Dr. Bush and I describe our experience applying the principles of decision analysis to small business, which typically do not have access to as many informed resources as larger organizations where decision analysis is more routinely applied.Published in "Decision Analysis Today," newsletter of the INFORMS Decision Analysis Society, Volume 29, No. 1, April 2010, pg. 16.

1. Tighten
up
the
Ship
or
build
an
Airplane?
-­‐
How
to
decide?
Robert
D.
Brown,
III
and
Gerald
A.
Bush
Tightening
up
the
ship,
rowing
faster,
getting
more
draft
per
stroke,
putting
stronger
rowers
on
the
oars
-­‐
whether
you
are
in
a
short
race
or
taking
a
long
voyage
across
the
sea,
selection
and
use
of
the
most
efficient
resources
and
processes
is
essential.
Sometimes
your
ship
needs
new
oars,
sometimes
better
communication
by
the
coxswain
calling
the
beat,
and
almost
always
a
clearer
map
to
navigate
the
trip
you
are
taking.
These
ideas
are
the
foundation
of
many
modern
business
improvement
initiatives
and
the
type
of
decision
that
leaders
make.
Businesses
seem
to
spend
a
lot
of
time
pursuing
operational
excellence.
A
variety
of
industrial
engineering
methods
exist
today
to
solve
problems
and
redesign
key
processes
and
systems
to
accomplish
this
goal.
Such
process
tools
and
data
analysis
methods
contribute
to
the
rethinking
about
how
products
or
services
are
delivered
internally
to
a
business
or
externally
to
its
customers.
It
has
become
competitively
necessary
to
seek
operational
excellence,
no
matter
what
business
model
or
strategy
you
pursue.
“…operational
excellence
does
not
However,
operational
excellence
does
not
ensure
a
competitive
advantage
assure
a
competitive
advantage
over
others
in
over
others
in
the
game.”
the
game.
Big
sailing
ships
enabled
small
European
countries
to
become
global
empires
in
the
17th
century
by
overpowering
those
who
did
not
have
such
ships
and
guns.
Later
on,
early
20th
century
warship
captains
were
surprised
when
they
looked
up
and
saw
airplanes
about
to
drop
bombs
on
their
ships.
Such
disruptive
competitive
advantages
cannot
be
fully
countered
just
by
redesigning
ships
for
incremental
improvements
in
efficiency,
pursuing
operational
excellence
or
using
advanced
data
analytics.
While
everyone
aims
for
operational
excellence
by
rowing
faster
and
more
efficiently,
someone
else
invents
sails
and
guns.
Then,
all
too
soon,
being
a
player
requires
airplanes
and
bombs.
Operational
excellence
contributes
to
more
efficient
use
of
resources
and
incrementally
better
results
from
existing
business
models,
while
strategic
innovations
explore
valuable
frontiers
or
new
combinations
of
business
models.
In
the
business
world,
an
example
of
“sails
and
guns”
might
be
the
use
of
supply
chain
technology
to
create
an
advantaged
cost
structure
and
high
velocity
in
the
retailing
industry,
e.g.
WalMart.
An
example
of
“airplanes
and
bombs”
might
be
Amazon’s
use
of
the
intelligent
recommendations
to
produce
a
richer
sales
basket
and
distributed
warehousing
with
overnight
shipping
to
obviate
the
need
for
stores.
These
competitive
advantages
allow
such
leaders
to
dominate
their
game,
while
operational
excellence
initiatives
assure
the
scale
and
sustainability
of
their
businesses.
“Big
data,”
“data
visualization,”
and
“analytics”
are
the
hot,
new
trends
offering
insights
into
previously
untapped
value
for
improving
operations.
The
real
time
capability
to
make
tactical
decisions
is
phenomenal.
A
lot
of
expensive
technology
and
systems
will
be
sold
and
attention
paid
to
analytics
in
the
next
few
years
as
this
next
level
of
operational
excellence
is
pursued.

2. With
all
the
technology
available
today,
analytics
will
quickly
become
another
necessary
ticket
for
operational
excellence
and
profitability
for
your
business
–
as
long
as
the
game
doesn’t
change.
“…the
data
of
a
business
is
a
treasure
trove
for
driving
Is
it
a
problem
when
the
game
changes?
operational
excellence
–
as
long
Business
trends
tend
to
develop
a
life
of
their
own,
demanding
organizational
focus
and
commitment
as
the
game
doesn’t
change.”
to
implementation
of
new
systems
to
improve
the
business.
This
can
prevent
decision
makers
from
recognizing
important
strategic
inflections
or
clearly
understanding
when
change
to
the
underlying
business
model
is
needed.
For
example,
a
recent
Wall
Street
Journal
article
described
how
a
struggling
food
distribution
company
invested
in
a
big
data
analytics
system
to
lift
it
out
of
its
malaise.
The
system
successfully
improved
sales
by
3-­‐4%
over
a
period
of
one
year,
with
incremental
value
per
delivery.
Despite
declaring
success,
it
was
not
clear
if
the
sales
improvement
was
sustainable,
as
it
did
not
expand
the
company’s
addressable
market.
Furthermore,
over
the
same
period
of
time
inflation
increased
by
about
3.5%,
matching
the
improvement
in
sales
for
the
year.
At
the
same
time,
other
food
retailers
were
changing
the
game
with
organic
and
locally
sourced
produce
displayed
in
appealing
fashion
inside
their
big
box
stores.
Trying
to
compete
with
these
players
who
were
achieving
major
gains
in
market
share,
the
management
team
of
the
struggling
food
company
may
have
fallen
prey
to
any
of
three
common
errors:
1. Succumbing
to
the
siren
song
of
trendy
technology
promises
in
a
time
of
desperation,
2. Allowing
operational
tunnel
vision
to
keep
them
from
acknowledging
the
true
nature
of
the
problem,
3. Not
evaluating
strategic
alternatives
that
could
deliver
a
better
return
on
capital.
So,
how
to
get
the
right
perspective
for
decisions?
Data
analytics
and
engineering
methods
look
inside
available
data
and
processes
to
achieve
better
performance
from
an
existing
business
model.
This
is
important,
but
too
much
of
a
focus
on
today
can
make
you
unprepared
for
what
is
coming
next.
Strategic
thinking
is
about
asking,
“What
are
the
real
problems
or
opportunities
we
face?
Have
we
considered
other
ideas
to
create
a
real
competitive
advantage?”
A
robust
business
considers
these
three
scenarios:
Scenario
1
In
a
stable
situation,
visualizing
available
data
is
sufficient
to
make
decisions.
Running
a
manufacturing
plant,
optimizing
a
network,
or
planning
product
allocation
can
rely
on
historic
data
trends
for
making
good
operating
decisions.
In
fact,
losing
time
overanalyzing
things
may
be
counterproductive
in
a
high
velocity
business.
Imagine
a
trading
house
having
the
patience
for
anything
beyond
a
few
fractions
of
a
second
to
capture
an
arbitrage
gap
in
the
market.
Scenario
2
When
there
are
problems
or
opportunities
to
change,
visualizing
data
can
help
to
illustrate
the
gaps.
A
quick
decision
analysis
for
comparing
alternative
solutions
prevents
the
tendency
to
jump
on
the
first
good
idea
that
comes
up.
The
process
may
validate
your
intuitive
hunch
or,
it
may
lead
to
some
ideas
that
better
address
issues
of
alignment,
risk
or
uncertainty
in
the

3. system.
The
result
will
be
more
informed
decisions,
along
with
the
knowledge
of
what
it
will
take
to
implement
the
change
successfully.
Instead
of
improving
just
a
few
percent,
it
may
provide
a
major
shift
in
growth
potential
or
operating
efficiency.
Also,
by
quantifying
the
business
value
of
initiatives,
decision
analysis
leads
to
more
objective
prioritization
and
focus
of
scarce
resources,
rather
than
overloading
the
organization
with
too
many
things
at
once.
Scenario
3
When
a
business
has
lost
its
competitive
advantage
or
an
idea
comes
up
that
may
be
a
real
“…it
is
usually
the
uncertainties
innovation,
then
a
strategic
decision
analysis
or
risks
that
lead
to
competitive
process
will
be
very
helpful.
It
is
usually
the
advantages.”
uncertainties
or
risks
that
provide
the
opportunity
to
create
a
competitive
advantage.
If
you
can
develop
options
to
navigate
through
the
risks
and
complexities
of
a
situation,
you
can
create
a
dominant,
game-­‐changing
business.
Many
businesses
spend
the
majority
of
their
time
trying
to
win
on
operational
efficiency
alone,
so
they
miss
the
next
big
opportunity.
Xerox
may
be
the
poster
child
for
this,
failing
to
use
the
innovations
from
their
Palo
Alto
Research
Center
(PARC),
while
many
Silicon
Valley
startups
built
new
businesses
around
them,
including
Apple.
Even
for
the
innovators,
however,
their
new
ideas
have
a
time
horizon
when
they
will
be
obsolete.
Once
others
begin
to
figure
out
the
same
information
inefficiencies
and
risks,
the
game
collapses
again
to
one
of
incremental
competition
for
declining
margins.
The
innovation
cycle
needs
to
start
anew.
Let
Decision
Science
be
your
guide
Data
analytics
is
necessary
to
run
a
business,
but
it
is
insufficient
without
decision
analysis
to
quantify
the
value
of
the
ideas
that
emerge
so
you
can
make
good
choices.
An
honest
assessment
about
your
business
situation
and
the
nature
of
the
information
you
have
(facts,
uncertainties,
risks)
avoids
the
biases
and
traps
that
quick
intuitive
decisions
can
lead
you
into.
Having
the
understanding
and
capabilities
to
guide
business
teams
and
decision
makers
“Decision
analysis
can
dramatically
through
a
quality
process
to
create
and
compare
reduce
the
risk
of
being
surprised
alternatives
utilizes
the
full
creativity,
value
of
by
a
wrong
decision
and
being
too
information,
and
value
of
control
available
late
to
do
anything
about
it.”
within
the
risks
and
uncertainties.
Where
it
has
become
a
part
of
the
culture,
executives
consider
decision
analysis
a
significant
competitive
advantage
over
others
who
rely
on
data
analysis
or
assumptions
in
their
business
case
approach
to
making
tough
decisions.
John
W.
Tukey
explained
the
distinction
between
exploratory
data
analysis
and
confirmatory
data
analysis
with
the
quote,
“I
would
rather
be
approximately
right
than
be
precisely
wrong.”
Decision
analysis
can
provide
the
guidance
to
know
when
to
continue
pursuing
operational
excellence
and
how
to
do
so,
or
when
to
switch
to
new
frontiers
of
value
creation.
It
dramatically
reduces
the
risk
of
being
surprised
by
a
wrong
decision
and
being
too
late
to
do
anything
about
it.

4. Gerald
A.
Bush,
Ph.D.
works
with
companies
on
innovative
business
strategies,
dealing
with
complex
business
decisions
and
building
an
adaptive
approach
in
dynamic
markets.
Typical
clients
have
included
Abbott,
GSK,
J&J,
Kimberly
Clark,
Merck,
Novartis,
Roche,
Delta
Air
Lines,
JetBlue,
Chevron,
Dow,
ExxonMobil,
Shell,
Motorola
and
HP.
Typical
engagements
include
creation
of
novel
product
concepts,
development
of
cost-­‐advantaged
operations,
decisions
on
global
marketing,
organizational
redesign
and
integration
of
acquisitions,
decisions
on
major
facilities
investments,
resolution
of
labor-­‐management
issues
and
adoption
of
information
technology
strategies.
Gary
is
a
graduate
from
Georgia
Institute
of
Technology.
He
is
an
invited
speaker
at
the
New
York
University
Stern
School
for
Business
and
the
Institute
for
Operations
Research
and
Management
Sciences.
Robert
D.
Brown,
III
is
an
experienced
decision
strategist
with
over
17
years
of
professional
experience
and
a
world
class
architect
of
complex,
quantitative
models
using
Analytica
software.
He
provides
advanced
decision
guidance,
risk
management,
and
business
analytics
to
help
executive
decision
makers
gain
deep
insights
into
complex
and
risky
capital
investment
opportunities,
system
behavior,
and
planning
exercises.
Some
of
his
clients
have
included
Bechtel-­‐SAIC,
Canon,
Chevron,
Cisco
Systems,
ExxonMobil,
Milliken,
and
Novartis.
Robert
is
a
graduate
from
the
school
of
Mechanical
Engineering
at
Georgia
Institute
of
Technology.