1
Food Insecurity in America:
A Macro and Micro-Level
Analysis
Virag Mody, Marielle Lenowitz, Aneesha Chowdhary, Eboni Freeman,
Jasmyn Mackell and Ben Gross
April 12, 2017

2
TABLE
OF
CONTENTS
1. Executive
Summary……………………………………………………………………………………..3
2. Project
Goal…………………………………………………………………………………………………4
3. Part
I:
A
Macro
Perspective
on
Food
Insecurity…………………………………………….4
3.1
Food
Insecurity
in
the
United
States……………………………………………4
3.2
Selection
of
Exponential
Smoothing
and
Alpha
Coefficient………….4
3.3
Exponential
Smoothing
Data
Analysis………………………………………….5
3.4 Forecasting
Food
Insecurity
with
Regression
Analysis………………….6
3.5 Comparing
Forecasts
–
Exponential
Soothing
vs.
Regression………..7
3.6 Note
on
Regression
Analysis………………………………………………………..7
4.
Part
II:
Quality
of
Inventory
at
a
Local
Food
Pantry…………………………………………7
4.1
Background
on
Toco
Hills
Community
Alliance………………………......7
4.2
Data
Collection……………………………………………………………………………8
4.3
P-­‐Bar
Chart
Construction
and
Analysis…………………………………….….8
4.5 R-­‐Chart
Construction
and
Analysis
…………………………………………….10
5. Recommendation………………………………………………………………………………………..11
6.
Future
Considerations………………………………………………………………………………..11
7. Appendix
A
(for
Part
II
data
and
graphs)……………………………………………………..13
8. Appendix
B
(for
Part
I
data
and
graphs)………………………………………………………14
9. Sources……………………………………………………………………………………………………….19

3
1.
Executive
Summary
There
is
a
food
insecurity
epidemic
in
America.
In
2016
over
17
million
American
households
were-­‐
at
some
point-­‐
food
insecure
(USDA
Food
Security
Study).
The
taxpayer
burden
of
this
insecurity
is
massive;
in
fiscal
year
2015,
the
federal
government
spent
over
75
billion
dollars
on
supplemental
food
programs
(Center
for
Budget
and
Policy
Priorities).
While
some
of
the
hunger
burden
is
relieved
through
specific
federal
programs,
such
as
the
free
and
reduced
lunch
program
for
students,
SNAP,
and
WIC,
a
significant
amount
of
food
is
distributed
through
non-­‐profit
entities
such
as
food
banks
and
food
pantries.
In
fact,
1
of
out
every
7
US
families
at
least
partially
relied
on
a
food
bank
or
food
pantry
in
the
last
year
to
meet
their
needs
(Feeding
America
Study).
For
our
study,
we
first
wanted
to
focus
on
a
local
food
pantry
where
we
could
offer
a
recommendation
that
could
benefit
the
thousands
of
families
whom
it
serves
each
month.
We
chose
Toco
Hills
Community
Alliance
in
Druid
Hills
to
conduct
our
survey,
due
to
its
proximity
to
Emory
and
the
wide
range
of
food
products
it
receives
each
month.
By
visiting
the
food
pantry,
we
were
able
to
collect
both
qualitative
and
quantitative
observations
about
its
inventory
management
system
and
the
clientele
it
serves.
We
took
16
samples
of
Toco
Hill
Community
Alliance’s
inventory
in
an
attempt
to
calculate
the
approximate
number
of
goods
that
are
defective
(expired).
Using
this
data,
we
were
then
able
to
calculate
that
number
of
defective
goods
per
million,
as
well
as
construct
a
P-­‐Bar
Chart
for
the
number
of
expired
goods
present
in
each
sample.
Additionally,
we
created
an
R-­‐Chart
of
the
sample
ranges
to
better
understand
the
extent
of
quality
management
situation.
From
both
of
these
charts,
we
found
that
the
number
of
expired
goods,
as
well
as
the
range
in
expiration
for
expired
goods,
varied
widely.
Such
variation
showed
that
the
food
bank
most
likely
does
not
have
a
system
in
place
to
ensure
that
goods
expiring
soonest
are
distributed
first.
To
better
understand
our
data,
we
also
examined
broader
food
insecurity
trends
in
the
US.
Using
data
from
USDA
studies
on
food
insecurity
in
the
US
from
1998-­‐2015,
we
conducted
exponential
smoothing
forecasts
of
total
US
households
and
US
households
with
general
food
insecurity
(as
well
as
for
subsections
with
low
food
security
and
very
low
food
security).
These
forecasts
turned
out
to
be
consistent
with
actual
data
from
the
period.
We
also
ran
regressions
for
these
four
categories
as
well,
which
had
a
noticeably
higher
error
when
compared
with
real
data
from
the
period.
We
then
used
the
regression
models
to
forecast
the
the
number
of
food
insecure
households
the
next
5
years.
Based
on
our
analysis
of
the
Toco
Hills
Community
Alliance
data,
as
well
as
the
P-­‐Bar
and
R-­‐Charts
we
constructed,
we
recommend
that
the
food
pantry
create
a
system
that
organizes
goods
by
expiration
date.
Goods
that
are
expiring
sooner
should
be
placed
in
the
front
of
the
room
and
on
the
outermost
edge
of
the
shelves,
as
a
means
of
encouraging
shoppers
to
pick
those
goods.
Meanwhile,
goods
that
are
received
and
have
several
years
before
their
expiration
should
be
placed
towards
the
back,
because
they
have
a
significantly
longer
“use-­‐by”
date.
This
organizational
solution
will
not
completely
eliminate
the
food
pantry’s
problem
with
expired
goods;
indeed,
some
of
the
goods
the
food
pantry
receives
are
already
close
to,
if
not
past,
their
expiration
date.
The
implementation
of

4
such
a
system
could
make
an
impact
in
reducing
the
total
number
of
expired
goods
that
the
pantry
keeps
in
its
inventory.
2.
Project
Goal
This
project
aims
to
analyze
the
quality
of
inventory
at
the
Toco
Hills
Community
Alliance,
a
local
food
pantry
in
Atlanta,
Georgia.
Additionally,
to
understand
the
large
number
of
food
insecure
households
that
frequent
these
types
of
food
pantries,
this
project
also
sought
to
forecast
macro
level
data
about
food
insecurity,
such
as
the
total
number
of
food
insecure
households
each
year,
from
1998-­‐2015
(as
well
as
subsections
of
this
data,
such
as
those
with
very
low
food
security).
3.
Part
I:
A
Macro
Perspective
on
Food
Insecurity
3.1
Food
Insecurity
in
the
United
States
The
United
States
Department
of
Agriculture
(USDA)
defines
food
insecurity
as
a
state
in
which
“consistent
access
to
adequate
food
is
limited
by
a
lack
of
money
and
other
resources
at
times
during
the
year.”
Food
insecurity
exists
whenever
the
availability
of
healthy,
nutritionally
adequate,
and
safe
foods
is
limited,
or
the
ability
to
obtain
sufficient
foods
in
a
legitimate
and
socially
acceptable
way
is
uncertain.
An
estimated
1
in
7
Americans
struggles
with
food
insecurity.
We
were
interested
in
the
relationship
between
food
pantries
and
food
insecurity,
but
before
we
could
focus
specifically
on
our
local
food
pantry,
the
Toco
Hills
Community
Alliance,
we
wanted
to
understand
the
larger
food
insecurity
problem
in
the
United
States.
Using
data
from
the
USDA
report
entitled
“Household
Food
Security
in
the
United
States
in
2015,”
we
chose
to
forecast
Total
Food
Insecurity
as
a
function
of
Total
Households,
which
could
then
further
be
broken
down
into
Low
Food
Security
and
Very
Low
Food
Security
(Exhibit
8,
Appendix
B).
By
analyzing
this
data,
we
would
be
able
to
get
a
macro-­‐level
perspective
on
a
topic
that
affects
people
both
locally
within
the
Atlanta
area,
as
well
as
nationally.
3.2
Selection
of
Exponential
Smoothing
and
Alpha
Coefficient
To
properly
forecast,
the
first
step
is
to
identify
which
method
of
forecasting
is
most
appropriate
to
use.
The
five
methods
available
are
Naïve,
Moving
Average,
Weighted
Moving
Average,
Exponential
Smoothing,
and
Regression.
The
following
shows
our
analysis
and
applicability
of
each
method,
except
for
regression
analysis,
which
is
mentioned
later:
•
Naïve
Forecasting
–
This
method
does
not
appropriately
account
for
historical
data,
with
the
exception
of
the
previous
period.
At
a
minimum,
the
population
tends
to
grow
positively,
so
using
the
prior
period’s
data
point
would
be
empirically
wrong,
thus
eliminating
this
method
as
a
viable
option.
•
Moving
Average
–
This
method
weights
each
data
point
equally,
meaning
that
data
from
1998
is
just
as
relevant
as
data
from
2014.
Weighting
older
data
equally
to
recent
data
would
be
problematic
for
this
project
because
numerous
factors
influence
levels
of
food
security
over
time,
such
as
economic
trends,
immigration,
population
changes,
and
health.
These
factors
cause
food
insecurity
to
evolve
over
time,
meaning
that
more
current
factors
are
more
relevant
to
present
food
insecurity
trends.
Therefore,
the
historical
data
from
1998
should
not
have
as
much
weight
as
recent
years,
removing
the
Moving
Average
as
an
option.

5
•
Weighted
Moving
Average
–
WMA
could
have
some
applicability,
but
without
knowing
how
to
weight
historical
data,
doing
so
would
be
arbitrary.
This
eliminates
WMA.
•
Exponential
Smoothing
–
This
forecasting
method
assigns
exponentially
decreasing
weights
as
the
observations
get
older,
allowing
us
to
put
more
weight
on
more
recent
and
more
relevant
data,
which
was
the
concern
pointed
out
in
the
Moving
Average
model.
This
means
that
Exponential
Smoothing
is
a
viable
method
for
forecasting
our
data.
Given
that
there
are
macro
factors
for
variability
in
food
insecurity,
including
immigration,
population
changes,
health,
and
economic
factors,
we
cannot
solely
rely
on
historical
data,
as
there
is
most
likely
not
a
consistent,
holistic
trend.
However,
we
cannot
assume
an
alpha
of
1
because
it
will
become
naive
forecasting.
Additionally,
immigration,
population
changes,
and
the
economy
often
follow
trends
and
cycles,
so
to
some
extent,
historical
data
is
useful.
Thus,
to
use
only
the
previous
years
would
be
inaccurate
and
naïve,
while
discounting
historical
data
altogether
would
make
for
a
poor
forecast.
In
order
to
appease
both
sides
of
this
narrative,
we
selected
an
alpha
value
of
0.5
as
a
median
between
discounting
historical
data
and
accounting
for
historical
information.
3.3
Data
Analysis
–
Exponential
Smoothing
After
forecasting
using
exponential
smoothing,
the
following
graphs
show
noteworthy
information.
The
raw
data
can
be
found
in
Exhibit
1
and
2
under
Appendix
B.
Exhibit
2
also
shows
the
MAPE
to
calculate
the
error.
•
Total
Households
–
Our
forecast
for
this
metric
is
fairly
accurate
in
tracking
Historical
Data,
with
a
MAPE
of
1.99%.
However,
except
for
1998,
forecasted
Total
Households
is
consistently
below
the
actual
data.
This
is
most
likely
because
there
were
variable
jumps
in
the
number
of
real
total
households,
which
could
not
be
accurately
accounted
for,
due
to
the
fact
that
our
exponential
smoothing
model
weights
the
previous
year’s
forecast
as
heavily
as
the
actual
data.
Thus,
any
lag
in
the
forecast
would
permanently
influence
future
predictions.

6
•
Total
Food
Insecurity
–
Analysis
of
Total
Food
Insecurity
be
can
be
broken
up
into
“Pre
2007”
and
“Post
2007.”
• Pre
2007
–
The
exponential
smoothing
forecasts
had
a
low
forecast
error
because
they
normalized
the
variability
in
total
food
insecurity.
The
dip
from
1998
to
2000
is
offset
by
the
increase
in
food
insecurity
from
2000
to
2004.
Because
the
model
accounts
for
historical
data
at
an
exponentially
decaying
rate,
the
variability
over
time
will
be
smoothed
in
our
forecasted
graph.
• Post-­‐2007
–
The
massive
jump
in
Total
Food
Insecurity
likely
resulted
from
the
housing
market
collapse
and
subsequent
recession.
Our
forecast
model
didn’t
intersect
the
actual
data
from
2007
to
2013
due
to
our
use
of
a
0.5
alpha.
An
alpha
of
1
would
have
better
accounted
for
the
spike.
•
Low
Food
Security
and
Very
Low
Food
Security
–
These
graphs,
found
under
Exhibits
3
and
4
in
Appendix
B,
provide
a
very
similar
analysis
to
that
of
the
Total
Food
Insecurity
graph.
A
notable
difference
can
be
seen
in
the
Very
Low
Food
Security
Graph,
whose
forecast
lags
from
2000
to
2014.
This
lag
is
due
to
the
same
reason
cited
as
Total
Households;
Very
Low
Food
Security
has
been
steadily
increasing
for
years,
and
our
exponential
smoothing
model
has
lagged
as
it
continually
accounted
for
historical
data
at
an
exponentially
decreasing
rate.
Exponential
smoothing
limited
our
ability
to
forecast
into
the
future
to
only
one
year
ahead,
2016.
If
we
wanted
to
forecast
further
into
the
future,
we
would
have
to
use
a
regression
analysis.
3.4
Forecasting
Food
Insecurity
with
Regression
Analysis
We
used
regression
analysis
because
this
method
allows
for
forecasting
beyond
a
single
year,
unlike
Exponential
Smoothing.
Additionally,
regression
analysis
predicts
linear
trends
more
accurately
than
exponential
smoothing.
The
regression
model
used
the
same
data
as
exponential
smoothing
(data
which
can
be
found
in
Exhibit
1,
Appendix
B).
In
analyzing
the
regression
results,
P-­‐values
for
all
different
regressions
are
less
than
0.05,
which
indicates
significance.
We
thus
felt
comfortable
using
the
regression
analysis
to
forecast.
Additionally,
looking
at
the
R2
values:

7
•
The
high
R-­‐Square
value
of
98
percent
for
the
“Total
Households”
regression
indicates
that
the
regression
is
representative,
though
there
may
be
concerns
of
overfitting
data,
which
may
account
for
noise
that
could
impede
future
projections.
•
The
R-­‐Square
values
of
the
regressions
for
Total
Food
Insecurity,
Low
Food
Security,
and
Very
Low
Food
Security
ranged
between
64
percent
and
84
percent,
which
indicates
that
there
is
a
higher
amount
of
variability
in
the
actual
data
relative
to
that
of
our
regression.
(Raw
numbers
for
p-­‐values
and
R-­‐squared
are
shown
in
Exhibit
5,
Appendix
B)
3.5
Comparing
Forecasts
–
Exponential
Soothing
vs.
Regression
(Graphs
of
regression
analysis
can
be
found
in
Exhibit
6,
Appendix
B)
Exponential
smoothing
is
limited
in
how
far
into
the
future
we
can
forecast
data,
but
it
excels
at
its
ability
to
fit
actual
data
closely.
This
is
shown
by
the
differences
in
MAPE
for
the
comparative
models.
MAPE
for
the
regression
models
is
higher
for
nonlinear
trends
than
it
is
for
linear
trends.
Indeed,
the
only
linear
trend
that
we
found
was
for
the
regression
for
total
households.
The
MAPE
calculations
can
be
seen
in
Exhibit
7,
Appendix
B.
MAPE
for
total
households
is
much
lower
when
the
regression
model
is
used
than
when
the
exponential
smoothing
model
is.
For
total
households,
there
are
more
predictable
causal
reasons
for
a
linear
trend.
Ultimately,
it
is
the
least
squares
component
of
regression
that
does
a
better
job
of
accounting
for
causal
factors
of
change
in
the
number
of
total
households.
3.6
Note
on
Regression
Analysis
The
regression
model,
while
applicable
for
periods
in
which
there
is
historical
data
following
a
linear
trend,
has
future
forecasts
for
years
2016-­‐2020
that
are
likely
inaccurate
(forecasts
for
those
years
can
be
found
in
Exhibit
7,
Appendix
B).
This
is
due
to
the
fact
that
the
regression
model
only
looks
at
aggregate
numbers
and
doesn’t
account
for
causal
factors.
A
multivariable,
non-­‐linear
regression
model
would
have
been
a
more
appropriate
way
to
forecast,
but
we
didn’t
have
the
capability
to
do
that
for
this
analysis.
Now
that
we
have
analyzed
overall
food
insecurity
in
the
United
States,
we
can
address
the
issues
faced
by
our
one
of
Atlanta’s
own
food
pantries,
Toco
Hills
Community
Alliance.
4.
Part
II:
Quality
of
Inventory
at
a
Local
Food
Pantry
4.1
Background
on
Toco
Hills
Community
Alliance
A
food
pantry
is
defined
as
a
charitable
organization
that
provides
those
in
need
with
food
and
grocery
products
for
use
and
consumption
at
home.
The
food
pantry
we
analyzed,
Toco
Hills
Community
Alliance,
is
a
food
pantry
that
serves
DeKalb
County
and
several
of
the
zip
codes
in
the
surrounding
area.
According
to
its
website,
Toco
Hills
Community
Alliance’s
chief
goal
is
“to
provide
assistance
and
support
for
individuals
and
families…
who
face
the
possibility
of
the
loss
of
housing
and/or
who
are
without
sufficient
food
for
themselves
of
their
families”
(Toco
Hills
Community
Alliance
Website).
The
pantry
receives
a
wide
variety
of
food
donations
from
both
local
grocery
stores
and
individuals
in
the
community.
These
goods
are
then
organized
into
different
rooms,
based
on
the
type
of
food
item,
by
the
employees
at
the
food
pantry.
For
example,
one
room
consists
of
mainly
canned
goods
and
breads,
while
another
room
contains
mostly
snacks.

8
The
food
pantry
follows
a
specific
routine
when
serving
its
patrons.
Individuals
enter
the
building
that
houses
the
pantry
and
must
prove
that
they
qualify
for
assistance.
Next,
they
are
placed
on
a
waiting
list
and
provided
with
forms
to
complete.
One
by
one,
Toco
Hills
Community
Alliance
workers
guide
these
individuals
through
the
different
food
storage
rooms.
Qualifying
individuals
are
allowed
to
select
the
types
of
items
they
want,
but
only
workers
can
physically
collect
the
groceries.
At
the
end
of
the
shopping
period,
the
workers
weigh
the
selected
groceries
and
record
the
amount.
Following
our
initial
visit
to
the
Toco
Hills
Community
Alliance,
we
decided
to
focus
on
the
“quality”
of
the
inventory.
For
our
purposes,
a
poor
quality
food
item
is
one
that
is
past
its
expiration
date.
We
chose
this
aspect
for
analysis
because
the
pantry’s
primary
goal
is
providing
food
to
those
in
need,
and
thus
it
is
important
that
it
is
serving
quality
food
that
won’t
make
people
sick.
Since
Toco
Hills
Community
Alliance
does
not
collect
information
on
the
donations
they
receive,
we
had
to
use
a
heuristic
that
would
represent
the
quality
of
inventory.
We
ultimately
decided
on
the
expiration
date
heuristic.
By
collecting
expiration
date
data,
we
hoped
to
determine
whether
a
quality
issue
existed
and
to
give
a
possible
recommendation
to
address
this
problem,
if
this
turned
out
to
be
the
case.
4.2
Data
Collection
To
analyze
the
quality
of
the
inventory
and
tracking
system
at
the
Toco
Hills
Community
Alliance,
we
visited
the
food
pantry
to
collect
samples.
We
took
three
samples
from
each
of
the
food
bank’s
five
storage
rooms,
for
a
total
of
15
samples.
Each
sample
was
obtained
randomly
and
contained
a
mix
of
10
perishable
and
non-­‐perishable
items.
For
every
sample,
we
recorded
the
number
of
defective
(expired)
goods
found
amongst
the
ten
items
surveyed.
The
expiration
date
of
an
item
was
recorded
if
the
item
was
found
to
be
defective.
See
Exhibit
1
in
Appendix
A
for
the
raw
sample
data.
By
taking
an
average
of
the
15
samples,
we
found
that
34%
of
the
sample
goods
were
defective.
This
finding
indicates
that,
on
average,
3.4
out
of
every
10
goods
at
the
Toco
Hills
Community
Alliance
should
be
expired.
Converting
this
number
to
defective
goods
per
million,
we
can
expect
that
340,000
out
of
every
million
goods
donated
to
Toco
Hills
Community
Alliance
will
be
defective.
4.3
P-­‐Bar
Chart
Construction
and
Analysis
After
collecting
our
data
and
calculating
the
average
number
of
defective
goods
per
million
at
the
food
bank,
we
constructed
a
P-­‐Bar
Chart.
We
created
a
P-­‐Bar
Chart
because
it
can
be
an
efficient
tool
to
analyze
the
number
of
defective
goods
relative
to
the
UCL
and
LCL,
as
well
as
show
whether
a
process
is
out
of
control
or
not.
In
our
case,
we
wanted
to
see
the
variation
in
defective
goods
among
the
five
sample
rooms
and
determine
whether
any
specific
rooms
fell
significantly
outside
of
the
average.
To
begin
the
construction
of
the
P-­‐Bar
Chart,
we
used
P-­‐Bar,
previously
found
to
be
0.34,
and
the
parameters
of
three
sigmas,
to
calculate
the
Upper
Control
Limit
(UCL)
and
the
Lower
Control
Limit
(LCL)
of
the
data.
The
UCL
and
LCL
were
found
to
be
0.45603
and
0.22396,
respectively.
It
is
important
to
note
that
we
are
not
analyzing
a
machine
or
production
process;
rather,
in
our
case,
the
UCL
and
LCL
serve
as
lower
and
upper
bounds
to
assess
if
our
data
goes
beyond
these
numbers
when

9
analyzing
the
quality
of
the
inventory.
After
the
calculation
of
these
values,
we
were
then
able
to
construct
the
P-­‐Bar
Chart.
See
Exhibit
2,
Appendix
B
for
the
full
P-­‐Bar
Chart
calculations.
Looking
at
our
P-­‐Bar
Chart,
represented
below,
we
can
see
that
the
data
varies
widely
in
respect
to
P-­‐
Bar,
UCL,
and
LCL.
There
are
two
key
reasons
for
this
vast
amount
of
variation.
First,
each
sample
corresponds
to
a
particular
room,
and
some
rooms
contained
significantly
more
expired
goods
due
to
the
types
of
items
that
they
stored.
For
example,
Room
4
(samples
7,
8,
and
9)
stores
goods
that
have
a
relatively
short
shelf
life
like
bread.
In
comparison,
Room
3
(samples
4,
5,
and
6)
mostly
stores
items
with
extended
shelf-­‐lives
such
as
canned
soups.
Second,
it
was
not
uncommon
to
find
a
group
of
cans
several
years
expired
sitting
next
to
a
loaf
of
bread
that
was
set
to
expire
in
a
few
days,
when
we
conducted
our
survey.
These
two
factors
created
significant
variation
in
the
data.
While
the
data
fluctuates
significantly,
it
is
important
to
point
out
samples
that
fall
either
considerably
below
the
LCL
or
considerably
above
the
UCL.
One
sample
that
fell
significantly
below
the
LCL
was
sample
4,
which
had
no
defects.
Two
samples
that
significantly
exceeded
the
UCL
were
samples
12
and
15,
each
of
which
had
six
defects.
Such
outliers
may
be
due
to
random
sampling
chance,
given
the
fact
that
on
average,
about
3.4
out
of
every
10
goods
at
Toco
Hills
Community
are
expected
to
be
defective.
It
is
also
possible
that
these
values
are
partially
due
to
the
rooms
where
the
sample
was
taken,
as
discussed
earlier.
For
instance,
when
compared
with
the
other
two
samples
from
the
refrigeration
room,
samples
13
and
14,
sample
15
does
not
stand
out
as
an
outlier.
4.4
R-­‐Chart
Construction
and
Analysis
In
addition
to
making
a
P-­‐Bar
Chart,
we
also
created
an
R-­‐Chart.
We
decided
to
make
an
R-­‐Chart
because
we
wanted
to
analyze
the
range
of
the
defective
goods-­‐–how
long
the
goods
in
each
sample

10
had
been
expired,
relative
to
the
day
that
we
took
the
sample
(April
5,
2017).
Ideally,
the
range
would
be
more
accurate
if
we
had
information
on
when
the
item
was
donated
to
the
pantry;
after
all,
some
goods
may
have
already
been
expired
when
donated.
However,
since
Toco
Hills
did
not
collect
this
information,
we
decided
that
we
could
best
estimate
this
figure
by
comparing
expiration
dates
to
the
date
we
took
the
samples.
To
calculate
the
range
for
each
sample,
we
found
the
good
with
the
most
recent
expiration
date,
and
subtracted
it
from
the
good
with
the
oldest
expiration
date.
Next,
we
found
the
average
of
the
15
sample
ranges,
or
R-­‐Bar,
which
we
calculated
to
be
10.357
months.
As
with
the
P-­‐Bar
Chart,
we
found
the
UCL
and
LCL,
which
were
18.3457
months
and
2.2785
months,
respectively.
These
control
limits
were
determined
using
the
D4
and
D3
values
on
page
185
of
the
Bus351
Textbook.
Calculations
for
the
R-­‐Chart
can
be
seen
in
Exhibit
3
of
Appendix
A.
The
R-­‐Chart,
shown
below
for
the
Toco
Hills
Community
Alliance,
shows
data
that
appears
to
have
no
distinct
pattern,
except
a
few
samples
(samples
13,
14,
and
15).
Some
samples
had
a
range
of
0
months
(significantly
below
the
LCL),
which
would
indicate
that
all
of
the
defective
goods
in
the
sample
had
the
same
expiration
date.
Such
an
R
value
makes
sense
for
samples
13,
14,
and
15
because
these
samples
were
from
the
refrigeration
room,
where
items
are
likely
to
have
a
short-­‐term
shelf
life,
and
are
consequently
likely
to
have
expiration
dates
close
to
one
another.
Meanwhile,
some
samples
had
an
enormous
range,
such
as
samples
7
and
9,
which
were
significantly
above
the
UCL
and
had
ranges
of
42
and
41
months,
respectively.
The
significant
variation
among
R
values,
as
well
as
the
presence
of
some
incredibly
high
values
(R=41,
R=42),
indicates
that
the
food
bank
does
not
have
a
way
to
monitor
the
expiration
of
goods,
therefore
the
data
suggests
the
need
for
some
type
of
organizational
system
to
ensure
that
the
food
pantry
serves
customers
items
that
have
not
yet
expired.

11
4. Recommendation
Our
analysis
using
the
P-­‐Bar
Chart
and
R-­‐Chart
demonstrates
that
the
Toco
Hills
Community
Alliance
needs
an
organization
schedule
by
expiration
date.
To
address
this
issue,
we
suggest
that
the
pantry
implement
a
First-­‐In
First-­‐Out
(FIFO)
system
to
prevent
donated
items
from
reaching
their
expiration
date
while
in
storage.
Under
our
proposed
system,
goods
would
continue
to
be
organized
by
food
type,
but
they
would
also
be
arranged
by
expiration
date.
For
example,
if
a
bag
of
apples
is
donated,
the
item
would
not
only
be
placed
in
a
room
with
similar
items,
but
would
also
be
placed
near
items
which
had
a
similar
expiration
date.
Items
that
are
close
to
their
expiration
date
would
in
the
front
of
the
room,
while
items
that
have
a
longer
time
before
expiration
would
be
placed
towards
the
back
of
the
room.
This
layout
would
encourage
shoppers
to
choose
items
that
are
close
to
their
expiration
date
because
those
items
would
be
in
their
direct
line
of
sight
when
entering
the
room.
This
model
mimicks
how
grocery
stores
stock
their
shelves.
We
believe
the
total
percentage
of
expired
goods
at
the
Toco
Hills
Community
Alliance
would
decrease
under
this
proposal,
as
goods
that
are
close
to
expiration
will
exit
the
pantry
sooner.
6.
Future
Considerations
While
we
believe
that
our
recommendation
will
reduce
the
amount
of
expired
goods
at
the
Toco
Hills
Community
Alliance
at
a
given
time,
we
do
not
believe
that
the
inventory
quality
problem
can
be
completely
resolved
by
implementing
this
recommendation.
This
is
due
to
the
complicated
reasons
why
the
food
pantry
has
expired
goods
in
the
first
place.
For
example,
a
large
portion
of
the
pantry’s
food
donations
come
from
major
grocery
stores
in
the
surrounding
area.
These
stores,
however,
primarily
donate
items
that
are
either
close
to
their
expiration
date
or
are
already
past
it.
This
raises
the
issue
of
whether
Toco
Hills
Community
Alliance
and
other
similar
institutions
should
dispose
of
items
once
they
expire.
Such
a
policy
would
eliminate
the
pantry’s
food
quality
problem
–
goods
simply
would
not
remain
in
storage
past
their
expiration
date.
Many
might
find
this
solution
to
be
wasteful
and
impractical.
The
disposal
of
expired
items
might
be
a
net
negative,
as
it
would
reduce
the
amount
of
food
available.
Also,
some
opponents
of
the
disposal
method
might
argue
that
food
products
are
often
“good”
well
past
their
expiration
date,
and
that
eating
them
would
not
cause
serious
illness.
For
these
reasons,
we
ultimately
refrained
from
implementing
a
disposal
policy
for
expired
goods.
Before
reaching
our
current
recommendation,
we
considered
the
idea
of
implementing
a
spreadsheet
system
to
record
every
item
the
pantry
received
as
a
donation.
The
proposed
spreadsheet
would
record
an
item’s
date
of
arrival,
food
type,
storage
room,
location
in
the
storage
room,
expiration
date,
and
date
of
exit.
After
further
consideration,
however,
we
felt
that
this
suggestion
was
impractical
given
the
human
capital
available
of
the
Toco
Hills
Community
Alliance.
Indeed,
the
pantry
is
relatively
small
and
run
by
a
few
volunteers,
and
such
a
solution
might
prove
to
be
too
time-­‐consuming.
Thus,
instead
of
recommending
a
spreadsheet,
we
decided
to
recommend
a
new
organization
system,
a
relatively
simple
change
that
we
feel
is
better-­‐suited
to
the
Toco
Hills
Community
Alliance’s
current
capabilities.
However,
if
the
pantry
increases
in
size
or
gains
more
full-­‐
time
volunteers
we
suggest
that
the
pantry
consider
the
idea
of
a
spreadsheet
system.

12
Lastly,
it
is
important
to
mention
that
we
had
previously
planned
to
analyze
the
effect
that
President
Trump’s
proposed
budget
cuts
might
have
on
increasing
food
insecurity
in
the
United
States.
However,
after
research,
it
became
apparent
that
government
programs
such
as
the
Supplemental
Nutrition
Assistance
Program
(SNAP),
which
helps
millions
of
low-­‐income
individuals
in
the
US
afford
groceries,
would
likely
be
unaffected
by
these
broad
budget
cuts.

13
7.
Appendix
A
Exhibit
1
Exhibit
2
Exhibit
3

14
8.
Appendix
B
Exhibit
1
–
Actual
Historical
Data
(Household
Food
Security
in
the
United
States
in
2015)
Exhibit
2
–
Forecasting
Historical
Data
using
Exponential
Smoothing

15
Exhibit
3
Exhibit
4

16
Exhibit
5
Exhibit
6

17

18
Exhibit
7
Exhibit
8

19
9.
Works
Cited
Coleman-­‐Johnson,
Alisha,
Matthew
P.
Rabbitt,
Christian
A.
Gregory,
and
Anita
Singh.
Household
Food
Security
in
the
United
States
in
2015.
Rep.
United
States
Department
of
Agriculture,
Sept.
2016.
Web.
25
Mar.
2017.
Echevarria,
Samuel,
Robert
Santos,
Emily
Engelhard,
Elaine
Waxman,
and
Theresa
Del
Vecchio.
Food
Banks:
Hunger's
New
Staple.
Rep.
Feeding
America,
2011.
Web.
20
Mar.
2017.
"Our
Organization."
Toco
Hills
Community
Alliance.
Web.
25
Mar.
2017.
"Policy
Basics:
Introduction
to
the
Supplemental
Nutrition
Assistance
Program
(SNAP)."
Center
on
Budget
and
Policy
Priorities,
18
Aug.
2016.
Web.
1
Apr.
2017.
"Supplemental
Nutrition
Assistance
Program
(SNAP)."
Food
and
Nutrition
Service.
United
States
Department
of
Agriculture,
30
Jan.
2017.
Web.
25
Mar.
2017.
Wunderlich,
Gooloo
S.,
and
Janet
L.
Norwood.
"Chapter
3."
Food
Insecurity
and
Hunger
in
the
United
States:
An
Assessment
of
the
Measure.
Washington,
D.C.:
National
Academies,
2006.
41-­‐54.
Print.