriven managers often
end up producing reams
of low-quality data. Think
about how companies
collect feedback on service from their customers.
It’s well known to statisticians that if you want evaluation forms to tell the real story, the anonymity
of the respondents must be protected. Yet out of a
desire to gather as much information as possible at
points of contact, companies routinely ask custom-
Promoter Score, which measures the likelihood
that customers will recommend a product or service. The NPS is a useful indicator only if recommendations play the dominant role in a purchase
decision; as its critics point out, customers’ propensity to switch in response to recommendations varies from industry to industry, so an NPS is probably
more important to, say, a baby-food manufacturer
than to an electricity supplier.
Similar issues arise about the much touted link
between employee satisfaction and pro?tability.
The Employee-Customer-Pro?t Chain pioneered by
Sears suggests that more-satis?ed employees produce more-satis?ed customers, who in turn deliver
higher pro?ts. If that’s true, the path is clear: Keep
your employees content and watch those pro?ts
soar. But employees may be satis?ed mainly because they like their colleagues (think lawyers) or
because they’re highly paid and deferred to (think
investment bankers). Or they may actually enjoy
what they do, but their customers value price above
the quality of service (think budget airlines).
A particular bugbear of mine is the application of
?nancial metrics to non?nancial activities. Anxious
to justify themselves rather than be outsourced,
many service functions (such as IT, HR, and legal)
try to devise a return on investment number to
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The Five Traps of Performance Measurement
help their cause. Indeed, ROI is often described as
the holy grail of measurement – a revealing metaphor, with its implication of an almost certainly
doomed search.
Suppose an HR manager undertakes to assign
an ROI number to an executive training program.
Typically, he or she would ask program participants
to identify a bene?t, assign a dollar value to it, and
estimate the probability that the bene?t came from
the program. So a bene?t that is worth $70,000 and
has a 50% probability of being linked to the program means a program bene?t of $35,000. If the
program cost $25,000, the net bene?t is $10,000 – a
40% ROI.
Think about this for a minute. How on earth can
the presumed causal link be justi?ed? By a statement like “I learned a production algorithm at the
program and then applied it”? Assessing any serious executive program requires a much more sophisticated and qualitative approach. First you have
to specify ahead of time the needs of the program’s
stakeholders – participants, line managers, and
sponsors – and make sure that the syllabus meets
your organizational and talent-management objectives. Once the program has ended, you have to
look beyond immediate evaluations to at least six
months after participants return to the workplace;
their personal feedback should be incorporated in
the next annual company performance review. At
the soft drinks company Britvic, HR assesses its executive coaching program by tracking coachees for
a year afterward, comparing their career trajectories with those of people who didn’t get coached.
trap
Gaming Your Metrics
In 2002 a leaked internal
memo from associates at
Clifford Chance, one of
the world’s largest law
firms, contended that
pressure to deliver billable hours had encouraged its lawyers to pad
their numbers and created an incentive to allocate to senior associates work that could be done by
less expensive junior associates.
Lawyers aren’t the only ones: A number of prominent companies have been caught trying to manipulate their numbers. Since 2004 Royal Dutch Shell
has paid $470 million to settle lawsuits relating to
its overstatement of reserves. Morgan Stanley was
reportedly willing to lose €20 million on a securi-
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ties trade for the Finnish government just before
closing its books for 2004 in order to improve its
position in the league table for global equity capital
market rankings.
You can’t prevent people from gaming numbers,
no matter how outstanding your organization. The
moment you choose to manage by a metric, you
invite your managers to manipulate it. Metrics are
only proxies for performance. Someone who has
learned how to optimize a metric without actually
having to perform will often do just that. To create
an effective performance measurement system, you
have to work with that fact rather than resort to
wishful thinking and denial.
It helps to diversify your metrics, because it’s a
lot harder to game several of them at once. Clifford Chance replaced its single metric of billable
hours with seven criteria on which to base bonuses:
respect and mentoring, quality of work, excellence
in client service, integrity, contribution to the community, commitment to diversity, and contribution
to the ?rm as an institution. Metrics should have
varying sources (colleagues, bosses, customers) and
time frames. Mehrdad Baghai and coauthors described in “Performance Measures: Calibrating for
Growth” (Journal of Business Strategy, July–August
1999) how the Japanese telecommunications company SoftBank measured performance along three
time horizons. Horizon 1 covered actions relevant
to extending and defending core businesses, and
metrics were based on current income and cash
?ow statements. Horizon 2 covered actions taken
to build emerging businesses; metrics came from
sales and marketing numbers. Horizon 3 covered
creating opportunities for new businesses; success
was measured through the attainment of preestablished milestones. Multiple levels like those make
gaming far more complicated and far less likely to
succeed.
You can also vary the boundaries of your measurement, by de?ning responsibility more narrowly
or by broadening it. To reduce delays in gate-closing
time, Southwest Airlines, which had traditionally
applied a metric only to gate agents, extended it
to include the whole ground team – ticketing staff,
gate staff, and loaders – so that everyone had an
incentive to cooperate.
Finally, you should loosen the link between
meeting budgets and performance; far too many
bonuses are awarded on that basis. Managers may
either pad their budgets to make meeting them
easier or pare them down too far to impress their
bosses. Both practices can destroy value. Some com-
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panies get around the problem by giving managers leeway. The office supplier Staples, for example,
lets them exceed their budgets if they can demonstrate that doing so will lead to improved service for
customers. When I was a CFO, I offered scope for
budget revisions during the year, usually in months
three and six. Another way of providing budget ?exibility is to set ranges rather than speci?c numbers
as targets.
trap
Sticking to Your
Numbers Too Long
As the saying goes, you
manage what you measure. Unfortunately, performance assessment
systems seldom evolve
as fast as businesses do.
Smaller and growing
companies are especially
likely to fall into this
trap. In the earliest stages, performance is all about
survival, cash resources, and growth. Comparisons
are to last week, last month, and last year. But as
the business matures, the focus has to
move to pro?t and the comparisons to
competitors.
It’s easy to spot the need for change
after things have gone wrong, but how
can you evaluate your measures before
they fail you? The answer is to be very
precise about what you want to assess,
be explicit about what metrics are assessing it, and make sure that everyone
is clear about both.
In looking for a measure of customer
satisfaction, the British law ?rm Addleshaw Booth (now Addleshaw Goddard) discovered
from a survey that its clients valued responsiveness
most, followed by proactiveness and commercialmindedness. Most ?rms would interpret this ?nding to mean they needed to be as quick as possible.
Addleshaw Booth’s managers dug deeper into the
data to understand more exactly what “responsiveness” meant. What they found was that they needed
to differentiate between clients. “One size does not
?t all,” an employee told me. “Being responsive for
some clients means coming back to them in two
hours; for others, it’s 10 minutes.”
The point is that if you specify the indicator
precisely and loudly, everyone can more easily see
when it’s not ?t for the purpose. The credit-rating
agencies have come under attack because they gave
AAA ratings to so many borrowers who turned out
to be bad risks. The agencies have argued in their
own defense that lenders misunderstood what the
ratings meant. The AAA rating, they claim, was
awarded on the basis of borrowers’ credit records,
and it described the likelihood of default under
normal market conditions; it did not factor in what
might happen in the event of a massive shock to
the ?nancial system. Reasonable as this explanation
may be, it is no consolation to those who thought
they knew what the magic AAA represented.
•••
Why do organizations that excel in so many other
ways fall into these traps? Because the people managing performance frameworks are generally not
experts in performance measurement. Finance
managers are pro?cient at tracking expenses, monitoring risks, and raising capital, but they seldom
have a grasp of how operating realities connect
with performance. They are precisely the people
who strive to reduce judgments to a single ROI
number. The people who understand performance
are line managers – who, of course, are crippled by
con?icts of interest.
The moment you choose to manage
by a metric, you invite your managers
to manipulate it.
A really good assessment system must bring ?nance and line managers into some kind of meaningful dialogue that allows the company to bene?t
from both the relative independence of the former
and the expertise of the latter. This sounds straightforward enough, but as anyone who’s ever worked
in a real business knows, actually doing it is a rather
tall order. Then again, who says the CEO’s job is
supposed to be easy?
Andrew Likierman ([email protected]) is
the dean of London Business School, a nonexecutive
director of Barclays Bank, and the chairman of the
UK’s National Audit Office.
Reprint R0910L
To order, see page 143.
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Harvard Business Review 101
9/4/09 12:39:36 PM
Harvard Business Review Notice of Use Restrictions, May 2009
Harvard Business Review and Harvard Business Publishing Newsletter content on EBSCOhost is licensed for
the private individual use of authorized EBSCOhost users. It is not intended for use as assigned course material
in academic institutions nor as corporate learning or training materials in businesses. Academic licensees may
not use this content in electronic reserves, electronic course packs, persistent linking from syllabi or by any
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learning management systems or use persistent linking or other means to incorporate the content into learning
management systems. Harvard Business Publishing will be pleased to grant permission to make this content
available through such means. For rates and permission, contact [email protected].
Michael E. Raynor is a
director at Detoitte Services
LP, and Mumtaz Ahmed
is a principal witii Deloitte
Consulting LLP and the chief
strategy officer for Deloitte
LLP. They are the authors
of The Three Rules: How Exceptional Companies Think,
forthcoming from Portfolio.
MUCH OF THE STRATEGY and management advice
that business leaders turn to is unreliable or impractical. That’s because those who would guide
us underestimate the power of chance. Gurus draw
pointed lessons from companies whose outstanding results may be nothing more than random fluctuations. Executives speak proudly of corporate
achievements that may be only lucky coincidences.
Unfortunately, almost no one provides scientifically
credible answers to every business leader’s basic
questions about superior performance: Which companies are worth studying? What sets them apart?
How can we follow their examples?
Frustrated by the lack of rigorous research, we
undertook a statistical study of thousands of companies, and eventually identified several hundred
io8 Harvard Business Review April 2013
among them that have done well enough for a long
enough period of time to qualify as truly exceptional.
Then we discovered something startling: The many
and diverse choices that made certain companies
great were consistent with just three seemingly elementary rules:
1. Better before cheaper—in other words, compete on differentiators other than price.
2. Revenue before cost—that is, prioritize increasing revenue over reducing costs.
3. There are no other rules—so change anything
you must to follow Rules 1 and 2.
The rules don’t dictate specific behaviors; nor are
they even general strategies. They’re foundational
concepts on which companies have built greatness over many years. How did these organizations’
HBR.ORG
April 2013 Harvard Business Review 109
THREE RULES FOR MAKING A COMPANY TRULY GREAT
leaders come to adopt them? We have no idea—nor
do we know whether the executives even followed
them consciously. Nevertheless, the rules can be
used to help today’s and tomorrow’s leaders increase
the chances that their companies, too, will deliver decades of exceptional performance.
we examined, barely 12% met our criteria, even for
Long Runner status.)
Exceptional companies, it turns out, come in all
shapes and sizes. 3M, with its legendary innovation
and thousands of products in commercial and industrial markets, made the list, but so did WD-40—a
company built on a single, unpatented product that
Beyond Truisms
was designed to prevent corrosion on nuclear misThe impetus for our research was the increasing pop- siles and has since become most famous as the bane
ularity over the past 30 years of “success study” busi- of squeaky hinges. The globally ubiquitous McDonness books and articles. Perhaps the most famous of ald’s proved to be exceptional, but so did Luby’s, a
these are Thomas Peters and Robert Waterman’s In cafeteria chain, when it had only 43 locations (it has
Search of Excellence (1982) and Jim Collins’s Good to since grown to almost 100). IBM qualified, and so did
Great (2001), but there are many others. The prob- Syntel, even though at the time it was only 0.5% of
lem with them is they don’t give us any way to judge Big Blue’s size.
whether the companies they hold up as examples
To understand what was behind superior perforare indeed exceptional. Randomness can crown an mance, we identified trios in each of nine industries;
average company king for a year, two years, even a each trio consisted of one company from each of
decade, before performance reverts to the mean. If our performance categories, carefully matched for
we can’t be sure that the performance of companies years of overlap and relative size, with one from each
mentioned in success studies was caused by more of our three performance categories. We searched
than just luck, we can’t know whether to imitate their for behavioral differences that might explain the
behaviors.
specific performance differences we had discerned.
We tackled the randomness problem head-on. For instance, if a Miracle Worker’s ROA advantage
Finding what we assumed would be weak signals was driven primarily by superior gross margins, we
in noisy environments required a lot of data, so we looked for behaviors that might account for that. If
began with the largest database we could find—the asset utilization was salient, we looked for the bemore than 25,000 companies that have traded on haviors that drove asset utilization. Where the data
U.S. exchanges at any time from 1966 to 2010. We permitted, we builtfinancialmodels to estimate the
measured performance using return on assets (ROA), impact of these behavioral differences on perfora metric that reflects strong, stable performance—un- mance. To illustrate: Heartland Express, the Miracle
like, say, total shareholder return, which may reflect Worker in our trucking-industry trio, relied entirely
the vagaries of the stock market and chainges in inves- on gross-margin advantage for its ROA edge, and
tor expectations rather than fundamental company its gross margins seemed to be a function of higher
performance. We defined two categories of superior prices. By recalculating the company’s financiáis
results: Miracle Workers fell in the top 10% of ROA forwithout that premium, we satisfied ourselves that
all 25,000 companies often enough that their per- Heartland’s pricing was a plausible explanation for
formance was highly unlikely to have been a fluke; its higher gross margins and thus its better ROA.
Long Runners fell in the top 20% to 40% and, again,
Then things got messy. We repeatedly tried and
did so consistently enough that luck was highly un- failed to isolate measurable behaviors that were conlikely to have been the reason. We call the companies sistently relevant. For example, atfirstit seemed that
in both these categories exceptional performers. For an M&A-shunning strategy might be driving excepcomparison purposes, we also identified companies tional results in the trucking industry; yet during one
that were Average Joes. (See the sidebar “Finding the 15-year period, top-performing Heartland was also
Signal in the Noise.”)
the most acquisitive. Nor could we conclude that a
A total of 174 companies qualified as Miracle propensity toward M&A was a consistently posirive
Workers, and 170 qualified as Long Runners. That’s factor in other industries, because in confectionery,
the entire population of companies that separated for example, Wrigley, the Miracle Worker, and Rocky
themselves from the noise in this way. (It’s probably Mountain Chocolate Factory, the Average Joe, had
worth mentioning that of the allegedly superior com- grown organically, whereas Tootsie Roll, the Long
panies mentioned by 19 high-profile success studies Rurmer, largely bought its growth.
110 Harvard Business Review April 2013
HBR.ORG
A statistical study of thousands
of companies identified several
hundred that have been good
enough long enough to qualify as
truly exceptional. It also revealed
that their strategic choices over
decades of success have been
consistent with three elementary
rules:
Better before cheaper (it’s best to compete on differentiators other than price).
Revenue before cost (prioritize Increasing
revenue over reducing costs).
There are no other rules (change anything
you must to follow the first two).
With few exceptions, the best companies behave as though these principles
guide them through all their important
decisions, from acquisitions to diversification to resource allocation to pricing.
Was customer focus the key? Nope. Innovation?
Risk taking? Nope and nope. All these factors were
ass