Welcome everyone and thank you so
much for being here.

Appreciate introduction.

My name is John Miller and
I'm representing along with the other

presenters are you and

me and

so our project is about and
I'd just like to give a shout out to you

hearing and

getting started with the project goal

is to reduce cost in there in
the Transportation Department they're

facing is that less than two percent of
their shipments are currently a part of

trying to identify every shipment we
could possibly combine and therefore save

them money so that the way to approach
this problem is through energy programs.

We have hundred different groups
going into that later but groups.

We've been able to identify
almost fifteen thousand will

generate over nine billion dollars So

this is a graphic that the United States
and there are in transportation.

It's very expensive

to distribute Now

hundred vendors actually from the vendors

and so.

The problems they're facing
in the current state.

We have two different vendors
shipping shipments to the same

two different routes.

So we're trying to combine the shipment

to the origin of shipment and hearing
that second shipment to the same route.

Now this is a great way

to start saving the money but it doesn't
quite fully maximize the potential.

So we've applied this same logic to it so

very similarly coming from
vendors going to the same and

we're trying to figure out each shipment.

So how

are we getting this uprising
started with individual shipments.

We've created and.

Shipments are created

and the data

we have over two hundred twenty thousand
shipments from August twenty eighth.

Well number of different.

The first and most important
is the shipment example here.

Boston Massachusetts.

When a truck driver gets over
there in Los Angeles California

because they meet with their vendors and
so

for this shipment here on average
about three hundred dollars to make

it back to

the bridge this is very different
from from where is it actually.

Thank you.

Three

very.

By ship.

I'm James which was tough to go
in excise and the value one and

zero is now and
some are logical as for exhale.

It should be scale and can you can buy
it gives becomes one of the why's and

so that's what it did yes and S.K.Y.
and you can check the function of

the savings that are generated
through these two stocks restocking So

we check to see if the savings is positive
from from combining shipments and

if the goal is to maximize the total
savings overall process just so there's

some additional consideration you take
into account here don't work the process

so constrained one into their preferred
to time constraints for a truck driver.

So it can only be on a road for

the next eleven hours on a certain day so
that includes the time it takes for

them to each of us need to go pick them up
to have them back to the R.T.C. as well.

Brakes they may take throughout the day
and so that's constrained eleven hours

are most important is greatest
is number three there.

So what that fixed to
be only used one time.

So every shipment can be hard to
stop thinking part of the restock

stuff they go and we also take
the sequencing into production.

It can be the first up and we're going to
the second stuff in the freezer and so

on and so this is strange sure is that.

We don't use a shipment for
a model to stop and

free stuff it goes to make another example
for you here unless you can see three

shipments from the Pennsylvania area that
were originally think of individually So

three separate trucks one fifth of the
shipments and lower than that the R.T.C.

of Dallas Texas has a total distance of
total cost associated with model found

into a restavec So
now it's sufficient for one for.

To go out and you can see the distance.

What used to be.

And there's a much lower total So there's
a saying that we generated from this.

So from there we took you
stock in free stuff browse and

found vendor groups that we
created which is the desire

that is there in each group these
vendors shit together frequently.

So we look at the stuff if we can see
which vendors are associated with them and

so if throughout the course of
the year vendor ship together

by actions from these vendors together it
makes sense to put them in a store and

that's an example of start five
vendors and they were there.

And so confusing.

This is if they see shipments from vendors
in specific group shipping in certain

opportunities to provide shipments
into the stuff it goes and

save significant money and

overseas calls for
a graph of the minimum and

maximum reduction in overall shipments
as a result of our optimization model

excluding those shipments are already
maxed out by weight and body constraints.

We were able to take approximately seven
thousand three hundred shipments teach

R.D.C. and
reduce that sent this reduction which can

be as can be attributed to the formation
of two and three stop because so

on average we created six hundred
twenty two stop the cops and

two hundred nine three stop pick ups for

each of our eighteen
parties in the same graph.

We see the minimum and
maximum savings as a result of our

stop formations on
average are three hundred

fifty thousand dollars to stop shipping it
does one hundred sixty thousand dollars.

For each of the eighteen R.

he sees this five hundred ten thousand
dollar savings compounds to about nine

billion dollars across the entire
dataset upon consolidating shipments.

We took the vendors that were
matched created two times per year

to create groups of three four and
five vendors.

I would be created.

Fifty two groups of three vendors
thirteen groups of four vendors and

three groups of five vendors for each of
our eighteen R D C's amounting to one

thousand two hundred vendors or
so in order to navigate this

large number of interviews that we created
we developed this time so that broke for

as uncomfortable to part time apart so
adding a specific state vendor ID and

R.T.C. the vendor chairs and sure bullets
are selected vendors associated with

as well as the vendor groups are selected
vendors are part of our display.

So for example in the first room you see
there are some of the vendor has been

matched with two other vendors twelve
times over the course of the calendar year

for our history five zero two three
generating a total savings of roughly ten

thousand dollars on the right side we
see that are so if you vendor is found.

In one of five vendors three
groups of four vendors and

two groups of three that was just to
reiterate in any specific manner.

Each vendor has been matched with
any other vendor at least twice over

the course of our hundred or
so in conclusion.

And yes it presented to us contained less
than two percent of shipments as most

opportunities through the formation of our
editor program optimization model we were

not only able to generate one thousand two
hundred thirty three vendor groups but

also bump up the number of Muslims stop
shipments to fourteen percent through

this process we are saving the Home Depot
or possibly nine billion dollars in

transportation and with that I'd
like to open up for any questions.

I've.

Dangers of the use

of the are you going to say You're saying

there's a ghost or you're saving
one if you get everything you want

or are you going to let
me stop while you're

here that's your question.

We're seeing chose not to say that and
I just time constraints but the reason why

just you know what people
actually really want to see but

realistically it becomes very feasible for
me to do some of the constraints like so

many hours of driving rain that's what

we consider so we should really
think there will be something

that's a take place bettors or
a switch or if that.