1. Final
Project
Report
1
Jesture
MyGlass
Created
by:
Collin
Kraczkowsky,
Angela
Hu,
Tanusree
Munshi,
Anthony
Siao,
and
Jason
Sher
2. Final
Project
Report
2
Table
of
Contents
Executive
Summary
…………………………………………………………………………………………...Page
3
Contents
of
Report
……………………………………………………………………………………Pages
4
-‐
54
A. Introduction
………………………………………………………………………………………Pages
4
-‐
8
1) Project
Overview
………………………………………………………………………………
4
2) Description
of
Project
………………………………………………………………………..
4
3) Project
Plan
………………………………………………………………………………………..
5
4) Corporate
Benchmarking
…………………………………………………………………..
6
5) Roles
&
Responsibilities
…………………………………………………………………….
7
B. Developing
a
Supply
Chain
Strategy
……………………………………………….Pages
8
-‐
13
1) Competitive
Strategy
………………………………………………………………………….
8
2) Product
Development
Strategy
………………………………………………………….
9
3) Software
Development
Strategy
………………………………………………………..
9
4) Supply
Chain
Network
Strategy
…………………………………………………………
11
C. Demand
Forecasting
……………………………………………………………………..Pages
13
-‐
19
D. Inventory
Management
…………………………………………………………………Pages
19
-‐
21
1) Fixed
Costs
………………………………………………………………………………………….
19
2) Cost
per
Unit
……………………………………………………………………………………….
19
3) Inventory
Holding
Cost
………………………………………………………………………
20
4) Cycle
Inventory
…………………………………………………………………………………..
20
5) Aggregation
Strategy
………………………………………………………………………….
20
6) Economic
Order
Quantity
…………………………………………………………………..
21
E. Distribution
Network
……………………………………………………………………Pages
21
-‐
35
1) Designing
the
Facilities
Network
……………………………………………………….
21
2) Designing
the
Transportation
Network
…………………………………………….
31
F. Supply
Chain
Driver
Alignment
…………………………………………………….Pages
36
-‐
40
G. SCM
Software
Implementation
……………………………………………………..Pages
40
-‐
46
1) Software
Implementation
…………………………………………………………………..
40
2) User
Manual
………………………………………………………………………………………..
46
Conclusion
…………………………………………………………………………………....................Pages
47
-‐
54
3. Final
Project
Report
3
Executive
Summary
Creating MyGlass our consumers will be able to look up recipes, watch the news, follow
tutorials, and much more on surfaces they would normally overlook. MyGlass utilizes a central
receiver system and a high-tech display system to allow consumers to interact with a glass
surface that can be put on top of a mirror, counter top, or even a television.
Drawing inspiration primarily from tablets, we hope to create a design that allows
consumers to still perform all of their daily tasks with the surface while still being able to utilize
the basic functions of a computer. As a result, our team has been working on a product that we
hope homeowners will see as a tool to bridge the gap between almost every appliance in their
home and their computer.
Our
product
myGlass
has
developed
to
the
point
where
consumers
are
able
to
perform
all
of
their
daily
tasks
with
the
surface.
We
integrated
as
much
technical
features
make
our
customer’s
lives
easier.
Now
the
goal
of
this
project
is
to
implement
an
applicable
SCM
software
information
system
to
simulate
our
integrated
enterprise
supply
chain.
We
want
to
provide
all
the
technology
that
a
standard
home
needs.
We
want
to
develop
our
products
step
by
step
since
we
are
pushing
at
a
wider
market
at
a
low
cost.
In
order
for
our
company
to
be
successful,
we
must
develop
as
much
as
we
can.
We
want
to
provide
as
much
technological
features
to
our
products
so
it
can
be
very
beneficial
to
homeowners.
Throughout
this
quarter,
we
have
improved
and
developed
our
business
plan
from
the
previous
quarter
by
performing
a
full-‐scale
design
process
to
create
the
supply
chain
network
for
our
company’s
flagship
product,
myGlass.
This
process
began
with
high-‐level
strategy
conception,
then
moved
into
detailed
planning
out
the
scope
of
our
supply
chain,
before
ending
with
the
daily
operations
of
each
component.
This
intensive
framework
ensured
that
each
decision
aligned
with
the
decisions
made
during
the
preceding
phase
and
ultimately
created
harmony
between
our
strategy
and
our
execution.
At
the
planning
level,
we
utilized
tools
such
as
network
optimization
models
and
Microsoft
Excel
to
perform
forecasting/time-‐series
analysis
as
well
as
uncertainty
analysis.
At
the
operational
phase
we
defined
our
needs
at
each
stage
along
the
supply
chain
such
as
inventory,
transportation,
and
facilities
developing
an
integrated
software
in
Visual
Basic
to
simulate
the
impact
of
various
scenarios
to
test
the
robustness
of
our
design.
Our
distribution
strategy
is
to
ship
out
as
many
orders
as
possible
on
the
same
shipment.
If
the
customers
are
within
the
same
area,
their
orders
would
be
shipped
at
the
same
time
to
reduce
costs
for
our
company.
Since
we
do
not
have
any
retailers
that
our
distributors
would
ship
to,
we
do
not
have
to
have
an
advanced
aggregation
strategy,
but
rather,
we
have
to
strategically
ship
orders
at
the
same
time
that
would
be
going
to
the
same
place.
4. Final
Project
Report
4
The
strength
of
our
distribution
network
strategy
is
also
in
the
financial
benefits
we
exploit
from
centralizing
inventory,
aggregating
demand,
and
simplifying
product
handling.
This
report
will
go
into
much
more
detail
about
the
strategies
just
discussed,
and
we
hope
to
have
your
continued
support
as
we
begin
to
work
on
prototyping
and
our
product
release
strategy
execution.
Contents
of
Report
A. Introduction
1) Project
Overview
In
Fall
Quarter
2014,
our
technology
firm,
Jesture,
developed
a
Business
Plan
for
our
product
-‐
myGlass.
myGlass
is
a
large,
all-‐purpose
surface
tablet
that
can
be
overlaid
on
to
any
plane
(i.e.
kitchen
counters,
shower
ledges,
car
hoods,
etc.)
to
become
a
computing
element
for
that
surface.
Given
the
portability
and
flexible
user
interface,
Jesture
initially
plans
to
target
the
homeowner
market
with
myGlass
before
eventually
expanding
into
enterprises
through
additional
professional
features.
During
Winter
Quarter
2015,
our
team
will
build
on
our
work
from
the
previous
quarter
by
designing
and
developing
the
value
and
supply
chain
network
for
myGlass.
Our
development
plan
is
3-‐tier
beginning
with
the
high-‐level
strategy
for
myGlass’
supply
chain
network,
moving
then
into
the
planning
phase
where
we
will
leverage
Excel
to
perform
forecasting/time-‐series
analysis
as
well
as
uncertainty
analysis,
and
finally
we
will
move
into
the
operations
phase
where
we
will
define
our
needs
at
each
stage
along
the
supply
chain
(i.e.
inventory,
transportation,
and
facilities).
By
performing
each
step
of
this
plan,
we
will
be
able
to
develop
and
implement
an
applicable
SCM
software
information
system
to
simulate
our
integrated
enterprise
supply
chain.
Through
this
project,
our
team
intends
to
meet
all
the
objectives
of
the
Management
of
Technology
II
course
as
well
as
engage
the
resolutions
we
established
at
the
beginning
of
this
class.
2) Description
of
Product
Figure
A.1
below
illustrates
the
Function
Analysis
System
Technique
diagram
for
our
product,
myGlass:
5. Final
Project
Report
5
Figure
A.1:
FAST
Diagram
for
myGlass
The
FAST
diagram
is
a
technique
for
analyzing
the
functional
structure
of
a
technical
system.
It
serves
as
a
useful
starting
point
from
which
to
introduce
the
functions
of
our
product
and
therefore
define
our
target
customer
base.
This
customer
base
is
essential
to
the
design
of
our
entire
supply
chain
network.
3) Time-‐phased
Project
Plan
We
organized
the
timeline
of
deliverables
for
our
project
into
the
follow
Table
A.1
below:
Table
A.1:
Time-‐Phased
Project
Plan
Task
Due
Date
Form
Project
Teams
&
Choose
Technology
Domain
In
class
on
January
6,
2015
Formulate
Project
Proposal
January
8,
2015
6. Final
Project
Report
6
Phase
1:
Technology/Product
Strategy
&
Supply
Chain
Strategy/Design
January
20,
2015
Phase
2:
Supply
Chain
Modeling
+
Planning
via
Demand
Forecasting
February
3,
2015
Phase
3:
Supply
Chain
Operations
-‐
Inventory,
Transportation,
&
Facilities
February
24,
2015
Phase
4:
The
Software
Information
System
for
the
Supply
Chain
via
Simulation
March
10,
2015
Phase
5:
Closure
&
Final
Report
March
12,
2015
4) Benchmark
Our
Plan
Against
an
Established
Company
When
designing
our
supply
chain
management
approach
and
implementation,
we
internalized
two
key
lesson
takeaways
from
Kai
Hypko,
former
Senior
Director
of
Supply
Chain
Systems
and
Strategy
at
Plantronics.
The
first
is
that
supply
chain
management
efforts
deliver
the
greatest
results
when
SCM
is
part
of
an
overall
business
strategy
–
not
a
stand
alone
effort.
We
interpreted
this
takeaway
as
the
need
to
align
Jesture’s
competitive
strategy
with
its
supply
chain
strategy
to
optimize
and
maintain
our
placement
in
the
zone
of
strategic
fit.
We
don’t
want
Jesture’s
individual
strategies
to
be
mismatched
with
the
overarching
business
strategy.
The
second
takeaway
is
that
supply
chain
management
is
most
likely
to
under
deliver
when
there
is
poor
connection
between
functions
across
a
total
business
–
often
noted
by
poor
supply
chain
visibility
and
lack
of
best
practice
sharing
internally.
To
us
this
meant
collaboration
both
upstream
and
downstream
between
each
stage
and
within
each
cycle
of
our
supply
chain.
It
also
highlights
the
need
for
sophisticated
information
infrastructure.
We
can
turn
these
strategic
takeaways
into
operational
components
by
implementing
what
Hypko
defines
as
qualities
of
world
class
supply
chain
companies.
First,
Jesture
must
make
effecting
internal
collaboration
to
optimize
processing
a
core
competency
of
our
company.
Second,
we
need
to
identify
key
suppliers
and
customers,
prioritize
and
organize
their
needs,
and
work
closely
with
them
to
best
match
their
demands.
Third,
we
must
effectively
apply
technology
as
an
enabler.
This
means
using
our
demand
forecasting
modules,
inventory
management
modules,
and
integrated
information
systems
to
enable
us
to
best
meet
customer
demand.
Plantronics
broke
down
their
supply
chain
implementation
into
a
timeline
which
we
have
organized
into
Table
A.2
below.
Jesture
will
use
this
time
phased
process
to
benchmark
the
progress
of
our
own
supply
chain
management
approach
and
implementation.
7. Final
Project
Report
7
Table
A.2:
Plantronics’
SCM
Implementation
Benchmarks
Year
1
Year
2
Year
3
Year
Demand
Planning
Sales
&
Operations
Planning
Global
Order
Promising
Promotions
Management
and
Optimization
Supplier
Collaboration
Inventory
Optimization
Customer
Collaboration
Advanced
Planning
and
Scheduling
(Part
1)
Advanced
Planning
and
Scheduling
(Part
2)
Transportation
Planning
Supply
Chain
Business
Intelligence
Production
Scheduling
We
can
use
this
planning
structure
to
gauge
the
progress
of
Jesture’s
supply
chain
maturity
by
benchmarking
it
against
the
maturity
of
another
company
in
the
technology
space.
5) Roles
&
Responsibilities
of
Team
Members
Jason
Sher-‐
Jason
has
taken
TIM
105
in
the
Fall
of
2013
and
is
now
excited
to
join
the
Jesture
team
and
the
product
myGlass.
This
opportunity
has
given
the
team
a
different
perspective
on
how
management
of
technology
is
performed
and
also
gives
different
experiences
due
to
him
working
with
other
groups
and
seeing
other
ideas
executed.
This
project
will
be
difficult
because
the
Jesture
team
must
integrate
two
new
members
into
their
team,
but
Jason
is
confident
that
the
team
will
ultimately
be
able
to
successfully
consolidate
any
differences
and
act
as
a
functioning
unit.
Tanusree
Munshi
-‐
Tanusree
was
a
part
of
the
original
Jesture
group
in
TIM
105
in
Fall
2014.
Because
of
this,
Tanusree
has
more
insight
on
the
product
and
can
clarify
the
details
of
both
our
product
and
company
to
our
newer
members.
Tanusree
is
good
at
communicating
what
needs
to
be
completed
and
organizing
tasks;
because
of
these
strengths
she
believes
she
will
be
able
to
help
the
team
work
more
efficiently.
Tanusree
thinks
she
will
focus
on
any
part
that
needs
to
be
completed.
Anthony
Siao
-‐
Anthony
is
one
of
the
original
members
from
TIM
105,
he
can
contribute
to
explaining
our
product
to
the
new
members
of
the
progress
we
have
made.
As
Anthony
being
an
Environmental
Studies
major
and
minor
in
TIM,
he
can
bring
different
ideas
to
the
table.
The
course
being
focused
on
technology,
he
can
bring
other
ideas
of
what
can
help
the
environment.
Whatever
tasks
he
is
assigned
to,
he
will
finish
it.
Angela
Hu
-‐
As
a
member
of
the
group
in
the
previous
course
(TIM105),
Angela
can
provide
help
to
the
newer
team
members
by
letting
them
know
about
our
product
and
8. Final
Project
Report
8
what
we’ve
done
so
far.
Being
more
interested
in
the
business
aspect
of
this
class,
Angela
can
provide
ideas
and
do
research
about
similar
products.
Also,
Angela
can
provide
help
with
organizing
the
different
phases
that
we
are
assigned.
Colin
Kraczkowsky
-‐
Being
one
of
the
newest
members,
Colin
can
serve
as
a
fresh
eye
to
the
Jesture
team
offering
affirmations
to
what
was
done
well
and
suggestions
to
areas
that
may
have
been
overlooked.
As
a
Business
Management
and
Economics
major,
Colin
brings
a
background
in
supply
chain
analysis
and
management
to
the
table
as
well
as
a
basic
knowledge
of
Excel
to
be
leveraged
for
spreadsheet
build
outs,
visual
basics,
and
various
analyses.
Colin
foresees
playing
his
greatest
role
in
the
strategy
phase
and
demand
forecasting
phase.
Last
quarter,
in
TIM
105,
our
group
worked
very
efficiently.
We
all
communicated
well
with
each
other
and
met
up
at
least
once
a
week
to
discuss
our
progress
and
what
tasks
were
remaining.
A
major
challenge
for
our
group
was
that
we
were
left
with
only
four
group
members
while
most
other
groups
had
six
or
seven.
This
was
difficult
for
us
in
the
beginning
because
individually,
we
had
to
complete
more
work
to
get
everything
done
on
time.
In
the
end
though,
it
wasn’t
so
bad
having
only
four
team
members
because
it
allowed
us
to
communicate
and
work
more
efficiently
since
there
were
less
people
to
coordinate
with.
This
quarter,
we
decided
to
split
up
the
tasks
according
to
our
strengths
and
weaknesses
so
everyone
would
be
comfortable
doing
their
assigned
role.
How
our
group
can
improve
the
quality
of
our
work
is
to
keep
on
completing
our
tasks
efficiently.
Also
we
want
to
build
a
good
working
system
with
our
new
partners,
since
we
lost
three
members
of
our
group
in
TIM
105.
We
want
to
continue
on
having
good
communication
with
one
another
and
always
having
assigned
tasks.
Since
we
have
more
group
members
now,
we
have
to
be
more
flexible
with
our
schedules
and
efficient
with
assigning
each
other
tasks.
B. Developing
a
Supply
Chain
Strategy
In
this
phase,
our
team
continued
building
our
strategies
for
the
launch
and
production
of
myGlass
including
developing
our
competitive
strategy
and
supply
chain
strategy
along
with
compiling
estimates
of
demand
for
our
product.
1) Competitive
(Marketing)
Strategy
For
our
marketing
strategy,
we
decided
to
partner
up
with
retail
locations
like
Home
Depot
and
provide
them
incentives
for
our
product
to
be
displayed
at
their
stores.
The
primary
market
for
our
products
will
be
consumers,
specifically
homeowners,
while
the
secondary
market
will
be
enterprises.
Jesture
will
be
a
leader
in
innovation,
quality,
and
performance.
We
will
have
a
differentiated
strategy
approach
with
unique
products
that
will
reach
a
large
portion
of
the
market,
enabling
us
to
have
substantial
market
opportunity
with
a
wide
range
of
buyers.
The
price
of
our
products
may
start
out
high
in
order
to
cover
startup
and
developmental
costs
associated
with
the
creation
of
our
unique
products,
but
will
shortly
become
affordable
to
the
every-‐day
consumer.
This
will
allow
us
to
engage
with
most
of
the
9. Final
Project
Report
9
prospective
market
and
to
dominate,
as
well
as
lead,
the
unique
new
market
we
have
created.
Figure
B.1:
2x2
Competitive
Strategy
Matrix
for
myGlass
As
indicated
in
Figure
B.1
above,
our
competitive
strategy
for
myGlass
is
a
Focused/Niche
competitive
strategy.
Since
our
product
is
a
breakthrough
product
and
there
isn’t
much
of
a
market
for
products
like
it
yet,
we
need
to
have
a
focused
strategy
and
target
a
particular
segment
of
the
market,
such
as
upper-‐middle
class
homeowners.
Above
is
the
matrix
that
shows
our
position
in
the
competitive
strategy
matrix.
Our
product
is
very
unique
so
we
need
to
focus
on
a
particular
segment
of
the
market.
If
we
begin
by
targeting
everyone,
our
product
will
not
be
successful.
2) Product
Development
Strategy
Our
developmental
goals
for
our
product
seek
to
satisfy
our
users’
needs.
We
are
going
to
make
our
products
simple
and
yet
complex.
We
want
to
provide
all
the
technology
that
a
standard
home
needs.
We
want
to
develop
myGlass
step-‐by-‐step
since
we
are
selling
to
a
wider
market.
In
order
for
our
company
to
be
successful,
we
must
develop
as
much
as
we
can.
We
want
to
provide
as
much
technological
features
to
our
products
so
it
can
be
very
beneficial
to
homeowners.
Once
we
fully
develop
our
products,
we
are
going
to
introduce
more
technology
that
can
be
added
to
bigger
markets.
Our
main
goal
for
our
products
is
to
provide
as
much
key
features
that
will
benefit
standard
homes
and
people’s
everyday
lives.
3) Software
Development
Strategy
Our
high
level
plan
for
the
software
development
of
myGlass
will
be
critical
for
the
success
of
our
supply
chain
and
our
myGlass
product.
Our
plan
will
need
to
incorporate
information
systems
that
make
key
and
valuable
information:
10. Final
Project
Report
10
1. Easy
to
access,
easy
to
manipulate.
2. Readily
available
for
all
members
that
need
access.
3. Information
must
be
correct
and
reliable.
Our
IT
system
will
involve
a
cloud-‐based
information
system
that
will
communicate
key
information
regarding
supplier,
customer
and
many
other
types
of
data.
This
data
will
be
communicated
throughout
the
supply
chain
and
help
guide
the
development
of
the
software
component
for
our
myGlass
product.
Figure
B.2
below
maps
out
the
flow
of
information
into
our
centralized
database
system:
Figure
B.2:
Company
Information
Infrastructure
Map
Our
system
will
have
open
communication
between
our
departments
to
be
able
to
deliver
to
our
customers
a
built-‐to-‐order
product
in
a
timely
and
cost
effective
manner.
Our
system
will
utilize
the
cloud
to
communicate
data
throughout
the
every
department
so
they
can
understand
the
changes
in
customers
needs,
prices,
changes
in
demand
and
many
other
important
aspect
of
the
supply
chain
and
our
business.
This
system
will
be
a
source
of
important
data
from
departments
that
are
not
necessarily
connected
directly,
such
as
manufacturing
and
retail.
The
customers
data
retail
gets
from
their
interaction
with
customers
can
be
communicated
to
manufacturing
through
the
Cloud-‐based
IT
system.
11. Final
Project
Report
11
This
will
help
manufacturing
to
be
more
efficient
on
how
much
supply
to
buy
from
suppliers
and
keep
costs
for
the
system
relatively
low.
The
communication
between
our
departments
directly,
along
with
the
shared
information
communicated
on
the
IT
system
will
be
very
beneficial
in
keeping
our
customers
satisfied
and
also
keeping
the
systems
profit
level
at
a
sustainable
rate.
4) Supply
Chain
Network
Strategy
Our
goal
is
to
align
each
of
these
strategies
above
with
a
supple
chain
strategy
that
provides
both
the
level
of
responsiveness
demanded
by
our
customers
as
well
as
means
of
efficiency
to
lower
our
overhead
costs
and
achieve
a
high
surplus
across
our
entire
supply
chain.
In
our
supply
chain
network,
Jesture
is
its
own
manufacturer
and
distributor.
We
don’t
have
any
other
companies
which
we
rely
on
to
be
our
distributors
and
retailers,
but
we
do
rely
on
companies
such
as
The
Home
Depot,
Brookstone,
Wal-‐Mart,
and
Bed
Bath
&
Beyond
for
marketing
purposes.
Home
improvement
and
large
retail
chains
would
showcase
our
product
to
the
general
public
where
Jesture
would
position
a
representative
that
would
be
talking
to
our
potential
customers
as
an
interactive
advertisement
for
myGlass.
The
customer-‐order
cycle
begins
when
an
interested
consumer
files
their
order
directly
to
Jesture.
We
would
then
enter
the
manufacturing
cycle
as
Jesture’s
plants
begin
to
custom
build
the
demanded
myGlass
according
to
the
customer’s
specified
measurements.
By
reducing
the
chain
of
distributors
and
retailers,
Jesture
is
trying
to
create
a
more
personalized
product,
with
results
similar
to
those
produced
by
Dell’s
supply
chain.
By
doing
this,
we
are
maximizing
the
responsiveness
of
our
company.
To
achieve
this
optimal
supply
chain
network,
we
had
to
build
a
strategy
that
achieved
a
strategic
fit
between
the
implied
demand
uncertainty
facing
myGlass
and
the
optimal
trade-‐off
between
responsiveness
and
efficiency
to
both
serve
the
needs
of
our
customers
as
well
as
maximize
supply
chain
surplus.
Figure
B.3
below
shows
the
spectrum
of
the
combined
uncertainty
of
supply
and
demand
referred
to
as
implied
demand
uncertainty
which
is
the
uncertainty
imposed
on
the
supply
chain
due
to
the
customer
needs
we
seek
to
satisfy.
We
have
determined
that
myGlass
will
experience
a
high
level
of
implied
demand
uncertainty
because
our
product
is
less
mature
and
is
entering
a
relatively
uninhabited
market
of
the
technology
space
meaning
that
our
sourcing
drivers
are
unstable
and
more
difficult
to
predict.
We
also
experience
higher
implied
demand
uncertainty
because
our
distribution
network
strategy
requires
that
the
number
channels
through
which
myGlass
is
acquired
increases
with
the
various
retail
marketing
locations
we
offer
as
customer
pickup
locations.
This
increased
Jesture’s
IDU
because
the
total
customer
demand
is
now
disaggregated
over
more
channels.
Figure
3.3:
Implied
Demand
Uncertainty
Spectrum
for
myGlass
12. Final
Project
Report
12
In
terms
of
responsiveness
and
efficiency,
our
goal
is
to
target
a
higher
placement
on
the
responsiveness
frontier
that
is
consistent
with
our
implied
demand
uncertainty.
As
discussed
earlier,
since
myGlass
is
custom-‐ordered
on
a
pull-‐based
process,
our
customers
implicitly
value
responsiveness.
We
achieve
responsiveness
by:
• Offering
our
customers
the
ability
to
use
our
product
at
our
retail
marketing
locations
before
making
their
purchase.
• Allowing
the
customer
to
fully
customize
their
myGlass
to
meet
their
exact
needs.
• Supporting
multiple
regional
manufacturing
locations
to
facilitate
short
lead
times
during
our
replenishment
cycles.
• Source
our
components
from
multiple
suppliers
to
better
handle
fluctuations
in
supply
uncertainty.
We
understand
that
higher
responsiveness
comes
at
higher
costs,
however
maintaining
our
position
in
the
zone
of
strategic
fit
is
crucial
to
aligning
our
competitive
strategy
with
our
supply
chains
strategy
and
ultimately,
as
Kai
Hypko
noted
in
his
200
presentation
on
Platronics,
leading
to
a
world
class
supply
chain
structure.
Figure
B.4
below
illustrates
Jesture’s
positioning
on
the
Efficiency
versus
Responsiveness
spectrum.
Figure
B.4:
Efficiency
versus
Responsiveness
Spectrum
for
myGlass
Combining
both
the
Implied
Demand
Uncertainty
Spectrum
and
the
Responsiveness
versus
Efficiency
Spectrum,
we
obtain
a
graph
that
depicts
the
zone
of
strategic
fit,
drawn
in
Figure
3.5
below,
where
Jesture
achieves
alignment
of
our
strategies.
Figure
3.5:
Zone
of
Strategic
Fit
Graph
13. Final
Project
Report
13
Achieving
and
maintaining
Jesture’s
optimal
space
within
the
zone
of
strategic
fit,
starred
in
Figure
3.5,
requires
that
we
achieve
the
balance
between
responsiveness
and
efficiency
that
best
supports
our
Focused
Niche
competitive
strategy.
We
can
shift
our
position
on
the
efficiency
versus
responsiveness
spectrum,
as
illustrated
Figure
3.4,
through
changing
and
adapting
our
logistical
and
cross-‐functional
drivers:
facilities,
inventory,
transportation,
information,
sourcing,
and
pricing.
C. Forecasting
Demand
for
myGlass
In
this
phase,
our
team
sought
to
prepare
a
demand
forecast
for
our
myGlass
to
help
us
get
an
idea
of
production
and
sales
volume
that
we
will
face
period
over
period
and
so
we
can
move
towards
the
next
phase
of
inventory
management.
We
broke
our
approach
into
phases:
first
aggregating
demand
estimates
from
like-‐products
to
simulate
our
historical
demand
data;
then
organizing
this
data
into
an
Excel
format
to
run
it
through
various
forecasting
models;
and
finally
drawing
conclusions
from
the
results
of
these
models
to
determine
the
most
accurate
representation
of
the
forecasted
demand
for
myGlass.
i. Look
up
demand
for
tablet
market
from
previous
years
to
use
as
a
basis
for
myGlass
demand
estimates.
According
to
Gartner,
Inc.,
the
demand
data
for
the
tablet
market
from
2011
-‐
2013
can
be
broken
down
as
follows:
2011
2012
2013
60,017,000
units
118,883,000
units
182,457,000
units
This
data
is
important
for
two
reasons.
First,
it
gives
our
team
an
idea
of
what
global
demand
for
similar
products
looks
like.
Second,
the
data
shows
that
the
demand
for
tablets
is
growing
and
has
been
increasing
by
an
average
of
76%
year
over
year
since
2011.
This
proves
that
the
market
is
lucrative
and
the
growing
demand
will
support
the
introduction
of
myGlass.
ii. Aggregate
annual
tablet
demand
data
and
source
quarterly
demand
data
from
competitor
products
mentioned
in
our
Business
Plan:
Apple
iPad
&
Samsung
Galaxy
Tab.
To
figure
out
demand
estimates
for
myGlass,
we
decided
to
extract
quarterly
demand
data
for
two
prominent
products
within
the
tablet
industry.
We
chose
Apple’s
iPad
and
Samsung’s
Galaxy
Tab
because
they
are
market
leading
products
with
readily
available
demand
data.
14. Final
Project
Report
14
Year
Quarter
Apple
iPad
Demand
(millions
of
units)
Samsung
Tablet
Demand
(millions
of
units)
2011
Q1
7.33
.08
Q2
4.69
.27
Q3
9.25
.3
Q4
11.12
.35
2012
Q1
15.43
.16
Q2
11.8
.04
Q3
17.04
.9
Q4
14.04
7.5
2013
Q1
22.86
8.5
Q2
19.48
6.9
Q3
14.62
8.4
Q4
14.08
13.6
Using
data
from
Appleinsider,
we
were
able
to
source
the
quarterly
units
sold
of
these
two
substitute
products
from
three
prior
demand
cycles.
iii. Estimate
prior
demand
data
using
metrics
from
theoretic
substitute
products.
Year
Quarter
Apple
iPad
Demand
(millions
of
units)
Samsung
Tablet
Demand
(millions
of
units)
Estimated
Past
Demand
(millions
of
units)
2011
Q1
7.33
0.08
3.705
Q2
4.69
0.27
2.48
Q3
9.25
0.3
4.775
Q4
11.12
0.35
5.735
2012
Q1
15.43
0.16
7.795
Q2
11.8
0.04
5.92
15. Final
Project
Report
15
Q3
17.04
0.9
8.97
Q4
14.04
7.5
10.77
2013
Q1
22.86
8.5
15.68
Q2
19.48
6.9
13.19
Q3
14.62
8.4
11.51
Q4
14.08
13.6
13.84
(Demand
estimated
using
the
averages
of
the
demand
for
these
two
products)
We
used
the
actual
sales
data
of
both
the
Apple
iPad
and
the
Samsung
Galaxy
Tab
to
make
demand
predictions
for
myGlass.
This
data
comes
from
both
of
these
products
as
they
entered
the
introductory
phase
of
the
product
lifecycle.
This
simulates
the
kind
of
demand
our
product
would
face
as
it
enters
the
product
lifecycle
giving
us
an
accurate
sense
of
production
needs.
iv. Translate
this
data
to
our
financial
software.
We
perform
this
step
to
synthesize
the
demand
data
with
our
production
schedule
and
to
accurately
factor
our
production
and
sales
volume
into
the
projections
for
the
Net
Present
Value
of
the
myGlass
project.
Using
these
demand
estimates
we
expect
to
see
positive
cash
flows
within
two
years
and
a
positive
Net
Present
Value
after
six
years.
v. Begin
forecasting
demand
using
the
Static
Forecasting
method
by
first
calculating
the
deseasonalized
demand
for
the
estimated
demand
data
of
myGlass.
Using
demand
estimates
we
acquired
in
step
iii.,we
then
sought
to
remove
variations
in
the
data
that
are
caused
by
seasonal
fluctuations.
We
performed
this
step
by
looking
for
the
periodicity
of
demand
in
the
tablet
market.
Looking
at
the
data,
the
general
trend
appeared
to
be
that
demand
was
relatively
low
during
the
second
quarter
of
each
year
and
then
would
increase
through
the
next
three
periods
before
dipping
again
in
the
second
quarter.
This
gave
us
a
periodicity
of
four
meaning
that
we
observed
roughly
four
periods
between
the
start
and
the
end
of
a
seasonal
cycle.
Combined
with
this
periodicity
estimate,
we
were
then
able
to
calculate
the
deseasonalized
demand
using
the
formula
for
calculating
deseasonalized
demand
with
an
even
periodicity.
vi. Run
a
regression
through
the
deseasonalized
demand
data
to
locate
the
trend
and
level
for
the
demand
for
myGlass.
Above
is
a
graphical
representation
of
both
our
Estimated
Demand
and
Deseasonalized
Demand
for
the
tablet
market.
Removing
predictable
seasonal
fluctuations
from
our
demand
estimates
allowed
us
to
run
a
regression
analysis
and
draw
a
trendline
through
the
deseasonalized
data.
We
needed
to
deseasonalize
our
Estimated
Demand
in
order
to
run
a
16. Final
Project
Report
16
linear
regression
because,
as
indicated
graphically
by
the
blue
diamonds,
the
original
demand
estimates
do
not
exhibit
a
linear
relationship.
The
regression
line
shown
gives
us
a
visual
representation
of
the
relationship
between
the
independent
variable,
number
of
periods,
and
the
dependent
variable,
demand
for
homeware
tablets,
to
estimate
the
conditional
expectation
of
demand
from
the
second
quarter
of
Year
2
to
the
second
quarter
of
Year
6.
The
equation
of
this
regression
line
is
as
follows:
Deseasonalized
Demand
=
1,372.2
+
930.25
*
a
given
Period
This
tells
us
that
the
level
of
demand
at
Period
0
is
1,372,000
units
(Level
=
1,372.2)
and
that
the
rate
of
growth
of
demand
from
one
period
to
the
next
is
930,250
(Trend
=
930.25).
vii. Using
the
trend
and
level,
calculate
the
deseasonalized
demand
for
all
the
past
estimated
demand
data
of
myGlass.
Equation:
Deseasonalized
Demand
=
1,372.2
+
930.25
*
Estimated
Past
Demand
Using
the
above
equation
allowed
us
to
get
accurate
estimates
for
deseasonalized
demand
for
all
periods
since
the
equation
for
deseasonalized
demand
has
restrictions.
We
will
need
the
demand
data
for
every
period
to
calculate
the
seasonal
factors
that
would
have
affected
our
demand
estimates
in
the
past
and
will
likely
affect
our
forecasted
demand
after
we
begin
producing
myGlass.
viii. Use
the
estimated
demand
and
the
deseasonalized
demand
to
calculate
seasonal
factors.
Equation:
Seasonal
Factors
=
Estimated
Demand
Data
/
Deseasonalized
Demand
Data
The
above
equation
will
produce
the
predictable
seasonal
factors
that
caused
fluctuations
in
our
demand
estimates.
We
need
to
calculate
these
factors
so
that
we
can
reseasonalize
the
demand
data
to
get
accurate
demand
forecasts.
ix. Use
the
seasonal
cycles
of
the
demand
periodicity
to
predict
the
seasonal
factors
for
future
demand.
The
equation
for
calculating
seasonal
factors
is
restricted
by
requiring
both
observed
(in
our
case
estimated)
and
deseasonalized
demand
data
which
is
only
available
after
the
period
has
ended
and
the
data
can
be
aggregated.
To
estimate
the
seasonal
factors
that
will
affect
our
future
demand,
we
average
out
the
calculated
seasonal
factors
for
periods
in
the
same
position
within
the
seasonal
cycle
(i.e.
Periods
6,
10,
and
14
all
exhibit
low
points
in
demand
so
we
would
take
the
average
of
these
three
periods
to
produce
an
estimated
seasonal
factor
for
Period
18).
We
need
the
seasonal
factors
for
the
periods
we
are
17. Final
Project
Report
17
forecasting
demand
because
we
will
need
to
reseasonalize
our
deseasonalized
demand
forecasts
during
that
time.
x. Use
the
trend,
level,
and
seasonal
factors
to
forecast
future
demand
for
my
glass.
Equation:
Forecasted
Demand
=
(Level
+
Period
*
Trend)
*
Seasonal
Factor
The
equation
for
forecasting
demand
has
two
components:
forecasted
deseasonalized
demand
for
a
given
future
period
and
the
corresponding
seasonal
factor.
Recalling
the
equation
we
extracted
from
the
graph
in
step
v.
we
know
that
the
future
deseasonalized
demand
will
fall
somewhere
along
the
line:
Deseasonalized
Demand
=
1,372.2
+
930.25
*
a
given
Period.
Factoring
in
the
periods
we
are
forecasting
demand
for,
we
then
multiply
by
our
estimated
seasonal
factors
to
get
measurements
for
our
demand
forecasts
that
will
accurately
take
into
account
seasonal
fluctuations.
Using
static
demand
forecasting,
we
have
estimated
that
demand
for
our
tablet
homeware
technology
will
have
reached
500
thousand
by
the
time
we
are
on
the
market
in
2016.
xi. Check
your
work
by
plotting
estimated
demand
data
and
forecasted
demand
data
on
the
same
graph.
Plotting
our
observed
demand
against
our
demand
forecasts
provides
a
visual
representation
to
see
if
the
Static
Forecasting
method
is
appropriate
for
our
demand
data.
While
the
forecasts
under
this
method
do
exhibit
a
similar
level
and
seasonal
pattern
as
the
observed
data,
the
static
method
appears
to
continue
growing
past
Year
6
while
we
have
estimated
that
our
product
will
have
fully
matured
by
then
and
actually
have
entered
the
Decline
phase
of
the
product
lifecycle.
This
is
because
the
static
method
assumes
that
level,
seasonality,
and
trend
are
all
static
meaning
that
the
trend
will
increase
at
a
constant
growth
rate.
Because
of
the
assumptions
under
the
Static
Forecasting
method,
we
have
decided
to
pursue
another
forecasting
method
that
will
take
into
consideration
the
full
scope
of
myGlass
as
it
passes
through
the
product
lifecycle.
xii. Begin
forecasting
demand
using
Winter’s
method
by
first
getting
initial
values
for
both
Level
and
Trend
using
the
deseasonalized
demand
data
from
the
Statis
Forecasting
method.
We
decided
to
pursue
Winter’s
adaptive
forecasting
method
because
it’s
adaptive
qualities
smooth
the
changes
in
Level,
Trend,
and
Seasonality
that
we
have
observed
in
our
demand
data.
These
changes
occur
as
myGlass
transitions
through
the
stages
of
the
product
lifecycle.
Because
Winter’s
model
of
adaptive
demand
forecasting
adjusts
for
seasonality,
we
use
the
deseasonalized
demand
data
to
get
initial
estimates
for
the
Level
and
the
Trend.
As
can
be
18. Final
Project
Report
18
seen
in
the
chart
above,
we
will
use
an
Initial
Estimate
of
Level
=
1372.2
and
an
Initial
Estimate
of
Trend
=
930.25
as
the
basis
for
our
forecasts.
xiii. Use
the
initial
estimates
of
Level
and
Trend
to
make
estimates
for
future
levels
of
Level
and
Trend
and
to
get
Predicted
Seasonal
Factors.
xiv.
Having
built
out
the
forecasted
estimates
for
Level,
Trend,
and
Seasonal
Factors,
forecast
demand
for
Periods
1
-‐
7.
xv. xv.
Perform
an
error
analysis
for
the
forecasted
demand
data.
The
constants
we
chose
were
as
follows:
a
Level
smoothing
constant
of
0.9,
a
Trend
smoothing
constant
of
0.9,
and
a
Seasonal
Factor
smoothing
constant
of
0.1.
We
chose
these
constants
because
they
provided
us
the
most
accurate
forecast
numbers
as
determined
by
the
most
minimal
errors.
As
can
be
seen
by
our
error
analysis
above,
our
Forecasted
Demand
estimates
start
off
shakey
with
Mean
Absolute
Percentage
Errors
higher
than
we
would
like
but
that
decrease
with
time
meaning
that
our
method
will
become
more
accurate
over
time.
An
encouraging
sign
is
the
Tracking
Signal
consistently
ranging
between
0
and
1
meaning
that
our
estimates
aren’t
overly
biased
in
either
direction.
xvi. Check
your
work
by
plotting
estimated
demand
data
and
forecasted
demand
data
on
the
same
graph.
Plotting
the
Forecasted
Demand
data
we
obtained
using
Winter’s
adaptive
forecasting
method
against
the
Observed
Demand
data
we
estimated
using
the
historical
demand
data
of
a
similar
branch
of
products
serves
us
in
two
ways.
First,
we
are
able
to
see
how
closely
the
Forecasted
Demand
points
relate
to
the
Observed
Demand
points
providing
confidence
that
we
performed
the
time-‐series
forecasting
correctly
and
that
it
provided
accurate
results.
Second,
we
are
able
to
observe
what
the
demand
of
future
periods
will
look
like
for
which
we
do
not
have
historical
demand
data.
What
we
see
is
a
similar
pattern
of
seasonal
fluctuations
as
was
seen
in
past
periods
however
we
also
observe
a
slight
dip
in
demand.
The
dip
in
demand
comes
between
our
fourth
and
fifth
year
of
operation
and
is
to
be
expected
as
fur
product
will
have
entered
the
Decline
Phase
of
the
product
lifecycle
similarly
to
what
we
saw
in
the
lifecycle
of
other
tablet
computers.
xvii. Synthesize
the
Forecasted
Demand
from
the
Winter’s
model
of
adaptive
forecasting
with
our
financial
estimates.
Our
final
step
is
to
take
the
Forecasted
Demand
data
we
obtained
in
step
xiv
and
implant
it
into
our
financial
estimates.
To
maintain
accurate
estimates
of
future
demand,
we
decided
to
forecast
up
until
Year
7.
Given
that
forecasting
further
than
historical
demand
data
under
Winter’s
method
means
multiplying
the
Trend
by
an
increasing
constant,
our
Forecasted
Demand
estimates
would
appear
to
continue
growing
which
would
be
inaccurate.
Because
there
was
not
enough
historical
data
to
substantiate
forecasting
further,
we
decided
to
hold
until
new
demand
would
come
in
and
provide
the
best
forecasts
for
Year
8
and
beyond.
19. Final
Project
Report
19
Consistent
with
our
observation
that
myGlass
will
have
entered
the
Decline
Phase
of
the
product
lifecycle
by
Year
7,
our
financial
software
shows
that
the
cash
flows
and
net
present
values
will
also
begin
to
decline.
Our
company,
Jesture,
will
then
have
to
decide
actions
to
take
next.
We
may
consider
increasing
our
investment
in
Marketing
&
Support
to
keep
myGlass
relevant,
generating
a
line
of
complementary
products
to
increase
demand
for
myGlass,
or
investigating
a
new
product
line
and
let
myGlass
run
the
full
length
of
the
lifecycle.
D. Inventory
Management
in
Jesture’s
Supply
Chain
1) Estimating
Fixed
Shipping
Charges
We
plan
to
ship
by
truck
if
the
customer
is
located
locally
within
the
Bay
Area
and
by
freight
or
air
if
the
customer
is
further
away
from
the
central
manufacturing
location
in
San
Jose.
If
the
customer
is
within
California
or
within
the
bordering
states,
we
would
ship
them
the
product
by
train,
but
if
the
customer
is
further
away
in
the
Mid-‐West
or
East
Coast,
we
would
ship
the
product
through
airplane.
-‐ Estimated
shipping
through
truck
(within
Bay
Area):
$50
-‐
$100
per
shipment
(for
one
unit
of
product)
-‐ Estimated
shipping
through
train
(within
California
or
bordering
states):
$100
per
shipment
(for
one
unit
of
product)
-‐ Estimated
shipping
through
air:
$135
per
shipment
(for
one
unit
of
product)
2) Estimating
the
Cost
per
Unit
We
plan
on
charging
our
customers
$500
for
the
initial
(2x2)
system,
then
$25
extra
per
square
foot.
The
cost
for
our
company
for
producing
the
product
is
about
$200
per
unit,
depending
on
the
size
of
the
order.
$200
would
be
the
initial
cost
for
producing
the
system,
including
the
receiver
and
central
processing
unit.
The
additional
costs
will
come
from
producing
the
glass
itself,
which
varies
depending
on
the
size
of
the
customer’s
order.
Our
main
product
that
we
will
be
focusing
on
is
a
2
foot
by
2
foot
square
of
myGlass.
This
product
will
include
one
receiver/CPU
and
a
2
by
2
piece
of
myGlass
that
can
be
used
on
the
go.
#
of
CPUs
needed
to
fulfill
EOQ
=
454*1=
454
#
of
Bose
speakers
to
fulfill
EOQ
=
454*2
=
908
#
of
myGlass
pieces
of
technology
to
fulfill
EOQ
=
454*4
=
1816
#
of
Processor’s
needed
to
fulfill
EOQ=
454*1
=
454
3) Annual
Inventory
Holding
Cost
Annual
Holding
Cost
is
given
by
this
formula:
Order
Quantity
*
Holding
cost
per
unit
per
year
20. Final
Project
Report
20
For
our
product,
the
order
quantity
(shown
below)
is
454
units
and
the
holding
cost
per
unit
is
given
by
(0.2
*
200)
=
$40.
With
these
numbers,
we
can
fill
in
the
formula
above:
4542
*
$40
=
$9,080
per
year
4) Estimating
Cycle
Inventory
Economic
Order
Quantity
is
given
by
the
following
equation:
2(Annual
Demand)(Shipping
Cost)(Holding
Cost)(Unit
Cost)
Our
annual
projected
demand
for
year
7
is
55,022
units
of
our
product.
Assuming
we
ship
by
truck
and
the
unit
cost
is
around
$200,
and
the
holding
percentage
is
20%
we
get
the
following
equation:
2(55,022)($75)(0.2)($200)
From
this
equation,
we
calculate
our
EOQ
to
be
about
454
units
per
shipment.
5) Determine
Aggregation
Strategy
Our
aggregate
strategy
would
be
to
try
and
ship
out
as
many
orders
as
possible
on
the
same
shipment.
If
the
customers
are
within
the
same
area,
their
orders
would
be
shipped
at
the
same
time
to
reduce
costs
for
our
company.
Since
we
do
not
have
any
retailers
that
our
distributors
would
ship
to,
we
do
not
have
to
have
an
advanced
aggregation
strategy,
but
rather,
we
have
to
strategically
ship
orders
at
the
same
time
that
would
be
going
to
the
same
place.
Table
D.1:
Demand
Data
for
Year
7
of
Production
Year/Quarter
Forecasted
Demand
Year
7/Quarter
1
14,749
Year
7/Quarter
2
14,234
Year
7/Quarter
3
13,055
Year
7/Quarter
4
12,984
Total:
55,022
Using
our
FAST
diagram
we
created
during
the
design
phase
of
our
product
we
can
have
an
idea
of
how
many
suppliers
we
need
and
how
we
can
begin
to
aggregate
our
orders
for
our
supplies.
As
shown
above
one
myGlass
unit
would
be
composed
of
an
Intel
Pentium
21. Final
Project
Report
21
microprocessor,
Bose
internal
speakers,
one
fingerprint
recognition
software
system,
an
Android
OS
and
one
Intel
730
SSD
card.
6) Determine
the
Economic
Order
Quantity
of
Supplies
As
mentioned
in
our
inventory
managment
for
myGlass
we
are
expecting
to
have
an
EOQ
of
454
units
of
myGlass
per
shipment.
To
fulfill
this
order
our
SC
manager
should
be
calculating
how
many
units
of
each
component
our
company
should
order
to
add
to
our
inventory
that
will
minimize
total
cost.
We
have
labeled
our
assumptions
for
how
many
units
of
each
component
would
need
to
be
ordres
to
build
the
454
units
needed
for
our
EOQ:
Table
D.2:
Economic
Order
Quantities
of
our
Components
Components
Order
Quantity
1
CPU
2
Internal
Bose
speakers
2x2
square
feet
of
myGlass
glass
technology
1
microprocessor
#
of
CPUs
needed
to
fulfill
EOQ
=
454*1=
454
#
of
Bose
speakers
to
fulfill
EOQ
=
454*2
=
908
#
of
myGlass
technology
to
fulfill
EOQ=
454*4
=
1816
#
of
Processor’s
needed
to
fulfill
EOQ=
454*1
=
454
E. Designing/Implementing
our
Supply
Chain
Drivers
Network
1) Designing
Jesture’s
Facilities
Network
While
making
decisions
regarding
Jesture’s
facilities
driver
in
our
supply
chain
network
design,
we
looked
at
the
role
of
each
facility,
the
location
of
manufacturing,
our
storage
needs,
and
the
allocation
of
capacity
and
markets
to
each
facility.
We
classified
these
down
into
high-‐level
planning
decisions
as
follows:
1. Facility
Role:
What
role
will
each
facility
play?
What
processes
are
performed
at
each
facility?
2. Facility
Location:
Where
will
we
locate
our
facilities?
3. Capacity
Allocation:
How
much
capacity
will
we
allocate
to
each
facility?
4. Market
and
Supply
Allocation:
What
markets
will
each
facility
serve?
Which
supply
sources
will
feed
into
each
facility?
To
determine
the
specifics
for
each
of
these
planning
decisions,
we
broke
our
process
down
into
a
phase-‐based
framework
from
which
we
could
execute
a
plan.
22. Final
Project
Report
22
Phase
I:
Define
a
Supply
Chain
Strategy/Design
Following
the
strategy
to
planning
to
operation
procedural
outline,
our
first
step
was
to
define
Jesture’s
high-‐level
supply
chain
design
that
maintains
alignment
between
our
competitive
and
supply
chain
strategies.
Having
identified
our
competitive
strategy
as
focusing
on
a
niche
market
of
tech-‐savvy
consumers
interested
in
the
integration
of
technology
into
homewares
who
are
active
buyers
in
the
Internet
of
Things
marketspace,
we
were
then
faced
with
the
challenge
of
specifying
what
capabilities
our
supply
chain
network
must
have
to
support
that
strategy.
Phase
II:
Define
the
Regional
Facility
Configuration
The
objective
of
the
next
phase
is
to
plan
out
and
identify
regions
where
our
facilities
will
be
located,
what
roles
they
will
serve,
and
how
much
capacity
will
be
allocated
to
each
facility.
We
first
revisit
Jesture’s
overall
supply
chain
network
as
illustrated
in
Figure
B-‐2
below
to
identify
the
layout
and
roles
of
our
facilities.
Figure
E.1:
Jesture’s
Supply
Chain
Network
For
myGlass,
Jesture
serves
the
primary
role
of
manufacturer.
Upstream
on
our
supply
chain,
we
have
outsourced
our
raw
material
and
components
suppliers
as
we
do
not
have
the
talent
nor
the
marginal
funds
to
develop
those
core
competencies
in-‐house.
Downstream
on
our
supply
chain,
we
have
our
retailer
marketing
outlets
which
we
have
also
outsourced
to
push
us
towards
the
efficiency
frontier.
These
locations
are
inside
established
retailers
who
have
a
historically
large
customer
base
that
includes
our
target
consumers.
Thus,
23. Final
Project
Report
23
Jesture’s
facilities
driver
will
comprise
of
manufacturing
plants
that
serve
the
role
as
production
facilities
of
myGlass,
transportation
docks
to
receive
parts
and
components
and
distribute
finished
products
to
our
retail
marketing
locations,
and,
lastly,
as
customer
pickup
locations.
Having
looked
at
the
role
of
our
facilities,
we
next
planned
out
the
general
area
we
want
to
locate
them
in.
To
do
this
we
broke
down
our
U.S.
market
into
three
supply
regions:
the
Pacific
Region,
the
Central
Region,
and
the
Atlantic
Region.
The
options
on
the
table
during
this
phase
are
to
determine
whether
we
needed
large
consolidated
facilities
or
smaller,
localized
facilities.
One
determining
factor
is
that
if
economies
of
scope
or
sale
are
not
significant
it
may
be
better
for
each
market
to
have
its
own
facility.
As
we
know
economies
of
scope
refer
to
the
ability
of
a
business
to
share
centralized
functions
improving
cost
efficiency
by
using
the
same
input
for
multiple
outputs.
Economies
of
scale,
on
the
other
hand,
are
the
cost
advantages
that
enterprises
obtain
due
to
scalability
of
their
operation
as
cost
per
output
generally
decreases
with
quantity
of
output
as
fixed
costs
are
spread
out
over
more
units.
Economies
of
scope
are
thus
efficiencies
wrought
by
variety
while
economies
of
scale
are
efficiencies
wrought
by
volume.
Our
economies
of
scope
are
minimal
since
we
serve
the
primary
role
of
manufacturer
that
outsources
most
of
our
secondary
functions
and
our
product
line
is
small
so,
as
of
now,
our
inputs
generate
just
a
single
output.
However
our
economies
of
scope
are
scaleable
as
we
forecast
growing
demand
for
our
product
over
time.
We
thus
initiated
this
process
with
the
plan
to
have
one
consolidated
manufacturing
plant
in
each
of
our
demand
regions.
Having
a
general
idea
of
our
regional
facility
configuration
in
mind,
our
next
step
was
to
build
a
network
design
model
that
would
help
us
determine
if
our
location
plan
was
economically
efficient
and
assign
the
corresponding
capacity
limits
to
our
plants.
We
also
needed
the
model
to
assign
demand
to
each
facility
and
identify
lanes
along
which
myGlass
would
be
transported.
We
chose
a
capacitated
network
optimization
model
as
a
launching
point
to
help
us
make
these
decisions.
To
create
this
model
we
first
needed
to
collect
and
organize
our
data
in
a
form
that
can
be
used
for
a
quantitative
mode.
The
data
collected
are
shown
in
Figure
E.2.
Figure
E.2:
Cost
and
Demand
Data
for
myGlass
24. Final
Project
Report
24
We
started
with
dividing
total
demand
amongst
our
demand
regions.
Using
our
demand
forecast
data,
we
anticipate
that
in
Year
7
of
production
we
will
see
a
mean
annual
demand
of
13,106,370
units
of
myGlass.
Taking
this
demand
figure,
we
distributed
it
to
each
supply
region,
assuming
that
the
West
Coast
and
East
Coast
would
exhibit
higher
levels
of
demand
due
to
the
density
of
their
populations
and
higher
buying
trends
of
tech
products.
We
then
developed
a
two-‐tier
capacity
plan
where
we
have
the
option
of
building
a
low-‐
capacity
plant
able
to
produce
3
million
units
per
year
or
a
high-‐capacity
plant
able
to
produce
6
million
units
per
year.
To
determine
the
fixed
cost
of
these
plants,
we
performed
some
outside
research
gathering
sales
data
on
available
factory
property
throughout
the
United
States.
These
listings
were
priced
according
to
size,
in
square
feet,
so
we
estimated
that
a
low-‐capacity
plant
for
myGlass
production
would
be
25,000
square
feet,
or
half
the
size
of
a
football
field,
and
a
high-‐capacity
plant
would
be
50,000
square
feet,
or
roughly
the
size
of
an
average
American
football
field.
Using
these
parameters
and
the
location
of
the
listing,
we
then
determined
the
fixed
cost
inputs
as
seen
in
Figure
B-‐2.
Our
final
step
was
to
calculate
the
inputs
that
would
hold
the
cost
of
producing
and
shipping
one
unit
of
myGlass
from
a
supply
region
to
a
corresponding
demand
region.
We
started
with
our
base
unit
production
cost
of
$400
per
unit
that
we
identified
in
the
financial
analysis
performed
in
the
TIM
105
course.
This
cost
would
be
standard
no
matter
which
region
we
chose
to
manufacture
in.
We
then
had
to
determine
the
transportation
costs
associated
with
getting
that
unit
from
its
supply
region
of
origin
to
its
relative
demand
region.
Referring
to
our
transportation
driver,
we
had
planned
to
use
LTL
shipping
to
move
finished
goods
from
our
manufacturing
plants
to
the
customer
pickup
location.
Performing
some
outside
research,
we
pulled
trucking
rates
for
each
region
to
estimate
the
dollar
per
mile
it
would
cost
per
unit
as
well
as
the
length
and
width
of
the
United
States
to
estimate
the
distance
that
unit
would
need
to
travel.
Having
calculated
our
inputs
we
then
needed
to
build
the
matrices
to
hold
our
decision
variables
and
constraints
that
would
correspond
with
our
objective
function.
Our
end
objective
function
measures
the
total
fixed
cost
+
the
variable
cost
of
setting
up
and
operating
Jesture’s
network.
Within
the
decision
variables
table,
the
grouping
of
cells
on
the
left
holds
the
quantity
that
would
be
shipped
from
a
plant
to
a
demand
region
within
our
network.
The
grouping
of
cells
on
the
right
holds
the
binary
value
of
whether
the
plant
would
actually
be
open
with
a
“1”
signifying
that
particular
plant
would
be
operational
and
a
“0”
signifying
that
particular
plant
would
not
be
operational.
The
constraints
table
holds
the
values
that
restricts
the
objective
function.
For
example,
the
capacity
of
one
of
our
plants,
subject
to
its
being
operational
as
determined
by
the
corresponding
decision
variable,
must
either
exceed
or
equal
the
total
annual
demand
coming
in
from
the
demand
regions
it
is
supplying.
Having
translated
these
pseudocoded
instructions
into
a
working
Excel
format,
we
could
then
leverage
Excel’s
Solver
tool
to
minimize
the
objective
function
subject
to
the
constraint
variables
and
produce
the
most
efficient
possible
design
for
our
network.
The
results
of
building
these
decision
and
constraint
matrices,
calculating
our
objective
function,
and
running
the
optimization
on
our
objective
function
are
illustrated
in
Figure
E.3
below:
25. Final
Project
Report
25
Figure
E.3:
Capacity
&
Cost
Allocation
of
Facilities
Recall
that
the
capacitated
plant
location
model
focuses
on
minimizing
the
cost
of
meeting
the
demand
facing
the
network
using
it.
What
this
snapshot
is
thus
telling
us
is
that
Jesture
should
seek
to
build
and
operate
three
facilities,
one
in
each
of
its
supply
regions.
Both
the
Western
Plant
and
the
Eastern
plant
will
be
high-‐capacity
facilities
capable
of
producing
six
million
units
of
myGlass
per
year.
While,
the
Central
Plant
will
be
a
low-‐
capacity
facility
capable
of
producing
three
million
units
of
myGlass
per
year.
Furthermore,
each
of
our
production
facilities
will
serve
the
market
within
the
region
it
is
located
i.e.
the
Western
Plant
will
supply
the
demand
from
the
West
Coast,
the
Central
Plant
will
supply
the
demand
from
Midwest,
and
Eastern
Plant
will
supply
the
demand
from
the
East
Coast.
These
network
decisions
are
organized
and
displayed
in
Table
E.1
below:
Table
E.1:
Role,
Location,
&
Capacity
of
Jesture’s
Facilities
Will
the
region
have
a
facility?
What
is
the
role
of
the
facility?
Who
will
the
facility
serve?
How
big
is
the
facility?
Western
Supply
Region
Yes
Manufacturing
Plant
Western
Demand
Region
6,000,000
units
per
year
Central
Supply
Yes
Manufacturing
Plant
Central
Demand
Region
3,000,000
units
per
year
26. Final
Project
Report
26
Region
Eastern
Supply
Region
Yes
Manufacturing
Plant
Eastern
Demand
Region
6,000,000
units
per
year
Fixed
and
variable
costs
considered,
this
optimized
network
will
cost
Jesture
$32.52
million
to
operate
as
determined
by
minimizing
the
objective
function.
Phase
III:
Select
a
Set
of
Desirable
Potential
Sites
Having
built
and
run
a
model
that
provides
a
minimized
cost
of
our
facilities
network
subject
to
the
decisions
of
which
of
our
supply
regions
contain
manufacturing
facilities
and
the
corresponding
capacities
of
these
facilities,
our
company
moved
to
the
next
phase
of
identifying
potential
locations
in
each
region
that
will
contain
a
Jesture
manufacturing
plant.
To
do
this,
we
leveraged
the
capabilities
of
the
gravity
location
model
which
useful
when
identifying
suitable
geographic
locations
within
a
region
that
minimize
the
cost
of
transporting
raw
materials
from
suppliers
as
well
as
finished
goods
to
the
markets
served.
Our
first
step
is
to
identify
or
calculate
the
required
inputs
for
the
gravity
location
model.
The
results
from
this
first
step
are
shown
in
Figure
E.4
below:
Figure
E.4:
Source
&
Market
Data
Inputs
We
determined
that
our
key
supply
source
would
be
Corning
Inc.
who
would
serve
as
the
main
supplier
of
our
manufacturing
demand
for
myGlass’
glass
component.
Given
that
glass
is
the
most
variable
component
in
our
production
line,
erecting
myGlass
production
plants
within
a
cost-‐effective
distance
from
our
key
supplier
would
optimize
our
overall
supply
27. Final
Project
Report
27
network
strategy.
Corning
Inc.
maintains
U.S.
production
facilities
in
Harrodsburg,
Kentucky,
Canton,
New
York,
and
Christiansburg,
Virginia,
cities
whose
latitudinal
and
longitudinal
coordinates
we
input
into
our
gravity
location
model
as
seen
in
Figure
E.4.
We
then
identified
and
found
the
corresponding
latitudes
and
longitudes
for
our
biggest
markets
within
each
of
the
demand
regions
that
our
three
production
facilities
would
be
serving.
To
determine
the
unit
shipping
cost
per
unit,
we
referred
back
to
our
dollar
per
mile
trucking
costs
from
performing
the
capacitated
plant
location
model
in
Phase
II
of
designing
Jesture’s
facilities
network.
We
also
referred
back
to
this
phase
to
pull
the
quantity
that
would
be
shipped
to
our
production
facilities
inbound
from
our
suppliers
to
meet
our
demand
as
well
as
outbound
to
the
regional
markets
to
meet
their
demand.
Performing
this
step
establishes
base
parameters
from
which
to
build
out
the
rest
of
the
model.
With
an
established
base,
our
next
step
was
to
create
a
column
of
distance
variables
that
would
hold
the
distance
between
our
production
facilities
at
the
optimal
location
and
the
corresponding
supply
sources
and
markets.
This
is
calculated
as
the
root
of
the
squared
sum
of
the
distances
between
the
coordinates
we
provided
for
the
supply
source
or
the
market
and
the
coordinates
of
the
location
we
have
chosen
for
our
facility.
We
then
sought
to
calculate
the
resulting
cost
of
the
transportation
amongst
these
facilities
which
is
the
sum
of
the
distances
between
the
facility
and
supply
source/market
multiplied
by
the
quantity
to
be
shipped
between
that
facility
and
supply
source/market
multiplied
by
the
cost
of
shipping
one
unit
for
one
mile
between
the
facility
and
supply
source/market
-‐
each
factor
having
been
previously
entered
in
our
base
parameters.
This
total
cost
is
then
populated
in
a
variable
holding
the
total
cost
of
each
scenario.
Building
this
into
a
readable
Excel
format,
we
can
than
utilize
Excel’s
Solver
tool
to
minimize
this
total
cost
cell
by
changing
the
cells
holding
the
variables
that
correspond
with
the
coordinates
of
the
facility.
Our
results
from
performing
this
procedure
are
illustrated
by
market
in
Figures
E.5,
E.6,
and
E.7
below:
28. Final
Project
Report
28
Figure
B.5:
Jesture’s
Eastern
Plant
Location
By
minimizing
the
cell
containing
the
total
cost
of
transportation
by
changing
the
coordinates
of
the
facility
location,
we
have
successfully
used
the
gravity
location
model
to
obtain
an
optimal
location
for
our
manufacturing
plant
for
the
Eastern
markets.
The
coordinates
of
this
plant
were
a
latitude
of
39.64
degrees
North
and
a
longitude
of
-‐78.34
degrees
West.
Locating
this
point
through
a
geographic
positioning
tool,
we
have
identified
that
Jesture’s
manufacturing
facility
for
the
East
Coast
that
will
serve
our
Eastern
markets
will
be
located
in
Pittsburgh,
Pennsylvania.
The
minimized
transportation
cost
of
this
decision
will
be
$184,650.31.
Figure
E.6:
Jesture’s
Central
Plant
Location
29. Final
Project
Report
29
By
minimizing
the
cell
containing
the
total
cost
of
transportation
by
changing
the
coordinates
of
the
facility
location,
we
have
successfully
used
the
gravity
location
model
to
obtain
an
optimal
location
for
our
manufacturing
plant
for
the
Central
markets.
The
coordinates
of
this
plant
were
a
latitude
of
38.07
degrees
North
and
a
longitude
of
-‐81.81
degrees
West.
Locating
this
point
through
a
geographic
positioning
tool,
we
have
identified
that
Jesture’s
manufacturing
facility
for
the
Midwest
that
will
serve
our
Central
markets
will
be
located
in
Charleston,
WV.
The
minimized
transportation
cost
of
this
decision
will
be
$201,114.01.
Figure
E.7:
Jesture’s
Western
Plant
Location
30. Final
Project
Report
30
By
minimizing
the
cell
containing
the
total
cost
of
transportation
by
changing
the
coordinates
of
the
facility
location,
we
have
successfully
used
the
gravity
location
model
to
obtain
an
optimal
location
for
our
manufacturing
plant
for
the
Western
markets.
The
coordinates
of
this
plant
were
a
latitude
of
37.76
degrees
North
and
a
longitude
of
-‐84.85
degrees
West.
Locating
this
point
through
a
geographic
positioning
tool,
we
have
identified
that
Jesture’s
manufacturing
facility
for
the
West
Coast
that
will
serve
our
Western
markets
will
be
located
in
Harrodsburg,
Kentucky.
The
minimized
transportation
cost
of
this
decision
will
be
$503,919.82.
Phase
IV:
Location
Choices
Having
determine
the
optimal
geographic
locations
of
each
facility
and
the
resulting
costs
to
transport
product
from
our
supply
source
to
each
facility
and
from
each
facility
to
its
corresponding
market,
we
can
now
add
to
Table
E.1
that
we
drew
in
Phase
II
to
organize
and
display
this
new
information.
The
results
are
shown
in
Table
E.2
below:
