Discuss the strategies and approaches in the development of new analgesics

Discuss the strategies and approaches in the development of new analgesics

For this case study, read the attached article and identify 4-5 issues and/or problems and then clearly research and state the concepts with the use of corroborating sources.  All issues must be analyzed and provide alternative solutions and/or options and provide recommendations for each one.  At least 3 sources must be used and must provide links to the articles and/or references that are within the last 5 years.
DECISION MAKING
E ALL KNOW THE POWER
of the killer app.
Over the years, groundbreaking systems from compa-
nies
such as American Airlines (electronic reservations),
Otis Elevator (predictive maintenance), and American
Hospital Supply (online ordering) have dramatically
boosted their creators’ revenues and reputations. These
heralded – and coveted – applications amassed and ap-
plied data in ways tha
t upended customer expectations
and optimized operations to
unprecedented degrees.
They transformed technology from a supporting tool
into a strategic weapon.
Companies questing for killer apps generally focus all
their firepower on the one area that promises to create
the greatest competitive advantage. But a new breed of
company is upping the stakes. Organizations such as
Amazon, Harrah’s, Capital One, and the Boston Red Sox
have dominated their fields by deploying industrial-
strength analytics across a wide variety of activities. In
essence, they are transforming their organizations into
armies of killer apps and crunching their way to victory.
Organizations are competing on analytics not just be-
cause they can – business today is awash in data and data
Every company
can learn from
what these
firms do.
by Thomas H. Davenport
Some
companies have
built their very
businesses
on their ability
to collect,
analyze, and
act on data.
COMPETING
ON ANALYTICS
crunchers – but also because they should. At a time when
firms in many industries offer similar products and use
comparable technologies, business processes are among
the last remaining points of differentiation. And analyt-
ics competitors wring every last dr
op of value from those
processes. So, like other companies, they know what prod-
ucts their customers want, but they also know what prices
those customers will pay, how many items each will buy
in a lifetime, and what triggers will make people buy more.
Like other companies, they know compensation costs and
turnover rates, but they can also calculate how much per-
sonnel contribute to or detract from the bottom line and
how salary levels relate to individuals’ performance. Like
other companies, they know when inventories are run-
ning low, but they can also predict problems with demand
and supply chains, to achieve low rates of inventory and
high rates of perfect orders.
And analytics competitors do all those things in a coor-
dinated way, as part of an overarching strategy champi-
oned by top leadership and pushed down to decision mak-
ers at every level. Employees hired for their expertise with
numbers or trained to recognize their importance are
armed with the best evidence and the best quantitative
tools. As a result, they make the best decisions: big and
small, every day, over and over and over.
Although numerous organizations are embracing ana-
lytics, only a handful have achieved this level of profi-
ciency. But analytics competitors are the leaders in their
varied fields–consumer products, finance, retail, and travel
and entertainment among them. Analytics has been in-
strumental to Capital One,  which has exceeded 20
%
growth in earnings per share every year since it became
a public company. It has allowed Amazon to dominate on-
line retailing and turn a profit despite enormous invest-
ments in growth and infrastructure. In sports, the real se-
cret weapon isn’t steroids, but stats, as dramatic victories
by the Boston Red Sox, the New England Patriots, and the
Oakland A’s attest.
At such organizations, virtuosity with data is often part
of the brand. Progressive makes advertising hay from its
detailed parsing of individual insurance rates. Amazon
customers can watch the company learning about them
as its service grows more targeted with frequent pur-
chases. Thanks to Michael Lewis’s best-selling book
Mon-
eyball,
which demonstrated the power of statistics in pro-
fessional baseball, the Oakland A’s ar
e almost as famous
for their geeky number crunching as they are for their
athletic prowess.
To identify characteristics shared by analytics compet-
itors, I and two of my colleagues at Babson College’s
Working Knowledge Research Center studied 32 organi-
zations that have made a commitment to quantitative,
fact-based analysis. Eleven of those organizations we clas-
sified as full-bore analytics competitors, meaning top
management had announced that analytics was key to
their strategies; they had multiple initiatives under way
involving complex data and statistical analysis, and they
managed analytical activity at the enterprise (not depart-
mental) level.
This article lays out the characteristics and practices of
these statistical masters and describes some of the very
substantial changes other companies must undergo in
order to compete on quantitative turf. As one would ex-
pect, the transformation requires a significant invest-
ment in technology,
the accumulation of massive stores
of data, and the formulation of companywide strategies
for managing the data. But at least as important, it re-
quires executives’ vocal, unswerving commitment and
willingness to change the way employees think, work, and
are treated. As Gary Loveman, CEO of analytics competi-
tor Harrah’s, frequently puts it,“Do we think this is true?
Or do we know?”
Anatomy of an Analytics Competitor
O
ne analytics competitor that’s at the top of its
game is Marriott International. Over the past 20
years, the corporation has honed to a science its
system for establishing the optimal price for guest
rooms (the key analytics process in hotels, known as rev-
enue management). Today, its ambitions are far grander.
Through its Total Hotel Optimization program, Marriott
has expanded its quantitative expertise to areas such as
conference facilities and catering, and made related tools
available over the Internet to property revenue managers
and hotel owners. It has developed systems to optimize of-
ferings to frequent customers and assess the likelihood of
those customers’ defecting to competitors. It has given
local revenue managers the power to override the sys-
tem’s recommendations when certain local factors can’t
be predicted (like the lar
ge number of Hurricane
Katrina evacuees arriving in Houston). The company has
even created a revenue opportunity model, which com-
putes actual revenues as a percentage of the optimal rates
that could have been charged. That figure has grown from
83
%
to 91
%
as Marriott’s revenue-management analytics
has taken root throughout the enterprise. The word is out
among property owners and franchisees: If you want to
squeeze the most revenue from your inventory, Marriott’s
approach is the ticket.
Clearly, organizations such as Marriott don’t behave
like traditional companies. Customers notice the differ-
ence in every interaction; employees and vendors live the
100
harvard business review
DECISION MAKING
Thomas H. Davenport
([email protected]) is the
President’s Distinguished Professor of Information Technol-
ogy and Management at Babson College in Babson Park,
Massachusetts, the director of research at Babson Executive
Education, and a fellow at Accenture. He is the author of
Thinking for a Living
(Harvard Business School Press, 2005).
difference every day. Our study found three key attributes
among analytics competitors:
Widespread use of modeling and optimization.
Any
company can generate simple descriptive statistics about
aspects of its business–average revenue per employee, for
example, or average order size. But analytics competitors
look well beyond basic statistics. These companies use
predictive modeling to identify the most profitable cus-
tomers – plus those with the greatest profit potential and
the ones most likely to cancel their accounts. They pool
data generated in-house and data ac-
quired from outside sources (which
they analyze more deeply than do their
less statistically savvy competitors) for
a comprehensive understanding of
their customers. They optimize their
supply chains and can thus determine
the impact of an unexpected con-
straint, simulate alternatives, and route
shipments around problems. They es-
tablish prices in real time to get the
highest yield possible from each of
their customer transactions. They cre-
ate complex models of how their oper-
ational costs relate to their financial
performance.
Leaders in analytics also use sophis-
ticated experiments to measure the
overall impact or “lift” of intervention
strategies and then apply the results
to continuously improve subsequent
analyses. Capital One, for example, con-
ducts more than 30,000 experiments
a year, with different interest rates,
incentives, direct-mail packaging, and
other variables. Its goal is to maximize
the likelihood both that potential cus-
tomers will sign up for credit cards and
that they will pay back Capital One.
Progressive employs similar experi-
ments using widely available insurance
industry data. The company defines
narrow groups, or cells, of customers:
for example, motorcycle riders ages 30
and above, with college educations,
credit scores over a certain level, and
no accidents. For each cell, the com-
pany performs a regression analysis to
identify factors that most closely corre-
late with the losses that group engen-
ders. It then sets prices for the cells,
which should enable the company to
earn a profit across a portfolio of cus-
tomer groups, and uses simulation soft-
ware to test the financial implications
of those hypotheses. With this approach, Progressive can
profitably insure customers in traditionally high-risk cat-
egories. Other insurers reject high-risk customers out of
hand, without bothering to delve more deeply into the
data (although even traditional competitors, such as All-
state, are starting to embrace analytics as a strategy).
An enterprise approach.
Analytics competitors under-
stand that most business functions – even those, like mar-
keting, that have historically depended on art rather than
science – can be improved with sophisticated quantitative
january 2006
101
YEL
MAG
CYAN
BLACK
Competing on Analytics