This document summarizes a webinar presented by Hannah Testani, CEO of Intelligent Audit, on how companies can transform their supply chains through digitization and decision making. The webinar discusses how disparate systems and a lack of centralized data makes it difficult for companies to measure and improve their supply chain processes. It also presents case studies where Intelligent Audit's machine learning tools helped companies detect anomalies in shipping costs, identify issues causing late deliveries, and prevent costly errors by flagging abnormal shipment weights. The webinar argues that digitizing supply chain data allows companies to make more informed, data-driven decisions that improve operations and the customer experience.

1. DIGITIZATION FOR
DECISION-MAKING
with Hannah Testani, CEO, Intelligent Audit
HOW TO TRANSFORM
YOUR SUPPLY CHAIN
October 18, 2022
11:00am PDT, 2:00pm EDT, 7:00pm BST
Tara Dwyer
Webinar Coordinator,
Supply Chain Brief

2. Avetta provides a cloud-based supply chain risk management and
commercial marketplace platform. Our global solution is uniquely
designed to connect the world’s leading organizations with
qualified suppliers, driving sustainable growth. We build
trustworthy bonds through responsive technology and human
insight. Our process is collaborative. Our global reach is
complemented by our local expertise. Hundreds of global
organizations depend on Avetta to align their supply chains to
sustainable business practices, worldwide. Discover more at
avetta.com.

3. TO USE YOUR TELEPHONE:
You must select "Use Telephone" after joining
and call in using the numbers below.
United States: +1 (415) 655-0052
Access Code: 979-551-452
Audio PIN: Shown after joining the webinar
TO USE YOUR COMPUTER'S AUDIO:
When the webinar begins, you will be connected to audio using your
computer's microphone and speakers (VoIP). A headset is recommended.
Click on the Questions panel to
interact with the presenters

4. HOW TO TRANSFORM YOUR SUPPLY CHAIN:
DIGITIZATION FOR DECISION-MAKING
Hannah Testani
CEO of Intelligent Audit

5. Today’s Supply Chain
Environment
Disparate
systems
No source
of truth
How do we make
it actionable?
Data
Overload
Reliance on
Excel
Can’t improve
what can’t be
measured

6. Rutgers MBA Program
Scope
6 groups of students
2 problem statements
25 minutes
Prepare presentation
Aimed at senior execs
Problem
Statement
1
Problem
Statement
2
Group A:
Raw, noisy
data
Group B:
Mostly clean
Excel data
Group C:
IA proprietary
application
Group A:
Raw, noisy
data
Group B:
Mostly clean
Excel data
Group C:
IA proprietary
application

7. Problem Statement 2
ABC Retail is getting flooded with escalations that
customers aren’t receiving shipments on time
How many shipments were delivered late?
What are the top 5 issues driving the exceptions?
Who can get the issues fixed between the
carrier, shipper and end consumer?
Problem Statement 1
ABC Retail is looking to reduce their
carbon footprint and transportation costs
Which expedited shipments could have
been delivered ground in the same time
or faster?
How much money would they have
saved, broken down by service?
How would it have reduced the carbon
footprint?
The Challenge
©Intelligent Audit 2022 Confidential. All
Rights Reserved.

8. The Results
Group C:
IA proprietary
application
Collaborative, eager
Got answers in a couple of
minutes, so looked for other
opportunities
Presented findings that far
exceeded expectations
These groups were all
empowered with the right tools
to truly make an impact.
The groups were mostly accurate,
but it took a lot of time and they
had to rely heavily on the “gurus.”
Group B:
Mostly clean
Excel data
More collaborative and excited
Team made some assumptions,
but mostly accurate
Didn’t have time to make a
presentation, but did have the
answers
Group A:
Raw, noisy
Excel data
Extremely frustrated
Experienced Excel crash
Team began to make assumptions
No one wanted to present results
No one likes to fail, and these
groups didn’t have the tools
they needed to solve the problem.

9. Looking Ahead
Understanding what KPIs
to measure
Transforming raw, noisy data
into actionable intelligence
Improving your company’s
bottom line
Leverage machine learning
Bettering your end customer
experience
Take the emotion out of
decision making

10. What Happened:
A handful of new
parcel shipper
accounts had their
cost per shipment
spike by over 250%
Case Study 1
Shipping Company Moves
Toward E-commerce
IA’s proprietary deep learning models detected this anomaly, so the client quickly changed their
shipping settings for these SKUs, offering customers to either pick up in store or accept an
additional shipping charge that was transparent to them if they selected to have it delivered by a
parcel carrier.
How Machine Learning Helped
Our customer launched a new e-commerce initiative for a newly acquired line of business. The
packaging they were using for a handful of SKUs exceeded the large package and additional handling
dimensions, so they were getting slammed with fees they hadn’t budgeted for.

11. Case Study 2
Bad Behavior
(Wrong Carrier Selected)
What Happened:
Cost per shipment
spiked 50X for one
day for a lane that’s
infrequently used
Our technology was able to quickly detect this behavior and rise it to the attention of the logistics and
procurement teams to determine who was responsible for the issue. With proper training and
awareness, we were able to quickly mitigate what would have become a much bigger issue, because
they were preparing to ship again with the same carrier.
How Machine Learning Helped
A global manufacturer with a robust procurement team had developed a routing guide with
contracted carriers. IA’s proprietary deep learning model detected anomalous behavior for a new
carrier that was 50X more expensive than any of the five agreed-upon rates for the lane.

12. Case Study 3
TMS Issues
What Happened:
Someone miskeyed
hundreds of shipments
as 500 lbs vs. 5.00.
As a result of the
“excess weight,” the
wrong carrier and
transportation mode
was selected
How Machine Learning Helped
Our client onboarded a new set of team members at one of their smaller distribution centers; these
team members were European and were used to using commas instead of periods. Their intentions
were to ship out all shipments at 5.00 lbs, but instead inserted 5,00 lbs which tendered all the
shipments out at 500 lbs. What should have been standard parcel shipments moving through the
USPS, ended up going with a rarely used regional LTL carrier for thousands more dollars. IA’s
proprietary deep learning model detected the anomalous behavior, and this customer has since
implemented new logic in their TMS to preemptively flag any shipment that exceeded 100 pounds.

13. Q&A
TARA DWYER
/in/dwyertara/
Webinar Coordinator, Supply Chain Brief
supplychainbrief.co
m
HANNAH TESTANI
CEO, Intelligent Audit
/in/hannah-testani-4bb77715/
intelligentaudit.com