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PROFESSOR: Why don't we
go ahead and get started.

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Thank you very much for making
the trek and the logistics

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to get all the
way across campus.

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And I'm pleased to see that
most of us made it back here.

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And if people straggle
in, we'll understand.

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So this afternoon, we're going
to shift from a technology

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focus to now
research groups that

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are concentrating on specific
business problems to solve.

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And our first speaker
is Dr. Elgar Fleisch,

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who is the head of the eyed labs
at the University of St. Gallen

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and ETH in Zurich, Switzerland.

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And he's here to share
about the flagship project

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for the auto-ID labs in
the anti-counterfeit space.

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ELGAR FLEISCH:
Thank you very much.

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So I hope you don't suffer
the same jet lag as I do.

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And I have the best
position in this day

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to talk about any
counterfeiting.

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So this is really
the flagship project.

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We have not only one
flagship project.

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Soon others will pop up.

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But this is a project which
goes across all the labs--

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our seven labs across the world.

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What's the need, actually,
for any counterfeiting?

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Why do we think
this is important?

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Without going into
details, you will find soon

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an extensive white paper
on any counterfeiting using

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RFID and stuff like
that on our web pages.

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You see the red line.

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This is really kind of the
global trade, how it grows.

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And the blue one, that is really
counterfeit, how those grow.

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It's different scales
though, so it has to.

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We see that in recent
years, counterfeiting

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is a good profession.

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And the topic is the
punishments on counterfeit goods

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is really not very high.

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The punishments are
low, but the returns--

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the business cases for the fake
producers, they're very good.

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It's like in drug business.

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So this is why
counterfeiting seems

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to be an increasing topic.

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We all know who work in this
area that in some literature,

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you find numbers that
say about 7% to 10%

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of all goods traded
internationally

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are trading with counterfeit
goods, or gray market goods,

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or stuff like that.

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This number is way exaggerated.

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That's what we found
out in our research.

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You have to divide
it by 10 or so.

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However, still the
problem is a big one.

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And we have not only impact
on companies and on users,

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but also on the economy,
so let's fight this one.

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And this, again, is an
area where a lot of chaos

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in the system, and we've seen
it in Sanjay's presentation.

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When you have somewhere
chaos in a system,

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then RFID might be a good
thing to use in order

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to get chaos straightened
out a little bit.

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What we see, that
we have actually

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very good methods of
authentications already

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in place if you use a banking
card or stuff like that.

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We have heard this morning
from an Austrian gentleman--

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so I'm also from Austria.

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I have to talk to you later on.

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Then we know that in
the banking business,

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we have already
good authentication

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technologies available.

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However, we don't have
low-cost authentication.

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Beyond authentication,
you have usually

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is made for things to humans.

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What we do usually, we
authenticate humans.

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We don't authenticate things.

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But we are building
the infrastructure

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for Internet of things.

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So let's talk about the
machine-machine authentication

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now.

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And therefore, we
need new technologies.

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For doing so, we created kind
of a special interest group.

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We have some companies
working with us.

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The number is strongly growing.

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And the question is really--

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and this is a research question
we didn't solve so far.

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We need support from all of you.

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And how can we use RFID
and related technologies

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in combination with
classical measurements

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to fight counterfeit, parallel
trade, illicit trade, all

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that stuff.

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Well, what are the
research questions?

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And I want you to
think about what could

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be potential research question.

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Where could you propose
some papers or help to us?

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Basically, there is four
different categories

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of research questions.

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One is, what is really the
economy of illicit trade?

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That seems to be
a simple question.

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But you could ask anybody--
pharma industry, whatever,

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in the automotive industry.

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You receive different
answers, unclear answers.

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So we would go
into very detailed.

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This is the first
work package, and I

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think we did some great
work there already.

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To understand what's the
economy of illicit trade.

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And Thorsten Staake over
there, with the nice tie,

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is leading those
research questions.

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The second one is how to
quantify illicit trade.

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Because you need to
have business cases.

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You need to understand, what
is the effective on brands,

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what is the effect
on revenue, what

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is the effective whatsoever.

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So come up with models which
are not consulting models where

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we say you could save 1% or 2%.

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No, we have to go
into the black box

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and come up with clear
figures, clear calculations.

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And then, of course, if we know
how the problem is structured,

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and if we know what
the savings are--

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so how expensive those
technologies could be,

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solutions could be-- then we can
derive the requirements, what

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are resolutions solutions in
any counterfeiting auto-ID base.

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And basically, at the
very end, then-- and this

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is the results for
EPC globalizer.

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What's the impact on
the infrastructure plus?

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So how would the infrastructure
of things, internet

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of things would have
to look like in order

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to serve all these requirements.

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Some of the primary results--

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I go through those
rather quickly--

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is, of course, there is
the good supply chain.

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We call it here, the illicit
intended supply chain.

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There is the bad supply chain.

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And the bad supply chain,
it's not only fakes.

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It's also if you run your
machine beyond the hours

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you should run your machine.

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So you produce products
for the grain market.

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It's parallel trade.

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It's theft.

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And if you have theft introduced
to the illicit supply chain

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somewhere, you bring it back
to the illicit supply chain.

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All those problems,
actually, you

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would find behind the
topic any counterfeiting.

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So we choose any counterfeiting
because of marketing reason.

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Everybody understands--
any counterfeiting.

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But in fact, it's
the most complicated

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track-and-trace thing
you could solve,

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because you need to have a
secure supply chain in order

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to solve those problems.

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One of the general
findings is, you

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see many arrows crossing between
the good and the bad supply

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chain.

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So what you have to
think about first--

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where are you able to
leverage new technology

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in order to, let's say,
do harm to the business

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case of fake producers?

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And this is two points.

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Because only at two points
in these two supply chains,

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there is a good
gatekeeper who watches

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when bad goods cross
from the bad supply chain

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into the good supply chain.

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And this is with customs, and
this is with the end consumers.

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So if we can build systems to
enable customers and customs,

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then we can fight illicit
trade considerably.

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We did some empirical
studies here--

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very funny results.

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We asked really who
would buy fake goods?

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Because it's not clear who
would really buy fake goods.

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And what are the reasons for it?

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Here, one of the interesting
results is that most of them

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buy faked goods because they
think the original one is

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too expensive.

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So it's kind of a
strange argument.

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And the other argument is
really that the cost ratio--

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cost performance ratio is very
good if you buy fake goods.

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Which is probably not true
if you talk about drugs.

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I mean, we have to be
anyway very carefully

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differentiating between
different types of products

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you would fake.

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The ones the
customer knows about,

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the others the customer
won't know about it.

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What's really very
interesting is that we learned

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that many of the people--

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actually, most of the people,
half of it-- we interviewed

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said they didn't buy
faked goods because they

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had no opportunity so far.

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So the market problem
is rather big.

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This is only true
for products you

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would know they are fake,
like the Rolex for $5

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or something like that.

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And most of your problems
are with products

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you don't know as a user.

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So what's the impact of
an auto-ID-based solution

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as one first framework for an
answer to the last research

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question?

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OK, if we have automated
product authentication,

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then just use the simple
managed methodology

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we developed in the labs.

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The cost of checking whether
something is identical or not

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decreases.

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It's always what we do
with this infrastructure,

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we reduce the cost
of measuring reality.

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So it's very simple and cheap
to check whether something

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is identical if you
have an RFID tag

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and some infrastructure
available on the product

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and around the product.

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So you start checking more
often in each warehouse,

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or with the mobile
phone at home.

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If you check more often,
you check not only

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on a statistical basis, but
you may do a 100% check.

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So end of statistics
is the key word there,

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which then will have
impacts on the revenue

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model out of package too.

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Reputation would go up.

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And it's not so simple,
because if you're Microsoft

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and your software
got faked in China,

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it's probably good for you now.

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Because you're developing
a kind of a standard there.

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In five or 10 years, you
start using the laws which

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are building up currently.

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And then force people to
buy your Microsoft products,

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but then it's standard already.

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So it's not always true
that fakes are bad.

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We have to be
careful about that.

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Anyway, so this would have
an impact on the return.

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Now we could think about,
OK, if we check a lot,

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we have a high-resolution
data on those illicit actors.

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If we know how the
actors work, we

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can do way more to
fight those parties.

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And of course, we
can also derive

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know-how and how to
engineer the products so

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that the counterfeiting
is getting more difficult.

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Which again, helps
in our business case,

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but then also we
can come up with

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nice competitive strategies.

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Because we learned
that the real danger is

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if somebody produces fakes,
uses your IP, at the beginning

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you lose revenue.

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But on the long run, these
guys build up know-how.

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And they become a
real competitor,

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so this is one thing
we want to fight.

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And then of course, if we have
a tool available for everybody

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where it's very simple.

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And I gave the signal--

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I said the goal of this research
is that we have a warehouse.

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And within one second,
we can check the product,

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enter our product in a
warehouse, whether they are OK

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or not.

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I mean, we cannot reach
this within the next years.

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But this is a clear goal.

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But if we have a tool
like that, then we

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can start giving to third-parity
suppliers, this tool.

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And they should check
for any counterfeited--

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for faked goods.

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So enter here, new business
models would come up.

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You probably could
enable a little army

251
00:11:41,650 --> 00:11:43,730
in fighting these issues.

252
00:11:43,730 --> 00:11:46,552
So again, it's not really
just the automation part,

253
00:11:46,552 --> 00:11:48,010
but it's the
transformational part,

254
00:11:48,010 --> 00:11:50,140
which seems to be
very interesting

255
00:11:50,140 --> 00:11:54,470
in this counterfeit research.

256
00:11:54,470 --> 00:11:56,380
Of course, there is
100 different ways

257
00:11:56,380 --> 00:11:58,280
in how to do any counterfeiting.

258
00:11:58,280 --> 00:12:00,940
That would be part of the
solution-to-work package.

259
00:12:00,940 --> 00:12:05,020
Three, do it via pedigree
and normal EPC tags,

260
00:12:05,020 --> 00:12:08,800
or do it via secure RFID tags.

261
00:12:08,800 --> 00:12:12,080
I think Friends of Austria
are developing this direction.

262
00:12:12,080 --> 00:12:13,970
So it depends on
the business case,

263
00:12:13,970 --> 00:12:15,430
it depends on the problem.

264
00:12:15,430 --> 00:12:19,810
And the nice part
is, the EPC network,

265
00:12:19,810 --> 00:12:22,210
the auto-ID
infrastructure should

266
00:12:22,210 --> 00:12:26,520
be able to cope with all those
different varieties of how

267
00:12:26,520 --> 00:12:27,970
to secure the supply chain.

268
00:12:27,970 --> 00:12:32,430
Not just with one, and this is
the tricky part in doing so.

269
00:12:32,430 --> 00:12:35,220
This is actually what
we are planning to do.

270
00:12:35,220 --> 00:12:38,250
We have some considerable
first results achieved.

271
00:12:38,250 --> 00:12:41,400
And we are planning
that by the autumn,

272
00:12:41,400 --> 00:12:44,623
there will be a book out there
showing the first results

273
00:12:44,623 --> 00:12:45,540
on any counterfeiting.

274
00:12:45,540 --> 00:12:52,180
And with this, I think I
hand over to the cool chain

275
00:12:52,180 --> 00:12:56,578
where we have another different
kind of how to secure a supply

276
00:12:56,578 --> 00:12:57,870
chain in a different direction.

277
00:12:57,870 --> 00:12:58,370
Thank you.

278
00:13:04,729 --> 00:13:06,250
PROFESSOR: So I'd
like to present

279
00:13:06,250 --> 00:13:07,250
John Pierre [INAUDIBLE].

280
00:13:07,250 --> 00:13:09,260
He is another member of
the conference committee

281
00:13:09,260 --> 00:13:10,790
that helped to
organize this event.

282
00:13:10,790 --> 00:13:14,390
He is a Associate Professor
of Agricultural and Biological

283
00:13:14,390 --> 00:13:17,180
Engineering and a Co-Director
for the Center for Food

284
00:13:17,180 --> 00:13:19,200
Distribution at--

285
00:13:21,710 --> 00:13:24,030
FSU or the University
of Florida?

286
00:13:24,030 --> 00:13:25,732
[INTERPOSING VOICES]

287
00:13:25,732 --> 00:13:27,940
GUEST SPEAKER: It's bad for
you, I can tell you that.

288
00:13:31,130 --> 00:13:33,380
OK, I'm going to do that.

289
00:13:33,380 --> 00:13:34,850
Well, let me start that.

290
00:13:34,850 --> 00:13:38,000
Well, I'm going to
talk about cold chain

291
00:13:38,000 --> 00:13:39,860
with RFID time
[INAUDIBLE] writing.

292
00:13:39,860 --> 00:13:42,710
And I'm going to change my
definition of cold chain,

293
00:13:42,710 --> 00:13:46,430
because after I saw us crossing
the street this afternoon,

294
00:13:46,430 --> 00:13:48,830
I think this is really cold
chain-- one by one, crossing

295
00:13:48,830 --> 00:13:49,830
the street in the slush.

296
00:13:49,830 --> 00:13:51,080
That was great.

297
00:13:51,080 --> 00:13:53,720
Well, you heard a lot of
research group talking

298
00:13:53,720 --> 00:13:54,290
this morning.

299
00:13:54,290 --> 00:13:57,860
And I feel like a bit odd,
because I'm more research

300
00:13:57,860 --> 00:13:59,930
group, but also end user.

301
00:13:59,930 --> 00:14:02,120
We developed some
of the technology,

302
00:14:02,120 --> 00:14:03,950
but we are a big
user of everything

303
00:14:03,950 --> 00:14:05,660
that you are
providing-- this morning

304
00:14:05,660 --> 00:14:06,840
that you are talking about.

305
00:14:06,840 --> 00:14:09,200
And let's go with that.

306
00:14:09,200 --> 00:14:12,710
Well, we are not the RFID lab.

307
00:14:12,710 --> 00:14:16,790
We are a research center
that has a RFID lab inside.

308
00:14:16,790 --> 00:14:19,590
Or we call that the Center
for Food Distribution

309
00:14:19,590 --> 00:14:21,410
Retailing, which is CFDR.

310
00:14:21,410 --> 00:14:23,890
And the mission of our center
is to provide a food industry

311
00:14:23,890 --> 00:14:26,785
unique environment to
assure food quality

312
00:14:26,785 --> 00:14:28,910
and safety throughout the
whole distribution chain.

313
00:14:28,910 --> 00:14:32,060
So we don't take only one part,
but we take the old thing,

314
00:14:32,060 --> 00:14:34,550
from the beginning in the field
or the manufacturing plant

315
00:14:34,550 --> 00:14:37,813
to until the customer
leaves the retail store.

316
00:14:37,813 --> 00:14:39,230
That way that we
organize that, we

317
00:14:39,230 --> 00:14:41,570
have 28 faculty at
University of Florida

318
00:14:41,570 --> 00:14:44,510
and six with our
university worldwide.

319
00:14:44,510 --> 00:14:47,870
And all of us has a
specific discipline, and we

320
00:14:47,870 --> 00:14:49,700
always joint effort together.

321
00:14:49,700 --> 00:14:53,540
The idea, in this case, is to
look at different angles, what

322
00:14:53,540 --> 00:14:56,330
we can do with RFID.

323
00:14:56,330 --> 00:14:59,450
Our center is as an
external advisory board,

324
00:14:59,450 --> 00:15:01,130
from the top down
international decision

325
00:15:01,130 --> 00:15:03,770
maker in the food industry,
retailer, food service.

326
00:15:03,770 --> 00:15:05,402
And to be advisory
board member, you

327
00:15:05,402 --> 00:15:06,860
have to be president
or senior vice

328
00:15:06,860 --> 00:15:09,420
president of a big company.

329
00:15:09,420 --> 00:15:12,320
So we have Walmart,
Publix, Albertsons, Burger

330
00:15:12,320 --> 00:15:16,490
King, [INAUDIBLE] ShopRite,
Preference Food Group, Outback.

331
00:15:16,490 --> 00:15:18,150
And are we going
to add three more.

332
00:15:18,150 --> 00:15:20,420
So these people, twice a
year, come in Gainesville

333
00:15:20,420 --> 00:15:22,980
and tell us what their
industry is looking for.

334
00:15:22,980 --> 00:15:25,460
And of course, RFID is
a big thing about it.

335
00:15:25,460 --> 00:15:29,858
But today, I'm going to talk
to you about why temperature

336
00:15:29,858 --> 00:15:31,400
tracking is so
important for the food

337
00:15:31,400 --> 00:15:34,572
industry and the pharmaceutical
industry from our eyes.

338
00:15:34,572 --> 00:15:36,530
From the external advisory
board, but also what

339
00:15:36,530 --> 00:15:39,020
we have done during
the whole year.

340
00:15:39,020 --> 00:15:41,465
For the food industry,
most personal products

341
00:15:41,465 --> 00:15:43,820
are really affected
by temperature.

342
00:15:43,820 --> 00:15:45,982
It's a question of
quality and safety.

343
00:15:45,982 --> 00:15:48,440
For some of you, last night I
was flying with [INAUDIBLE],,

344
00:15:48,440 --> 00:15:49,850
and you can watch TV.

345
00:15:49,850 --> 00:15:51,260
And I was watching
Dateline where

346
00:15:51,260 --> 00:15:55,430
they were promoting a big
survey that they did about food

347
00:15:55,430 --> 00:15:57,200
safety and the retail store.

348
00:15:57,200 --> 00:16:00,140
And they were reporting
that most of the infractions

349
00:16:00,140 --> 00:16:01,850
were about temperature
management.

350
00:16:01,850 --> 00:16:04,833
We get food poisoning, not
only at the retail store,

351
00:16:04,833 --> 00:16:06,500
but also at restaurant
chains and things

352
00:16:06,500 --> 00:16:09,380
like that just because of poor
management of temperature.

353
00:16:09,380 --> 00:16:11,960
And it makes a big
difference, because each year,

354
00:16:11,960 --> 00:16:14,600
retail stores can
lose about $400,000

355
00:16:14,600 --> 00:16:17,600
due to bad temperature
management per store.

356
00:16:17,600 --> 00:16:20,990
For example, if I count all the
stores that our advisory board

357
00:16:20,990 --> 00:16:23,640
has, it's about 8,000 stores.

358
00:16:23,640 --> 00:16:26,720
So if you add $400,000 for each
of them, it's about $3 billion

359
00:16:26,720 --> 00:16:30,290
per year we lose, just because
of poor temperature management,

360
00:16:30,290 --> 00:16:32,870
but also people
that get sick too.

361
00:16:32,870 --> 00:16:35,090
So this is what we
have been looking for.

362
00:16:35,090 --> 00:16:37,790
Trying to focus on what we can
do with temperature tracking

363
00:16:37,790 --> 00:16:39,720
to help our industry.

364
00:16:39,720 --> 00:16:41,635
So where temperature
may be a problem--

365
00:16:41,635 --> 00:16:43,010
well, it can come
from the field.

366
00:16:43,010 --> 00:16:44,390
You have to know what
kind of temperature

367
00:16:44,390 --> 00:16:45,515
when you harvest something.

368
00:16:45,515 --> 00:16:46,820
Is it cold, is it warm?

369
00:16:46,820 --> 00:16:49,340
If it's very warm, I
have to cool it down.

370
00:16:49,340 --> 00:16:52,230
And at the warehouse,
also, during transit

371
00:16:52,230 --> 00:16:53,280
is a major thing.

372
00:16:53,280 --> 00:16:55,880
During transit, distribution,
and in the store--

373
00:16:55,880 --> 00:16:58,820
if I can have all
this information live,

374
00:16:58,820 --> 00:17:01,190
I can manage much
better things in terms

375
00:17:01,190 --> 00:17:02,600
of cold chain management.

376
00:17:02,600 --> 00:17:04,849
Also, I can prevent
any problem that

377
00:17:04,849 --> 00:17:07,160
can happen during by
transit without it happening

378
00:17:07,160 --> 00:17:09,690
and get too bad.

379
00:17:09,690 --> 00:17:12,089
Well, knowing
real-time temperature

380
00:17:12,089 --> 00:17:14,579
can also predict
residual shelf life

381
00:17:14,579 --> 00:17:16,540
and make decisions
based on this knowledge.

382
00:17:16,540 --> 00:17:19,530
So what we have been spending
many years in our center

383
00:17:19,530 --> 00:17:22,890
is that we are trying to have a
good sense of what temperature

384
00:17:22,890 --> 00:17:26,079
has an effect on quality
and safety of food.

385
00:17:26,079 --> 00:17:28,573
So we have been focusing a
lot on produce, because we

386
00:17:28,573 --> 00:17:29,740
are in the state of Florida.

387
00:17:29,740 --> 00:17:30,960
So we have a lot of produce.

388
00:17:30,960 --> 00:17:33,300
And what we have developed
in the last few years

389
00:17:33,300 --> 00:17:37,500
is a mathematical product
to predict quality.

390
00:17:37,500 --> 00:17:39,510
So just to give you
an example here,

391
00:17:39,510 --> 00:17:42,690
is that based on experimental
data that we have done,

392
00:17:42,690 --> 00:17:45,030
we can predict, if you
give me the temperature

393
00:17:45,030 --> 00:17:47,460
chart, the history of the
temperature of your product,

394
00:17:47,460 --> 00:17:50,830
I can predict exactly how the
product is going to look like

395
00:17:50,830 --> 00:17:54,090
and what kind of shelf life
I still have in my store

396
00:17:54,090 --> 00:17:56,820
or at least at the moment
that I read my temperature.

397
00:17:56,820 --> 00:17:59,570
So we have a huge database
of experimental data.

398
00:17:59,570 --> 00:18:03,330
And all these models have
been developed by our team.

399
00:18:03,330 --> 00:18:05,310
So if you provide
me-- and also we

400
00:18:05,310 --> 00:18:07,140
can predict what kind
of quality criteria

401
00:18:07,140 --> 00:18:09,150
you're looking for if
you give me temperature.

402
00:18:09,150 --> 00:18:12,720
I can predict if it's the
crispiness of lettuce you're

403
00:18:12,720 --> 00:18:17,100
looking forward, the color, if
it's mold grown on raspberries,

404
00:18:17,100 --> 00:18:19,200
we can all predict
these things if you

405
00:18:19,200 --> 00:18:21,760
provide me the temperature
tracking of that.

406
00:18:21,760 --> 00:18:23,730
So we have been trying
a lot to do that.

407
00:18:23,730 --> 00:18:25,782
One of the requests was
from the restaurant chain

408
00:18:25,782 --> 00:18:26,490
and the retailer.

409
00:18:26,490 --> 00:18:29,702
They said, when we get something
in our distribution center

410
00:18:29,702 --> 00:18:32,160
and we have this temperature
monitoring, we can look at it,

411
00:18:32,160 --> 00:18:35,130
but it's very difficult
to do something with it.

412
00:18:35,130 --> 00:18:36,640
We have to interpret that.

413
00:18:36,640 --> 00:18:38,970
We look for peaks of temperature
and things like that,

414
00:18:38,970 --> 00:18:41,530
but not exactly what
to make a decision.

415
00:18:41,530 --> 00:18:44,880
So if we can track that way
before it gets to the DC,

416
00:18:44,880 --> 00:18:47,970
we can always manage our
inventory and our distribution

417
00:18:47,970 --> 00:18:51,163
center and decide if my
product that came yesterday,

418
00:18:51,163 --> 00:18:52,830
maybe it's in better
shape that the ones

419
00:18:52,830 --> 00:18:54,300
coming in an hour from now.

420
00:18:54,300 --> 00:18:55,890
And maybe I ship
that to my store

421
00:18:55,890 --> 00:18:58,260
right away, the one coming
in, and keep the other one

422
00:18:58,260 --> 00:18:59,280
in my warehouse.

423
00:18:59,280 --> 00:19:04,300
Because it can still stay for a
few days more without problem.

424
00:19:04,300 --> 00:19:08,160
So we can predict in
terms of the quality,

425
00:19:08,160 --> 00:19:10,640
in terms of the pallet level,
the case level, and the item

426
00:19:10,640 --> 00:19:11,140
level.

427
00:19:11,140 --> 00:19:12,630
And this is what we
have been working.

428
00:19:12,630 --> 00:19:14,547
We have been working
with temperature tracking

429
00:19:14,547 --> 00:19:18,120
tags available on the market
from different suppliers

430
00:19:18,120 --> 00:19:19,740
at different frequencies.

431
00:19:19,740 --> 00:19:21,690
But still, the same
problem is where

432
00:19:21,690 --> 00:19:23,640
I'm going to put
my tag to give me

433
00:19:23,640 --> 00:19:25,680
a good idea of what is
my temperature to predict

434
00:19:25,680 --> 00:19:27,450
the shelf life of that.

435
00:19:27,450 --> 00:19:28,920
One of the issues
that we have done

436
00:19:28,920 --> 00:19:31,523
is that, well, the
best way to measure

437
00:19:31,523 --> 00:19:32,940
my temperature of
my product would

438
00:19:32,940 --> 00:19:35,520
be right in the core of my
product at different locations,

439
00:19:35,520 --> 00:19:37,810
at least have a good
reading of that.

440
00:19:37,810 --> 00:19:40,200
But as you know, a
lot of these products,

441
00:19:40,200 --> 00:19:43,620
like if you take
lettuce with 94% water,

442
00:19:43,620 --> 00:19:45,870
it's pretty difficult to
get a signal through it.

443
00:19:45,870 --> 00:19:49,570
So I cannot read it, so I have
to place my tag somewhere else.

444
00:19:49,570 --> 00:19:52,470
So at this point, we try
with the pallet level one,

445
00:19:52,470 --> 00:19:55,380
where we have to place this
thing on the outside, right

446
00:19:55,380 --> 00:19:58,750
at the bottom of the
pallet at this point here.

447
00:19:58,750 --> 00:20:01,470
So what we did is that, hey,
let's measure temperature

448
00:20:01,470 --> 00:20:03,120
inside the pallet
and measure what

449
00:20:03,120 --> 00:20:06,090
the RFID tag is providing us.

450
00:20:06,090 --> 00:20:07,680
Well, that's the problem here.

451
00:20:07,680 --> 00:20:10,920
It's because, for example,
I hope that you can see it.

452
00:20:10,920 --> 00:20:13,530
But the red line is the
temperature of my tag,

453
00:20:13,530 --> 00:20:16,830
and all the other lines is the
temperature inside my pallet

454
00:20:16,830 --> 00:20:17,710
load.

455
00:20:17,710 --> 00:20:20,820
So I'm pretty far for
what I should measure.

456
00:20:20,820 --> 00:20:23,440
Of course, some people are
going to tell you, well,

457
00:20:23,440 --> 00:20:25,950
if you know the package,
you can always predict.

458
00:20:25,950 --> 00:20:29,550
With formula heat
and mass transfer,

459
00:20:29,550 --> 00:20:30,630
I know all this stuff.

460
00:20:30,630 --> 00:20:31,240
That's fine.

461
00:20:31,240 --> 00:20:33,570
But the problem is that I
deal with the food industry

462
00:20:33,570 --> 00:20:35,790
when we have about
650 different cases

463
00:20:35,790 --> 00:20:38,370
or different configurations
of materials,

464
00:20:38,370 --> 00:20:40,170
more than 1,000 products.

465
00:20:40,170 --> 00:20:42,240
Do the math-- all
the combinations

466
00:20:42,240 --> 00:20:43,410
that can come together.

467
00:20:43,410 --> 00:20:45,330
It's pretty difficult
to get that.

468
00:20:45,330 --> 00:20:47,680
So at this point,
we said, all right,

469
00:20:47,680 --> 00:20:48,810
let's go to the case level.

470
00:20:48,810 --> 00:20:50,500
Maybe we can get it better.

471
00:20:50,500 --> 00:20:53,100
So what we have done is that
we have embedded the RFID temp

472
00:20:53,100 --> 00:20:56,010
tag on a reusable
plastic container.

473
00:20:56,010 --> 00:20:58,860
So at least we are getting
a little bit closer to that.

474
00:20:58,860 --> 00:21:02,820
That these are designed to
be five on the pallet load,

475
00:21:02,820 --> 00:21:05,100
meaning that I always have
one end of the container

476
00:21:05,100 --> 00:21:08,850
that they can see so I don't
have a problem to read the tag.

477
00:21:08,850 --> 00:21:12,480
The problem is exactly the
same if you look at it.

478
00:21:12,480 --> 00:21:16,680
The RFID tag is the blue
line, and the purple one

479
00:21:16,680 --> 00:21:20,010
is the surface of my container,
and the blue is what is inside.

480
00:21:20,010 --> 00:21:22,470
Even a small
container like that,

481
00:21:22,470 --> 00:21:25,350
I still cannot get my
temperature inside.

482
00:21:25,350 --> 00:21:28,140
Well, it's better because
if I put that and embed

483
00:21:28,140 --> 00:21:30,570
that in a plastic
container, I pretty much

484
00:21:30,570 --> 00:21:32,863
know what will be
the heat transfer

485
00:21:32,863 --> 00:21:34,530
to predict what will
be the temperature.

486
00:21:34,530 --> 00:21:36,820
So I am in a better prediction.

487
00:21:36,820 --> 00:21:39,720
Now I can predict what's
going on inside my box,

488
00:21:39,720 --> 00:21:43,170
and we can get a better
sense of prediction.

489
00:21:43,170 --> 00:21:46,320
At the item level, well, we
didn't see very much use of it.

490
00:21:46,320 --> 00:21:49,380
Because we always move
these items to a certain--

491
00:21:49,380 --> 00:21:53,628
I don't decide, I don't get my
box in my distribution center

492
00:21:53,628 --> 00:21:55,920
and just look at one lettuce,
and this one is not good,

493
00:21:55,920 --> 00:21:56,490
this one is good.

494
00:21:56,490 --> 00:21:57,690
No, I don't have
time to do that.

495
00:21:57,690 --> 00:21:58,898
They have to move my product.

496
00:21:58,898 --> 00:22:01,410
So case level would
be the smallest item

497
00:22:01,410 --> 00:22:03,360
that I can use with
temperature tracking,

498
00:22:03,360 --> 00:22:07,440
with temperature keeping in
memory so I can download that

499
00:22:07,440 --> 00:22:08,980
at any time.

500
00:22:08,980 --> 00:22:11,670
So at the item level, it's
more a punctual temperature.

501
00:22:11,670 --> 00:22:12,480
I just want some--

502
00:22:12,480 --> 00:22:13,920
I don't want to store this data.

503
00:22:13,920 --> 00:22:16,590
Just tell me the temperature
of my product right now.

504
00:22:16,590 --> 00:22:18,130
Is it at the right place?

505
00:22:18,130 --> 00:22:19,710
So what I can do
is, because I can

506
00:22:19,710 --> 00:22:22,560
see if it was unbroken
or not on the display.

507
00:22:22,560 --> 00:22:25,290
So we can see that if my
refrigerator display doesn't

508
00:22:25,290 --> 00:22:27,990
work well, I should have
a signal about that.

509
00:22:27,990 --> 00:22:30,570
Because right now,
you see the difference

510
00:22:30,570 --> 00:22:34,020
between nine hours of not
good refrigerator display

511
00:22:34,020 --> 00:22:36,420
compared to the
unbroken cold chain.

512
00:22:36,420 --> 00:22:38,220
I prefer this one than this one.

513
00:22:38,220 --> 00:22:41,310
Well, in your case,
it's this one here.

514
00:22:41,310 --> 00:22:41,900
It's snowy.

515
00:22:41,900 --> 00:22:44,270
You know, it looks like it
will get some snow soon.

516
00:22:44,270 --> 00:22:47,760
Well, so punctual temperature
at the item level,

517
00:22:47,760 --> 00:22:48,630
it's very important.

518
00:22:48,630 --> 00:22:50,088
Because it can
prevent misplacement

519
00:22:50,088 --> 00:22:52,722
of the product in the store.

520
00:22:52,722 --> 00:22:54,180
It's bad to say
that, but each time

521
00:22:54,180 --> 00:22:56,763
that you have a human decision
in this old distribution chain.

522
00:22:56,763 --> 00:22:58,620
This is where you have losses.

523
00:22:58,620 --> 00:23:03,170
And this is what my wife told
me each time that I shop.

524
00:23:03,170 --> 00:23:06,470
You made a decision to buy
that, and that's the last.

525
00:23:06,470 --> 00:23:10,460
But the thing is that
you put this item,

526
00:23:10,460 --> 00:23:12,110
and if it's not the
right temperature,

527
00:23:12,110 --> 00:23:13,550
you kill your product.

528
00:23:13,550 --> 00:23:17,540
You will not have the
top quality, meaning

529
00:23:17,540 --> 00:23:19,190
that people won't buy it.

530
00:23:19,190 --> 00:23:21,863
And now it's opened the
door to smart display.

531
00:23:21,863 --> 00:23:23,780
We are already working
on the copying on that.

532
00:23:23,780 --> 00:23:28,490
Where if I can read my tag,
even if the precision is not

533
00:23:28,490 --> 00:23:30,950
there that much, at
least I know if there's

534
00:23:30,950 --> 00:23:33,200
a problem with the airflow.

535
00:23:33,200 --> 00:23:37,200
Or if I should redirect my air
flow in order to [INAUDIBLE]..

536
00:23:37,200 --> 00:23:40,610
And now, we should
provide a good thing

537
00:23:40,610 --> 00:23:42,740
for-- like the food
service company.

538
00:23:42,740 --> 00:23:45,470
Where I can have a best
before date dynamic.

539
00:23:45,470 --> 00:23:48,140
Because the best before date,
when I produce something,

540
00:23:48,140 --> 00:23:49,640
it's always the
worst-case scenario,

541
00:23:49,640 --> 00:23:52,500
that somebody kept that in their
car for that number of hours.

542
00:23:52,500 --> 00:23:56,060
So my best [INAUDIBLE]
is going to be that date.

543
00:23:56,060 --> 00:24:00,223
But if I do a good job, I
should have credit from that.

544
00:24:00,223 --> 00:24:02,390
If I have a good cold chain
management, particularly

545
00:24:02,390 --> 00:24:04,340
for food service and
restaurant chain,

546
00:24:04,340 --> 00:24:07,010
I should have my best before
date maybe stretch a little bit

547
00:24:07,010 --> 00:24:10,670
longer, because my temperature
management was great.

548
00:24:10,670 --> 00:24:14,360
So just to give you an idea,
if I do that tomorrow morning,

549
00:24:14,360 --> 00:24:17,900
the food service company is
going to save 30% of losses

550
00:24:17,900 --> 00:24:20,000
right away, because
I allowed them

551
00:24:20,000 --> 00:24:21,560
to have a longer
best before date,

552
00:24:21,560 --> 00:24:23,510
because they did a
good job in coaching.

553
00:24:23,510 --> 00:24:25,370
That's a lot of money for them.

554
00:24:25,370 --> 00:24:27,980
At home application,
we discussed about that

555
00:24:27,980 --> 00:24:28,530
this morning.

556
00:24:28,530 --> 00:24:30,980
You said something about
refrigerator, thinking smart,

557
00:24:30,980 --> 00:24:32,660
and what you have
in the refrigerator.

558
00:24:32,660 --> 00:24:35,720
Well, I can even tell
you, later in the future,

559
00:24:35,720 --> 00:24:38,593
if my refrigerator is
well, or if maybe I

560
00:24:38,593 --> 00:24:40,010
should eat this
thing that I don't

561
00:24:40,010 --> 00:24:42,260
want to eat because tomorrow
it's going to be too bad.

562
00:24:42,260 --> 00:24:44,450
Or maybe I should wait tomorrow.

563
00:24:44,450 --> 00:24:48,170
So prevention, also, of
the DC is very important

564
00:24:48,170 --> 00:24:49,453
because we get all this load.

565
00:24:49,453 --> 00:24:51,620
We should make sure that
they are at the right place

566
00:24:51,620 --> 00:24:52,670
at the right time.

567
00:24:52,670 --> 00:24:54,530
I can have smart
transportation--

568
00:24:54,530 --> 00:24:57,200
refrigerated
trailer, C container.

569
00:24:57,200 --> 00:24:59,030
I can optimize my
cooling at the form.

570
00:24:59,030 --> 00:25:00,950
And we're going see in
the next presentation,

571
00:25:00,950 --> 00:25:04,310
that cooling is a critical
thing on your shelf life.

572
00:25:04,310 --> 00:25:07,320
It prevents non-safe food to
enter my distribution chain.

573
00:25:07,320 --> 00:25:09,410
So if my truck is
coming and I know a day

574
00:25:09,410 --> 00:25:12,410
before that this
thing is going rotten

575
00:25:12,410 --> 00:25:14,720
because of the
temperature, I don't want

576
00:25:14,720 --> 00:25:16,280
to open this door in my DC.

577
00:25:16,280 --> 00:25:19,770
With all the airflow,
spores flying everywhere,

578
00:25:19,770 --> 00:25:21,140
it's going to be a pain for us.

579
00:25:21,140 --> 00:25:23,720
But also, it's going to be a
cold chain diagnostic tool.

580
00:25:23,720 --> 00:25:25,820
Because each time that
I can fix something,

581
00:25:25,820 --> 00:25:29,540
I can backtrack very well, very
precisely where my unit is not

582
00:25:29,540 --> 00:25:30,350
working properly.

583
00:25:30,350 --> 00:25:31,808
Because sometimes
it can take weeks

584
00:25:31,808 --> 00:25:34,310
before you find out about that.

585
00:25:34,310 --> 00:25:36,930
Very quick, let's go
with the pharma industry.

586
00:25:36,930 --> 00:25:38,930
The pharma industry has
a very strict regulation

587
00:25:38,930 --> 00:25:39,930
about temperature range.

588
00:25:39,930 --> 00:25:42,650
And I'm talking about cold
chain management here.

589
00:25:42,650 --> 00:25:45,380
Neither, very good accuracy--
if you provide me a temperature,

590
00:25:45,380 --> 00:25:48,530
it's better to be good, because
my range is very, very small.

591
00:25:48,530 --> 00:25:50,570
Most of the vaccine is
going to be between 2

592
00:25:50,570 --> 00:25:52,170
and 8 degrees Celsius.

593
00:25:52,170 --> 00:25:56,150
So if you tell me that your
RFID tag is plus-minus 2 degrees

594
00:25:56,150 --> 00:25:59,210
Celsius, well, I'm
not interested at all.

595
00:25:59,210 --> 00:26:02,210
And I have to be able to read
it before I open the container.

596
00:26:02,210 --> 00:26:05,270
Because if I cannot, if
I have to open it, well,

597
00:26:05,270 --> 00:26:09,320
this is where it gets to a
conflict of what happened

598
00:26:09,320 --> 00:26:10,130
before I opened it.

599
00:26:10,130 --> 00:26:14,090
But also, I want to read
that during my transit.

600
00:26:14,090 --> 00:26:15,530
For some of you
who are aware, we

601
00:26:15,530 --> 00:26:17,480
have been contracted by
the American Red Cross

602
00:26:17,480 --> 00:26:20,330
to redesign their distribution
system in terms of packaging,

603
00:26:20,330 --> 00:26:22,170
but also on the network.

604
00:26:22,170 --> 00:26:24,890
And we are trying this
thing, also the RFID unit.

605
00:26:24,890 --> 00:26:27,820
And what is the major
thing is, for them,

606
00:26:27,820 --> 00:26:29,570
it's not a question
of losing the product,

607
00:26:29,570 --> 00:26:31,740
but more not
providing the supply.

608
00:26:31,740 --> 00:26:35,408
So if they can understand what
is the temperature before it

609
00:26:35,408 --> 00:26:37,700
gets to this nation and decide
if this product is going

610
00:26:37,700 --> 00:26:39,710
to be accepted or
not, well, they

611
00:26:39,710 --> 00:26:42,052
can always ship another
one to make sure

612
00:26:42,052 --> 00:26:43,010
that they get a supply.

613
00:26:43,010 --> 00:26:46,040
Because in 24 hours, you have
to supply 4,000 different drop

614
00:26:46,040 --> 00:26:48,120
point hospitals in US with them.

615
00:26:48,120 --> 00:26:51,650
So you have to know this
visibility about that.

616
00:26:51,650 --> 00:26:54,620
One of the problems
with the pharma industry

617
00:26:54,620 --> 00:26:55,760
with the cold packaging--

618
00:26:55,760 --> 00:26:57,380
I have two minutes?

619
00:26:57,380 --> 00:26:59,720
And very quick is
that it's unfriendly.

620
00:26:59,720 --> 00:27:01,610
Many of the packaging
components are

621
00:27:01,610 --> 00:27:04,220
very unfriendly to
RF temperature tag.

622
00:27:04,220 --> 00:27:08,120
For example, if I take some of
the components-- we have foil,

623
00:27:08,120 --> 00:27:11,540
we have gel packs, we have metal
cans, and all these things.

624
00:27:11,540 --> 00:27:13,370
I can get a signal through it.

625
00:27:13,370 --> 00:27:16,400
Even some of the vaccines
are so bulky that I can not

626
00:27:16,400 --> 00:27:17,150
do that either.

627
00:27:17,150 --> 00:27:21,240
For example, I just
slice one packet in half.

628
00:27:21,240 --> 00:27:24,560
So it's the side of a styrofoam
container with all the vaccine

629
00:27:24,560 --> 00:27:25,520
and all the gel packs.

630
00:27:25,520 --> 00:27:26,990
And I just cut it in half.

631
00:27:26,990 --> 00:27:29,660
And what happened
is that many places,

632
00:27:29,660 --> 00:27:30,950
I cannot even read the tag.

633
00:27:30,950 --> 00:27:33,530
Maybe at the bottom,
sometimes I have enough

634
00:27:33,530 --> 00:27:35,030
that I can read something.

635
00:27:35,030 --> 00:27:36,560
But most of the time, I cannot.

636
00:27:36,560 --> 00:27:39,050
So we have to find a
way to get around that.

637
00:27:39,050 --> 00:27:41,480
Because the good
value about it is

638
00:27:41,480 --> 00:27:44,180
because I don't want to
open my container before.

639
00:27:44,180 --> 00:27:45,680
Another one is the vacuum panel.

640
00:27:45,680 --> 00:27:50,750
Vacuum panel is a vacuum that
is wrapped with mylar or foil.

641
00:27:50,750 --> 00:27:53,480
And because of the vacuum,
it has very high insulation.

642
00:27:53,480 --> 00:27:56,300
So the top-notch product in
the pharma industry-- vaccine,

643
00:27:56,300 --> 00:27:57,470
very expensive one--

644
00:27:57,470 --> 00:28:00,900
they all ship like that because
it's very good for them.

645
00:28:00,900 --> 00:28:02,490
We have no signal through that.

646
00:28:02,490 --> 00:28:04,103
It's kind of
completely shielded.

647
00:28:04,103 --> 00:28:06,270
So I cannot measure any
temperature and read it from

648
00:28:06,270 --> 00:28:07,207
the outside.

649
00:28:07,207 --> 00:28:08,790
And we have been
challenged with that,

650
00:28:08,790 --> 00:28:11,830
because a lot of companies
are using that now.

651
00:28:11,830 --> 00:28:13,680
So just as a
conclusion, I can say

652
00:28:13,680 --> 00:28:17,020
that RFID temp tags opened a new
era for cold chain management.

653
00:28:17,020 --> 00:28:20,195
Now all our researchers
are thrilled about that.

654
00:28:20,195 --> 00:28:21,570
Because it can
predict things, it

655
00:28:21,570 --> 00:28:23,850
can have data that they
didn't have before.

656
00:28:23,850 --> 00:28:26,760
The food industry should benefit
from new smart technology.

657
00:28:26,760 --> 00:28:28,500
And the pharmaceutical
industry can

658
00:28:28,500 --> 00:28:30,000
have the real-time
visibility, which

659
00:28:30,000 --> 00:28:31,583
is going to change
quite a lot the way

660
00:28:31,583 --> 00:28:33,510
that they decide
which items they

661
00:28:33,510 --> 00:28:35,490
should send to the other one.

662
00:28:35,490 --> 00:28:36,610
That's it.

663
00:28:36,610 --> 00:28:37,110
Thank you.

664
00:28:47,211 --> 00:28:49,210
PROFESSOR: Thank you, very much.

665
00:28:49,210 --> 00:28:53,190
Our next speaker-- in place
of Christian Helms who

666
00:28:53,190 --> 00:28:57,510
had a family emergency,
he was kind enough

667
00:28:57,510 --> 00:29:03,930
to send Michael Nicometo who
is Director at the Cold Chain

668
00:29:03,930 --> 00:29:06,840
Group AG in Bremen, Germany.

669
00:29:14,740 --> 00:29:16,685
MICHAEL NICOMETO:
OK, basically, I'm

670
00:29:16,685 --> 00:29:19,060
here to talk about what some
of the problems that we have

671
00:29:19,060 --> 00:29:21,850
are when we think about
embracing or adopting

672
00:29:21,850 --> 00:29:25,180
RFID technology
within the cool chain.

673
00:29:25,180 --> 00:29:26,620
We have a board of directors.

674
00:29:26,620 --> 00:29:29,020
We have investors
who expect an ROI.

675
00:29:29,020 --> 00:29:32,680
We have customers who don't
want to pay for something

676
00:29:32,680 --> 00:29:34,840
unless they really
see added value.

677
00:29:34,840 --> 00:29:38,530
Added cost is one thing, added
value is a different thing.

678
00:29:38,530 --> 00:29:42,940
So where we fit-- just so you
know a little bit about us--

679
00:29:42,940 --> 00:29:48,430
is we're a global cool
chain logistics provider.

680
00:29:48,430 --> 00:29:50,860
Christian Helms is the
CEO and Managing Director.

681
00:29:50,860 --> 00:29:54,070
I'm the Director and I'm also
responsible for global IT

682
00:29:54,070 --> 00:29:55,840
and IS.

683
00:29:55,840 --> 00:30:00,010
We were formed as a new
company in February of 2005.

684
00:30:00,010 --> 00:30:02,860
We announced it through
logistic in Berlin last year.

685
00:30:02,860 --> 00:30:05,080
But our experience goes
far back beyond that.

686
00:30:05,080 --> 00:30:07,390
Christian has been-- he's
built the perishable network

687
00:30:07,390 --> 00:30:10,870
for Kuehne and Nagel, which is
a global logistics provider.

688
00:30:10,870 --> 00:30:12,370
And we work together
with Hellmann.

689
00:30:12,370 --> 00:30:15,220
And at Hellmann, I was
sitting second to the CIO

690
00:30:15,220 --> 00:30:17,860
where we had offices
in 80 countries--

691
00:30:17,860 --> 00:30:20,720
130 offices and 3,500 users.

692
00:30:20,720 --> 00:30:23,680
So a lot of our experience
comes from before this.

693
00:30:23,680 --> 00:30:25,900
This is a new company
that's dedicated

694
00:30:25,900 --> 00:30:28,270
to perishable
temperature-sensitive types

695
00:30:28,270 --> 00:30:29,470
of handling.

696
00:30:29,470 --> 00:30:33,010
In July of 2005, we had a slight
addition to our business plan,

697
00:30:33,010 --> 00:30:36,130
because we acquired a company
that's like a mini-Cisco,

698
00:30:36,130 --> 00:30:36,660
if you will.

699
00:30:36,660 --> 00:30:38,077
It's food distribution
in Germany.

700
00:30:38,077 --> 00:30:40,720
It's very high-end
fresh, gourmet food

701
00:30:40,720 --> 00:30:43,060
that lost 7.5 million
euros last year.

702
00:30:43,060 --> 00:30:45,850
So we had the task of turning
that around very quickly,

703
00:30:45,850 --> 00:30:48,590
which we have in the six months
that we've run it this year.

704
00:30:48,590 --> 00:30:49,855
That's called RUNGIS Express.

705
00:30:49,855 --> 00:30:51,940
It's in Meckenheim Germany.

706
00:30:51,940 --> 00:30:58,690
Our focus really, again, is
providing a global network

707
00:30:58,690 --> 00:31:01,270
of offices that are
dedicated to the specialized

708
00:31:01,270 --> 00:31:03,400
handling of
temperature-sensitive products

709
00:31:03,400 --> 00:31:06,130
with skilled and
experienced people.

710
00:31:06,130 --> 00:31:09,670
Locations are very important,
gateways, what we're doing.

711
00:31:09,670 --> 00:31:12,460
We worked with John Pierre for
the last seven or eight years,

712
00:31:12,460 --> 00:31:13,720
I think it is now.

713
00:31:13,720 --> 00:31:16,780
Back to the days that he wrote
the perishable handling manual

714
00:31:16,780 --> 00:31:18,940
for [INAUDIBLE] when
he was up in Quebec.

715
00:31:18,940 --> 00:31:21,100
And we continue to work
with him very closely

716
00:31:21,100 --> 00:31:23,740
through the
University of Florida.

717
00:31:23,740 --> 00:31:26,950
There's an awful lot of services
that Cool Chain Group does.

718
00:31:26,950 --> 00:31:31,450
And RFID is one of the solutions
sets that we're looking at

719
00:31:31,450 --> 00:31:33,880
to apply to all of the
solutions that we have

720
00:31:33,880 --> 00:31:36,790
with handling these products.

721
00:31:36,790 --> 00:31:38,860
And John Pierre has
really talked a lot

722
00:31:38,860 --> 00:31:41,110
about exactly the same things
I'm going to talk about.

723
00:31:41,110 --> 00:31:45,340
I'm going to look at them from a
more summarized business format

724
00:31:45,340 --> 00:31:48,730
rather than looking at it
from a research, and a design,

725
00:31:48,730 --> 00:31:52,150
and optimization of all
of the different protocols

726
00:31:52,150 --> 00:31:54,190
and everything that's involved.

727
00:31:54,190 --> 00:31:59,380
With the cool chain, we really
need to optimize transit time.

728
00:31:59,380 --> 00:32:03,880
If we can capture a broken
transit point in the supply

729
00:32:03,880 --> 00:32:07,210
chain immediately-- let's
say that a container doesn't

730
00:32:07,210 --> 00:32:10,750
get on a plane, and we don't
know if it's confirmed on board

731
00:32:10,750 --> 00:32:11,470
or not.

732
00:32:11,470 --> 00:32:13,240
If it's sitting
in Miami and it's

733
00:32:13,240 --> 00:32:16,420
on the tarmac at 110 degrees
and it's blackberries

734
00:32:16,420 --> 00:32:18,370
and raspberries,
we don't have very

735
00:32:18,370 --> 00:32:21,483
long to recover it to get it
back inside of a cool chain.

736
00:32:21,483 --> 00:32:22,900
A lot of times,
we don't even know

737
00:32:22,900 --> 00:32:25,450
that until the products
become a product

738
00:32:25,450 --> 00:32:26,590
that you take to the dump.

739
00:32:26,590 --> 00:32:27,965
Or make wine out
of it, if you're

740
00:32:27,965 --> 00:32:29,720
a winemaker, that might work.

741
00:32:29,720 --> 00:32:32,295
So if we actually had the
infrastructure in place

742
00:32:32,295 --> 00:32:34,420
to where we would know
whether or not that actually

743
00:32:34,420 --> 00:32:37,300
got on the aircraft or
not, we could actually

744
00:32:37,300 --> 00:32:40,475
have some dynamic reporting,
exception reporting,

745
00:32:40,475 --> 00:32:42,100
and get to it, and
bring it back to us.

746
00:32:42,100 --> 00:32:45,340
So optimizing transit time
is extremely important.

747
00:32:45,340 --> 00:32:47,830
Finding where there's an
exception to what we anticipate

748
00:32:47,830 --> 00:32:49,830
is going to happen
is very important.

749
00:32:49,830 --> 00:32:52,330
And the other thing is, if we
have automated data collection

750
00:32:52,330 --> 00:32:55,600
at receiving and shipping,
that saves us a lot of time.

751
00:32:55,600 --> 00:32:58,840
If you receive 2,000
or 3,000 containers

752
00:32:58,840 --> 00:33:02,950
of goods off of an LD7 or a
couple of different containers

753
00:33:02,950 --> 00:33:05,010
off of an airline, and
you have trucks backed up

754
00:33:05,010 --> 00:33:06,760
to your warehouse door
honking their horns

755
00:33:06,760 --> 00:33:10,180
because they want to get it, and
you have to receive and enter

756
00:33:10,180 --> 00:33:12,910
every single box, whether
it's by barcode or manually,

757
00:33:12,910 --> 00:33:14,800
you're spending an
awful lot of time.

758
00:33:14,800 --> 00:33:17,620
If you could take that same
product through a portal,

759
00:33:17,620 --> 00:33:19,210
or with a handheld
or anything else

760
00:33:19,210 --> 00:33:22,600
you want to think of, to read
it for a reader configuration,

761
00:33:22,600 --> 00:33:25,510
you really cut down the
receiving time, as well as

762
00:33:25,510 --> 00:33:27,580
finding out if
there's any errors.

763
00:33:27,580 --> 00:33:31,493
The temperature and monitor
problems-- as JP talked about,

764
00:33:31,493 --> 00:33:32,410
that's very important.

765
00:33:32,410 --> 00:33:33,760
You saw what happens
to product when

766
00:33:33,760 --> 00:33:34,885
it's out of the cool chain.

767
00:33:34,885 --> 00:33:36,640
For sensitive
products like berries,

768
00:33:36,640 --> 00:33:38,440
one hour above
temperature equals

769
00:33:38,440 --> 00:33:41,260
one day of lost shelf life.

770
00:33:41,260 --> 00:33:45,050
It's an interesting statement,
because as JP pointed out,

771
00:33:45,050 --> 00:33:47,920
when you looked at the charts
measuring surface temperature

772
00:33:47,920 --> 00:33:49,950
on the outside of
a pallet or a case,

773
00:33:49,950 --> 00:33:52,450
it doesn't necessarily tell you
what the pulp temperature is

774
00:33:52,450 --> 00:33:53,650
of the product.

775
00:33:53,650 --> 00:33:56,997
And when somebody
receives product,

776
00:33:56,997 --> 00:33:59,080
a lot of times if they get
a truckload of product,

777
00:33:59,080 --> 00:34:01,270
they've got one or two
traditional temperature records

778
00:34:01,270 --> 00:34:01,770
in there.

779
00:34:01,770 --> 00:34:04,000
If it's out of control,
they'll reject the load.

780
00:34:04,000 --> 00:34:07,270
Or they'll set it off until
they can do further QC.

781
00:34:07,270 --> 00:34:09,530
And then they either
reject the whole load,

782
00:34:09,530 --> 00:34:11,380
or they don't reject
the whole load

783
00:34:11,380 --> 00:34:13,750
based on what they see on
one or two measuring points.

784
00:34:13,750 --> 00:34:20,190
With RFID, we think we can put
a tag on every single pallet

785
00:34:20,190 --> 00:34:22,170
in different locations
on a pallet, map

786
00:34:22,170 --> 00:34:24,308
the whole container,
and basically

787
00:34:24,308 --> 00:34:26,100
then be able to tell
if some of the pallets

788
00:34:26,100 --> 00:34:28,560
were exposed to a bad
temperature- either too

789
00:34:28,560 --> 00:34:31,380
warm, or too cool, or both
in the same container.

790
00:34:31,380 --> 00:34:33,750
And through research
that JP is doing

791
00:34:33,750 --> 00:34:35,699
at the university
with his team, we

792
00:34:35,699 --> 00:34:38,580
can actually map
what the effect is

793
00:34:38,580 --> 00:34:41,010
for different types of
packaging versus different types

794
00:34:41,010 --> 00:34:44,153
of commodities, as to what
the ambient temperature is,

795
00:34:44,153 --> 00:34:46,320
and how far you go out of
temperature upper or lower

796
00:34:46,320 --> 00:34:48,050
control limit, and
the length of time

797
00:34:48,050 --> 00:34:49,800
that you're out as to
what the effect will

798
00:34:49,800 --> 00:34:51,120
be on the pulp temperature.

799
00:34:51,120 --> 00:34:53,610
So we can start to assign
an awful lot of business

800
00:34:53,610 --> 00:34:56,940
intelligence to being able
to really tell what that's

801
00:34:56,940 --> 00:34:59,640
doing to the product, not just
look at the surface temperature

802
00:34:59,640 --> 00:35:02,160
and make a decision.

803
00:35:02,160 --> 00:35:03,840
Real-time alerts
during transit--

804
00:35:03,840 --> 00:35:05,850
if there is a problem,
then we can have an alert

805
00:35:05,850 --> 00:35:08,910
and go back through cellular or
other types of communication.

806
00:35:08,910 --> 00:35:10,350
Or to the driver,
we can actually

807
00:35:10,350 --> 00:35:13,298
take remedial action
during the transit process

808
00:35:13,298 --> 00:35:15,090
rather than wait until
we open up the doors

809
00:35:15,090 --> 00:35:17,070
and find out that we
have something wrong.

810
00:35:17,070 --> 00:35:19,680
And the shelf-life
predictability--

811
00:35:19,680 --> 00:35:21,360
not just accepting
or rejecting, but

812
00:35:21,360 --> 00:35:23,460
the shelf-life
predictability becomes

813
00:35:23,460 --> 00:35:28,200
much more doable than it is
with current technologies.

814
00:35:28,200 --> 00:35:30,390
Trace and track is a
very important thing

815
00:35:30,390 --> 00:35:31,890
for safety and recall.

816
00:35:31,890 --> 00:35:34,440
Also for tracking, like
with food and other items--

817
00:35:34,440 --> 00:35:36,030
what did we actually
sell the thing

818
00:35:36,030 --> 00:35:37,830
for at the retail
shelf versus what

819
00:35:37,830 --> 00:35:40,530
lot did it come from-- so
we know how to do accounting

820
00:35:40,530 --> 00:35:43,210
and to send the revenues
to the right people.

821
00:35:43,210 --> 00:35:46,770
But that's very pervasive.

822
00:35:46,770 --> 00:35:49,740
That's very time intensive when
you have human people trying

823
00:35:49,740 --> 00:35:52,050
to do all this, and you
have ERP systems that

824
00:35:52,050 --> 00:35:54,420
are trying to take different
receiving and shipping,

825
00:35:54,420 --> 00:35:56,040
and different
logistics packages,

826
00:35:56,040 --> 00:35:57,420
and different airline tracking.

827
00:35:57,420 --> 00:35:59,190
And you try to put
that all together, it

828
00:35:59,190 --> 00:36:02,400
becomes very difficult. If
you're able to do it with RFID,

829
00:36:02,400 --> 00:36:05,610
then it becomes transparent,
and it becomes non-pervasive

830
00:36:05,610 --> 00:36:09,160
or less pervasive to the actual
process of the business flow

831
00:36:09,160 --> 00:36:11,430
so you can actually
start to do that.

832
00:36:11,430 --> 00:36:13,770
Humidity and
atmospheric conditions

833
00:36:13,770 --> 00:36:15,490
are similar to temperature.

834
00:36:15,490 --> 00:36:17,490
Their second level, they're
also very important.

835
00:36:17,490 --> 00:36:19,740
I didn't list any bullets
here because it's really

836
00:36:19,740 --> 00:36:21,210
similar types of
things just using

837
00:36:21,210 --> 00:36:23,340
different types of sensors.

838
00:36:23,340 --> 00:36:25,845
Warehouse and handling
efficiency, we touched on.

839
00:36:25,845 --> 00:36:28,950
Real-time receiving and
shipping and pick detail--

840
00:36:28,950 --> 00:36:33,210
not only do you save time,
but you also gain on accuracy.

841
00:36:33,210 --> 00:36:35,760
With barcodes, sometimes
people will read a box--

842
00:36:35,760 --> 00:36:38,570
they can't read this box, and
they go scan one they can.

843
00:36:38,570 --> 00:36:40,080
Until you get to
the end of the lot,

844
00:36:40,080 --> 00:36:41,700
you don't really know what's
going out the last door.

845
00:36:41,700 --> 00:36:43,700
The last door is whatever
is still in the system

846
00:36:43,700 --> 00:36:44,850
and they ship it.

847
00:36:44,850 --> 00:36:47,010
Warehouse workers that are
responsible for picking

848
00:36:47,010 --> 00:36:50,070
and packing, in many cases,
are very ingenious into how

849
00:36:50,070 --> 00:36:51,400
to get around a system.

850
00:36:51,400 --> 00:36:54,000
And so, good RFID will
handle all of that.

851
00:36:54,000 --> 00:36:56,167
Also, we can do time
studies on which people

852
00:36:56,167 --> 00:36:57,750
are doing their job
better than others

853
00:36:57,750 --> 00:36:59,760
by observing
different activities

854
00:36:59,760 --> 00:37:01,080
throughout different points.

855
00:37:01,080 --> 00:37:05,610
And we can have automated
inventory with bin polling.

856
00:37:05,610 --> 00:37:07,320
So what are our challenges?

857
00:37:07,320 --> 00:37:10,363
We need to be able to establish
measurable baseline costs.

858
00:37:10,363 --> 00:37:12,780
Everybody wants an ROI, but
they don't know what it really

859
00:37:12,780 --> 00:37:14,910
costs them right now.

860
00:37:14,910 --> 00:37:16,410
We don't know in
the business world,

861
00:37:16,410 --> 00:37:18,300
a lot of times, how much
we're really losing.

862
00:37:18,300 --> 00:37:21,210
If Christian was here, he'd give
you all kinds of percentages

863
00:37:21,210 --> 00:37:23,640
and talk about everything
from Marks and Spencers--

864
00:37:23,640 --> 00:37:26,010
which is one of the
best with shelf-life,

865
00:37:26,010 --> 00:37:28,047
not having a lot of
shelf life loss--

866
00:37:28,047 --> 00:37:29,130
to some of the worst ones.

867
00:37:29,130 --> 00:37:32,160
But really, people don't
have a good baseline.

868
00:37:32,160 --> 00:37:33,870
You have to establish
a baseline so

869
00:37:33,870 --> 00:37:36,270
that when you look at an ROI,
you can now actually start

870
00:37:36,270 --> 00:37:38,010
to measure the differences
and know what you're really

871
00:37:38,010 --> 00:37:39,000
coming up with.

872
00:37:39,000 --> 00:37:40,590
And then you've got
a sustainable ROI,

873
00:37:40,590 --> 00:37:42,497
so it's just not a
flash in the pan.

874
00:37:42,497 --> 00:37:44,580
Where you put ROI in, you
run it for three months,

875
00:37:44,580 --> 00:37:47,250
and then you quit doing it.

876
00:37:47,250 --> 00:37:50,880
We need to understand that
not all RFID technology is

877
00:37:50,880 --> 00:37:51,870
EPC RFID.

878
00:37:51,870 --> 00:37:54,120
And there are some
closed applications,

879
00:37:54,120 --> 00:37:58,710
low-frequency temperature tags,
for example, 125 kilohertz,

880
00:37:58,710 --> 00:38:01,290
probably, in some of these
packages with a close read

881
00:38:01,290 --> 00:38:04,870
range could work, whereas,
900 megahertz wouldn't work.

882
00:38:04,870 --> 00:38:07,920
And so, we have to really
figure out what fits where.

883
00:38:07,920 --> 00:38:10,410
But still, as we
gain experience,

884
00:38:10,410 --> 00:38:12,510
then we need to move
towards how do we get this

885
00:38:12,510 --> 00:38:14,250
all into a standardized
infrastructure

886
00:38:14,250 --> 00:38:15,958
so that we don't have
high infrastructure

887
00:38:15,958 --> 00:38:18,858
costs for different
types of frequencies.

888
00:38:18,858 --> 00:38:20,400
We have to have tags
that are applied

889
00:38:20,400 --> 00:38:21,817
at the first stage
of the process.

890
00:38:21,817 --> 00:38:24,150
Because it's much
harder to cost justify

891
00:38:24,150 --> 00:38:28,020
applying tags midstream in any
logistic process, or bar code,

892
00:38:28,020 --> 00:38:30,120
or any other type of labeling.

893
00:38:30,120 --> 00:38:32,370
So it has to really start
at the beginning of whatever

894
00:38:32,370 --> 00:38:35,400
the process is so that we can
utilize the benefit of that

895
00:38:35,400 --> 00:38:38,520
through the whole supply chain.

896
00:38:38,520 --> 00:38:40,530
Right now, read accuracy
is a big problem.

897
00:38:40,530 --> 00:38:43,320
If we've got 95% of the data,
it's really not good enough.

898
00:38:43,320 --> 00:38:46,560
If we have to verify what's on
there through traditional means

899
00:38:46,560 --> 00:38:49,380
of measurement or
identification,

900
00:38:49,380 --> 00:38:51,780
then RFID is just an extra
layer that's not really

901
00:38:51,780 --> 00:38:53,268
displacing that other cost.

902
00:38:53,268 --> 00:38:54,810
And it's getting
better all the time.

903
00:38:54,810 --> 00:38:57,422
Some things are going to
be close enough to 100%,

904
00:38:57,422 --> 00:38:59,130
I think, to where we
can start using them

905
00:38:59,130 --> 00:39:01,306
pretty rapidly in real time.

906
00:39:01,306 --> 00:39:03,270
The infrastructure
has to be affordable,

907
00:39:03,270 --> 00:39:05,582
maintainable through
the whole supply chain.

908
00:39:05,582 --> 00:39:08,040
Listening to some of the things
you thought about with cost

909
00:39:08,040 --> 00:39:10,260
of readers early on, where
they're not there yet,

910
00:39:10,260 --> 00:39:11,040
I agree with it.

911
00:39:11,040 --> 00:39:13,867
You know, electronics
normally come down in cost.

912
00:39:13,867 --> 00:39:15,450
I'm sure the readers
haven't come down

913
00:39:15,450 --> 00:39:17,520
as much as we
expected them to yet.

914
00:39:17,520 --> 00:39:20,250
But as compared to moving
parts or scanners with lasers,

915
00:39:20,250 --> 00:39:22,830
definitely, the
electronic reader for RFID

916
00:39:22,830 --> 00:39:25,170
is going to be far less
to install and maintain

917
00:39:25,170 --> 00:39:27,990
than anything with any
mechanical moving parts in it.

918
00:39:27,990 --> 00:39:29,525
But it has to be
there, and it has

919
00:39:29,525 --> 00:39:30,900
to be affordable
and maintainable

920
00:39:30,900 --> 00:39:33,570
through the whole supply chain.

921
00:39:33,570 --> 00:39:35,800
We should approach the
container level, pallet level,

922
00:39:35,800 --> 00:39:39,270
and item level, and sensor
level independently.

923
00:39:39,270 --> 00:39:43,780
Sometimes people try to put
together the total solution.

924
00:39:43,780 --> 00:39:45,630
And you never finish
what you start.

925
00:39:45,630 --> 00:39:47,610
If we break it down
into pieces, and that's

926
00:39:47,610 --> 00:39:48,985
what we're trying
to do with some

927
00:39:48,985 --> 00:39:51,150
of our pilots and our
tests, we can actually

928
00:39:51,150 --> 00:39:52,950
do something, gain
some experience,

929
00:39:52,950 --> 00:39:55,660
and actually get an ROI on
different pieces of that

930
00:39:55,660 --> 00:40:00,180
and then work towards building
a complete infrastructure

931
00:40:00,180 --> 00:40:02,100
where everything works together.

932
00:40:02,100 --> 00:40:04,450
And we have to find
ways to lower costs,

933
00:40:04,450 --> 00:40:06,660
even with the
current technology.

934
00:40:06,660 --> 00:40:08,850
And it's a lot easier
to justify on a pallet

935
00:40:08,850 --> 00:40:12,210
basis or a container
basis than it is on a case

936
00:40:12,210 --> 00:40:13,710
basis or a level basis.

937
00:40:13,710 --> 00:40:17,340
So we're focusing on
that on the front end.

938
00:40:17,340 --> 00:40:19,860
What are some of the
concepts, some of the pilots

939
00:40:19,860 --> 00:40:22,030
that we're thinking about?

940
00:40:22,030 --> 00:40:25,350
Well, one of the things is
to identify the right clients

941
00:40:25,350 --> 00:40:26,910
and commodities
where we can actually

942
00:40:26,910 --> 00:40:30,443
put RFID tags onto cases
when they produce them,

943
00:40:30,443 --> 00:40:32,610
when they label them, so
we can run them all the way

944
00:40:32,610 --> 00:40:34,680
through the supply chain.

945
00:40:34,680 --> 00:40:37,320
And we can read
them and not fight

946
00:40:37,320 --> 00:40:39,600
the commodities that
have poor read rates,

947
00:40:39,600 --> 00:40:43,020
or the commodities that have
very little profit in them

948
00:40:43,020 --> 00:40:43,980
or very low value.

949
00:40:43,980 --> 00:40:45,653
Because nobody is
going to accept that.

950
00:40:45,653 --> 00:40:47,070
So we want to get
to where there's

951
00:40:47,070 --> 00:40:50,640
a low threshold for adoption
for the concept in general,

952
00:40:50,640 --> 00:40:54,150
and for a reasonable
expectation of an ROI,

953
00:40:54,150 --> 00:40:55,920
so that we can go
ahead and do the pilots

954
00:40:55,920 --> 00:40:58,625
with those types of commodities
and those types of customers.

955
00:40:58,625 --> 00:41:00,000
Then we'll determine
the accuracy

956
00:41:00,000 --> 00:41:02,208
of the technology and the
impact on the process flow.

957
00:41:02,208 --> 00:41:04,590
Do we really speed the
actual process flow up,

958
00:41:04,590 --> 00:41:07,410
or do we slow it down?

959
00:41:07,410 --> 00:41:11,310
We want to find carriers to test
RFID tags on container level.

960
00:41:11,310 --> 00:41:13,890
And it's not traditionally RFID.

961
00:41:13,890 --> 00:41:17,745
It may just be RF with other
types of communication devices.

962
00:41:17,745 --> 00:41:19,620
A lot of ocean containers
have modems in them

963
00:41:19,620 --> 00:41:20,730
now, with the gensets.

964
00:41:20,730 --> 00:41:22,272
And there's a lot
of things that they

965
00:41:22,272 --> 00:41:26,280
do where we can start to
slowly purvey that with RFID

966
00:41:26,280 --> 00:41:28,620
as we think of it
here, so that we

967
00:41:28,620 --> 00:41:29,870
can start tracking containers.

968
00:41:29,870 --> 00:41:31,453
But we have to have
the infrastructure

969
00:41:31,453 --> 00:41:32,640
throughout the supply chain.

970
00:41:32,640 --> 00:41:37,350
And it may be that we can
find an ROI just from point G

971
00:41:37,350 --> 00:41:42,030
to point K, and not from A
to Z. We don't necessarily

972
00:41:42,030 --> 00:41:44,190
have to try to do
the complete chain.

973
00:41:44,190 --> 00:41:46,920
And again, look at what the
accuracy of the technology

974
00:41:46,920 --> 00:41:49,650
is, the impact on the
process flow, and then

975
00:41:49,650 --> 00:41:51,900
we establish our ROI.

976
00:41:51,900 --> 00:41:57,060
And the final thing is talking
about temperature mapping,

977
00:41:57,060 --> 00:41:58,920
where we're talking
about whether it's

978
00:41:58,920 --> 00:42:01,560
on a case level or a pallet
level, like I said earlier.

979
00:42:01,560 --> 00:42:03,320
If we can map that
whole container

980
00:42:03,320 --> 00:42:05,310
or that whole truckload,
we can actually

981
00:42:05,310 --> 00:42:07,890
start getting a lot
more information.

982
00:42:07,890 --> 00:42:09,810
And then we can take
business intelligence

983
00:42:09,810 --> 00:42:12,030
that we build into
the final application,

984
00:42:12,030 --> 00:42:15,180
and we can say, all right, now
we're going to accept or reject

985
00:42:15,180 --> 00:42:16,470
this load.

986
00:42:16,470 --> 00:42:19,560
These pallets have a longer
shelf life on the same load

987
00:42:19,560 --> 00:42:20,910
than these pallets do.

988
00:42:20,910 --> 00:42:23,760
So instead of doing FIFO, or
first-in first-out inventory

989
00:42:23,760 --> 00:42:25,470
management, we're
going to actually start

990
00:42:25,470 --> 00:42:27,630
doing shelf-life
inventory management.

991
00:42:27,630 --> 00:42:29,160
Instead of sending
the stuff that's

992
00:42:29,160 --> 00:42:32,370
been stressed by
temperature across the state

993
00:42:32,370 --> 00:42:34,980
or whatever the longest
distance from this DC is,

994
00:42:34,980 --> 00:42:37,230
we're going to send it to
the store across the street.

995
00:42:37,230 --> 00:42:39,272
We're going to send the
stuff with the most legs,

996
00:42:39,272 --> 00:42:42,840
or the most shelf life left
on it, to the father points.

997
00:42:42,840 --> 00:42:45,990
And so, by mapping
the complete trailer,

998
00:42:45,990 --> 00:42:48,040
we can start to get
much better experience.

999
00:42:48,040 --> 00:42:49,665
And even though our
temperatures aren't

1000
00:42:49,665 --> 00:42:52,350
going to be exact
comparing surface to pulp,

1001
00:42:52,350 --> 00:42:54,760
we're going to get a lot
better than we are today.

1002
00:42:54,760 --> 00:42:56,340
When I sit down
and talk to people

1003
00:42:56,340 --> 00:42:59,760
who are very technical and
very accurate in their analysis

1004
00:42:59,760 --> 00:43:02,430
of this, they're saying, oh,
we can't do all these things.

1005
00:43:02,430 --> 00:43:05,070
And I laugh and I say, well,
what are we doing today?

1006
00:43:05,070 --> 00:43:07,650
We have two temperature,
or one temperature recorder

1007
00:43:07,650 --> 00:43:09,252
in the truck that's
not accurate.

1008
00:43:09,252 --> 00:43:10,710
And half the time,
the truck driver

1009
00:43:10,710 --> 00:43:13,382
throws it away if
he has a problem.

1010
00:43:13,382 --> 00:43:15,840
Anything we do is going to be
a gross improvement over what

1011
00:43:15,840 --> 00:43:16,660
we've got today.

1012
00:43:16,660 --> 00:43:20,110
So let's go ahead and get the
experience and take it forward.

1013
00:43:20,110 --> 00:43:20,860
So that's it.

1014
00:43:20,860 --> 00:43:23,790
I think I just really wanted
to give you a brief overview

1015
00:43:23,790 --> 00:43:25,985
from our perspective.

1016
00:43:25,985 --> 00:43:27,360
You know, we're
not a big company

1017
00:43:27,360 --> 00:43:28,500
that's going to be
an industry leader,

1018
00:43:28,500 --> 00:43:29,460
investing a lot of money.

1019
00:43:29,460 --> 00:43:30,630
But we're also not
a company that's

1020
00:43:30,630 --> 00:43:32,310
going to wait and see
what everybody else does.

1021
00:43:32,310 --> 00:43:33,560
We're somewhere in the middle.

1022
00:43:39,268 --> 00:43:40,560
PROFESSOR: Thank you very much.

1023
00:43:40,560 --> 00:43:42,930
Questions, if you could come
down to the microphones,

1024
00:43:42,930 --> 00:43:44,820
please.

1025
00:43:44,820 --> 00:43:46,860
One question, we
actually had a student

1026
00:43:46,860 --> 00:43:48,510
working on this last year.

1027
00:43:48,510 --> 00:43:50,610
And the question came
up around sampling rates

1028
00:43:50,610 --> 00:43:53,970
and how significant that was
in the temperature of business.

1029
00:43:53,970 --> 00:43:57,750
I mean, if we take a read event
and then we have some kind

1030
00:43:57,750 --> 00:44:00,510
of telemetry stream-- so this
would be characteristic of any

1031
00:44:00,510 --> 00:44:01,230
sensor--

1032
00:44:01,230 --> 00:44:04,290
at what point do you marry that
up, per an earlier question

1033
00:44:04,290 --> 00:44:06,690
today, with your XML
representation of the read

1034
00:44:06,690 --> 00:44:07,290
event?

1035
00:44:07,290 --> 00:44:09,850
And how often do
you need to do that?

1036
00:44:09,850 --> 00:44:12,030
Clearly a different
time cycle than what

1037
00:44:12,030 --> 00:44:16,100
we're doing in the ALE filtering
of the actual EPC read event.

1038
00:44:16,100 --> 00:44:20,850
I would be interested in your
experiences in that regard.

1039
00:44:20,850 --> 00:44:26,410
MICHAEL NICOMETO: I think,
from a temperature perspective,

1040
00:44:26,410 --> 00:44:29,110
what we're really interested
in are two things.

1041
00:44:29,110 --> 00:44:31,080
The most obvious one
is whether we're out

1042
00:44:31,080 --> 00:44:33,190
of an upper or
lower control limit.

1043
00:44:33,190 --> 00:44:34,590
And if we are, we
want to measure

1044
00:44:34,590 --> 00:44:36,630
all of the events that
happened once we go out

1045
00:44:36,630 --> 00:44:38,200
of the control limit.

1046
00:44:38,200 --> 00:44:42,420
So the relationship
is going to be

1047
00:44:42,420 --> 00:44:45,630
that we have a certain
amount of temperature

1048
00:44:45,630 --> 00:44:48,060
variation over a
certain amount of time

1049
00:44:48,060 --> 00:44:51,790
that's going to affect the pulp
temperature of that product.

1050
00:44:51,790 --> 00:44:54,300
A quick rise in surface
temperature and a drop

1051
00:44:54,300 --> 00:44:57,510
probably isn't going to affect
the pulp temperature very much.

1052
00:44:57,510 --> 00:45:01,560
We can actually-- at this
level of the analysis-- say,

1053
00:45:01,560 --> 00:45:04,260
we're going to ignore all
temperature readings that

1054
00:45:04,260 --> 00:45:06,220
are in the temperature range.

1055
00:45:06,220 --> 00:45:09,015
So we can actually say we want
a temperature recorder that's

1056
00:45:09,015 --> 00:45:11,140
going to make sure we're
within our control limits.

1057
00:45:11,140 --> 00:45:14,910
As long as we are, don't use the
memory on the tag or the module

1058
00:45:14,910 --> 00:45:15,745
to store any data.

1059
00:45:15,745 --> 00:45:17,370
The only time we want
to store the data

1060
00:45:17,370 --> 00:45:18,990
is once you go out of limit.

1061
00:45:18,990 --> 00:45:20,610
We may even say we
only want to store

1062
00:45:20,610 --> 00:45:23,980
certain incremental adjustments
over a certain amount of time

1063
00:45:23,980 --> 00:45:27,040
so we can then predict what
the effect has been on it.

1064
00:45:27,040 --> 00:45:31,050
However, at another level, if
we can get the proper memory

1065
00:45:31,050 --> 00:45:34,920
storage and also the
readability within a bandwidth--

1066
00:45:34,920 --> 00:45:37,230
like when we come
through a portal--

1067
00:45:37,230 --> 00:45:41,400
it would be nice to know
how much variations there

1068
00:45:41,400 --> 00:45:44,050
has been within the limits,
within the upper-lower control

1069
00:45:44,050 --> 00:45:44,550
limit.

1070
00:45:44,550 --> 00:45:46,717
Because you have to make
some judgments when you set

1071
00:45:46,717 --> 00:45:47,760
those control limits.

1072
00:45:47,760 --> 00:45:50,790
And a variation of temperature
even within the limits

1073
00:45:50,790 --> 00:45:52,980
affects the quality
of some products.

1074
00:45:52,980 --> 00:45:54,555
And so, it depends
on where we're at.

1075
00:45:54,555 --> 00:45:57,180
But again, when we compare it to
what traditional equipment is,

1076
00:45:57,180 --> 00:45:59,100
anything we do is better.

1077
00:45:59,100 --> 00:46:02,970
And so, it's all a matter
of, what's the right balance?

1078
00:46:02,970 --> 00:46:05,250
And if we have
10,000 data points,

1079
00:46:05,250 --> 00:46:06,780
and we're coming
through a portal,

1080
00:46:06,780 --> 00:46:10,710
and we're reading a pallet, then
the first thing we have to do

1081
00:46:10,710 --> 00:46:14,400
is be able to read whether or
not there's an alert situation.

1082
00:46:14,400 --> 00:46:16,100
So we're looking
at, how do we set--

1083
00:46:16,100 --> 00:46:17,850
if there is some type
of an alert-- how do

1084
00:46:17,850 --> 00:46:20,473
we set the profile for this
commodity on his pallet

1085
00:46:20,473 --> 00:46:22,140
so that when we come
through the portal,

1086
00:46:22,140 --> 00:46:24,930
we only have to read enough data
to know if there's a problem.

1087
00:46:24,930 --> 00:46:27,660
If we try to read all of
the data with the bandwidth

1088
00:46:27,660 --> 00:46:30,155
that we have available
through RFID today,

1089
00:46:30,155 --> 00:46:31,530
with that many
storage points, we

1090
00:46:31,530 --> 00:46:34,958
don't have enough time as the
[INAUDIBLE] comes through.

1091
00:46:34,958 --> 00:46:36,500
GUEST SPEAKER: Well,
maybe I can just

1092
00:46:36,500 --> 00:46:38,760
say a work note, as it were.

1093
00:46:38,760 --> 00:46:40,040
Yes.

1094
00:46:40,040 --> 00:46:42,590
One thing that I just want
to say is that, of course,

1095
00:46:42,590 --> 00:46:44,630
if you get the temperature
of the tag, what

1096
00:46:44,630 --> 00:46:47,970
is outside the package, it's
kind of a problem, an issue

1097
00:46:47,970 --> 00:46:48,470
right now.

1098
00:46:48,470 --> 00:46:50,750
Because if I take the
temperature of my product

1099
00:46:50,750 --> 00:46:54,260
inside and I do regular
data acquisition,

1100
00:46:54,260 --> 00:46:55,490
I don't take that much data--

1101
00:46:55,490 --> 00:46:58,070
10 minutes, 15 minutes,
20 minutes interval.

1102
00:46:58,070 --> 00:47:00,950
That's way enough,
because the lag

1103
00:47:00,950 --> 00:47:03,560
that the time that it takes to
change temperature of a product

1104
00:47:03,560 --> 00:47:04,730
is pretty slow.

1105
00:47:04,730 --> 00:47:06,890
But if I take on the
outside of my package,

1106
00:47:06,890 --> 00:47:08,420
we have a big issue like that.

1107
00:47:08,420 --> 00:47:10,520
Because if I take
every 20 minutes,

1108
00:47:10,520 --> 00:47:13,880
my picture can look much
worse than it is in fact.

1109
00:47:13,880 --> 00:47:16,580
So this is what, as Mike said.

1110
00:47:16,580 --> 00:47:21,150
Sometimes we try to work on
the range and fine tune that.

1111
00:47:21,150 --> 00:47:25,320
But don't think that it's a
ton of data, to be honest.

1112
00:47:25,320 --> 00:47:28,160
Only a few per hour would
be way enough in our case.

1113
00:47:30,918 --> 00:47:32,460
PROFESSOR: Well,
thank you very much.

1114
00:47:32,460 --> 00:47:34,070
Oh, we have a question.

1115
00:47:34,070 --> 00:47:35,610
AUDIENCE: Just out
of curiosity, I

1116
00:47:35,610 --> 00:47:38,160
always wonder, if you
have a rule saying

1117
00:47:38,160 --> 00:47:42,600
a product has to be within
the range of 60 to 90 degrees

1118
00:47:42,600 --> 00:47:46,840
in the distribution network,
what happens if it's 59 or 81?

1119
00:47:46,840 --> 00:47:49,710
do you really care?

1120
00:47:49,710 --> 00:47:51,270
GUEST SPEAKER: What
kind of product?

1121
00:47:51,270 --> 00:47:53,437
AUDIENCE: I don't know,
just any perishable product.

1122
00:47:53,437 --> 00:47:54,390
You have this rule.

1123
00:47:54,390 --> 00:47:56,190
If I encode it in my system--

1124
00:47:56,190 --> 00:47:58,410
GUEST SPEAKER: If
it's in the pharma,

1125
00:47:58,410 --> 00:48:01,900
by some regulation, if it goes
out of the range, it's out.

1126
00:48:01,900 --> 00:48:04,370
You can not use it at all.

1127
00:48:04,370 --> 00:48:07,290
The reason why it's going
like that is because--

1128
00:48:07,290 --> 00:48:11,100
we cannot prove it-- but if
you have a time and temperature

1129
00:48:11,100 --> 00:48:14,640
relationship, we may be
more able to release some

1130
00:48:14,640 --> 00:48:18,355
of the product that has been out
of range because we can prove

1131
00:48:18,355 --> 00:48:20,730
that it didn't affect the
temperature of the product very

1132
00:48:20,730 --> 00:48:22,260
much at that time.

1133
00:48:22,260 --> 00:48:25,710
In terms of food, you have
some thresholds sometimes.

1134
00:48:25,710 --> 00:48:27,660
Some products like
fruit and vegetables,

1135
00:48:27,660 --> 00:48:30,750
if you reach a certain
threshold, it's gone.

1136
00:48:30,750 --> 00:48:33,300
So I'm talking about
freezing products.

1137
00:48:33,300 --> 00:48:35,730
Some products are like
minus 1, minus 1.5.

1138
00:48:35,730 --> 00:48:37,840
If you go down over
that, it's over.

1139
00:48:37,840 --> 00:48:40,440
So sometimes, some of the ranges
are pretty precise that you

1140
00:48:40,440 --> 00:48:41,375
have to be there.

1141
00:48:41,375 --> 00:48:44,380
AUDIENCE: Yeah, just
I'm the systems person.

1142
00:48:44,380 --> 00:48:46,020
I'm wondering if it
would be beneficial

1143
00:48:46,020 --> 00:48:48,720
if I put in my system,
say, it's perishable,

1144
00:48:48,720 --> 00:48:51,540
but if confidence is 90%,
does that help at all?

1145
00:48:51,540 --> 00:48:52,540
GUEST SPEAKER: Oh, yeah.

1146
00:48:52,540 --> 00:48:53,250
AUDIENCE: OK.

1147
00:48:53,250 --> 00:48:56,160
Yeah, sometimes those magic
numbers are hard to explain.

1148
00:48:56,160 --> 00:48:57,713
It's 90, the absolute
magic number?

1149
00:48:57,713 --> 00:48:59,130
GUEST SPEAKER: And
the worst thing

1150
00:48:59,130 --> 00:49:01,380
is that we are working
with biological products.

1151
00:49:01,380 --> 00:49:03,150
And all of them
have an attitude.

1152
00:49:03,150 --> 00:49:04,570
So they all behave differently.

1153
00:49:04,570 --> 00:49:07,740
So of course, we learn enough
after a while doing research

1154
00:49:07,740 --> 00:49:12,300
that the precision is something
that you have to take slightly

1155
00:49:12,300 --> 00:49:14,910
sometimes, because the
variation is pretty wide.

1156
00:49:14,910 --> 00:49:17,065
But we know some
guidelines, at least,

1157
00:49:17,065 --> 00:49:18,907
that you have to
be in this time.

1158
00:49:18,907 --> 00:49:20,490
MICHAEL NICOMETO:
In addition to that,

1159
00:49:20,490 --> 00:49:22,382
we've got some
conditions that we

1160
00:49:22,382 --> 00:49:24,090
don't know about the
product at the point

1161
00:49:24,090 --> 00:49:25,500
that we start to
measure temperature.

1162
00:49:25,500 --> 00:49:27,420
For example, what are
the harvest conditions?

1163
00:49:27,420 --> 00:49:29,820
Was it rainy for
one week beforehand?

1164
00:49:29,820 --> 00:49:30,660
Was it dry?

1165
00:49:30,660 --> 00:49:34,390
All of that affects the
beginning life of the product.

1166
00:49:34,390 --> 00:49:37,800
And so, what we're measuring
when we measure the product is,

1167
00:49:37,800 --> 00:49:40,260
we're measuring what's
the optimal conditions.

1168
00:49:40,260 --> 00:49:43,140
And this is where I was talking
about some of the software

1169
00:49:43,140 --> 00:49:45,842
that John Pierre's team
is working on developing.

1170
00:49:45,842 --> 00:49:47,550
It's giving you exactly
the type of thing

1171
00:49:47,550 --> 00:49:49,660
of that you're talking
about, which says,

1172
00:49:49,660 --> 00:49:51,120
well, if something's
out of limit,

1173
00:49:51,120 --> 00:49:53,550
we don't just reject it other
than in case of controlled

1174
00:49:53,550 --> 00:49:55,470
substances or pharmaceuticals.

1175
00:49:55,470 --> 00:49:59,790
But if something is out of limit
for a short period of time,

1176
00:49:59,790 --> 00:50:01,140
what was the out of limit?

1177
00:50:01,140 --> 00:50:03,720
You know, so it's a time
and temperature factor.

1178
00:50:03,720 --> 00:50:06,367
How far out of
temperature, and how long

1179
00:50:06,367 --> 00:50:07,950
was it out of
temperature, that really

1180
00:50:07,950 --> 00:50:10,680
makes that a
predictable situation.

1181
00:50:10,680 --> 00:50:12,450
And again, if it's
food products,

1182
00:50:12,450 --> 00:50:14,490
we still have some other
uncontrollable things

1183
00:50:14,490 --> 00:50:15,570
that are there.

1184
00:50:15,570 --> 00:50:17,410
AUDIENCE: Thank you.

1185
00:50:17,410 --> 00:50:20,060
PROFESSOR: Well, thank you
very much to the panel.