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SONIA: Let's turn now to Nvidia who
brings us this next story from one of their customers.
The Archer-Daniels-Midland Company, or ADM,
is a nutrition leader
powering many of the world's top food,
beverage, health, and wellness brands.
Partnering with manufacturing data
platform Sight Machine,
ADM has developed a new tool that uses
Nvidia GPU technology hosted on
Microsoft Azure to organize data and speed
up AI led transformation across ADMs factory floors.
Let's learn more from the
ADM Principal Analytics Engineer,
Isaac Himanga. Isaac, hello. Welcome.
ISAAC HIMANGA: Hello, Sonia. Thanks for
having me on the program.
SONIA: Hey, excited you are here.
Awesome. Let's get started.
What challenge is ADM trying to solve
and how are Sight Machine
and Nvidia helping you in this process?
ISAAC HIMANGA: ADM manufacturing
facilities around
the globe are working to transform
natural products into complete portfolio and
ingredients and flavors for foods and beverages,
supplements, nutrition for pets and livestock and more.
In order to give us and our customers
a competitive advantage,
we use data to make
better decisions at every part
of our global supply chain,
including at our manufacturing locations.
Many techniques and products
exist that use machine learning
and AI to uncover value in our processing facilities.
Some identify when to take equipment offline
for maintenance prior to unscheduled downtime,
other models find opportunities to save money,
reduce energy, or increase yield.
In theory, these models can
be ran against our entire dataset,
but in reality there's a lot of contextual information
and limitations that must be taken
into account for every use case.
A machine learning model might
think it's possible to save steam in
a process by reducing a temperature set point,
but perhaps reducing that temperature set point would
increase the likelihood of making out a spec product.
Similarly, models need to understand what parts of
a process we can
change and which ones we can only react to.
If a unit operation exists to
process the byproduct of another process,
we might not be able to speed up or slow down that line,
even if an analysis of that system
indicates it would be cost effective to do so.
Setting these techniques loose on
a data set just results in
an enormous amount of work for a subject matter expert
familiar with that process
to sift through the model outputs
and recommendations and find the real opportunities.
We need to tell these systems where
the boundaries are and to do that,
we need to find data points
that represent those boundaries.
Despite the countless opportunities to apply
new and innovative analytics and modeling techniques,
we found that all of these are at
best significantly hindered or more
often rendered ineffective unless they're
given data that has at least a minimum amount of
curation with hundreds of thousands of applications
each with tens or thousands
of potentially important variables,
this step is paralyzing.
Our challenge is getting data ready for
AI by tying tags to
specific equipment properties after
repeatedly demonstrating that garbage
in leads to garbage out,
we need to get our data organized so
models give us useful information.
We have shown that doing some pre-work
significantly speeds up projects and improves returns,
but we need to do that at scale
and we need to do it fast.
Asking people at a plant
to manually categorize tags or their own data,
it's a worthwhile effort,
and while it provides significant value,
it would require an enormous amount of time and effort
to categorize our millions of tags around the globe.
We need to move faster.
Now, that brings us to Sight Machine
and their project to speed up
the process of identifying
and categorizing and process data.
The idea is to use a combination of techniques to group
tags together using both meta and payload data,
and to use those groups to guide
subject matter experts through filling
out a hierarchy of process equipment.
If an SME is asked to find the feed flow from a list of
20 tags with the relevant information
right at their fingertips,
they can move much faster than
needing to manually search for that tag.
This will let our experts focus on
more important tasks and expedite the roll out
of all initiatives that
leverage our data to improve margins,
operate more sustainably, reduce
our own environmental footprint,
and to produce products with the best quality.
SONIA: Awesome. How's the process
going right now?
How's it unfolding? Status update?
ISAAC HIMANGA: We're currently working with
Sight Machine to test their system.
It's under development using Nvidia hardware and
software and it's hosted on Microsoft infrastructure.
It automatically groups tags
in a way that can be quickly
interpreted by subject matter experts at each location.
We're working with about three quarter of a million tags
from five locations right now,
but we're really excited to see how we can apply this to
our entire set of manufacturing data.
The focus today is making
a user interface that it would have easy to
use and of course, fast and efficient.
SONIA: Great. What's next for ADM
with the solution?
ISAAC HIMANGA: Our current trial should
wrap up this summer.
No doubt there's going to be fine tuning of
the results and future iterations,
but we look forward to using this
new technology to prepare
our data for ML and AI algorithms that will
continue to give us a competitive advantage
by rapidly and accurately using data to drive decisions.
We're really excited to see how Sight Machine,
Nvidia, and Microsoft can help us do that.
SONIA: Awesome. We're
excited to see it too.
I think that's all the time we have for today.
Thank you so much, Isaac, for joining us.
ISAAC HIMANGA: Yes, thank you.
SONIA: Awesome.
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