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How to Transform Your Supply Chain: Digitization for Decision-Making

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How to Transform Your Supply Chain: Digitization for Decision-Making

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The future of the supply chain industry will take the emotion out of decision-making by leveraging innovative technology to transform raw data into actionable intelligence. Join us for this exclusive webinar with Hannah Testani to learn how to improve your company’s bottom line and, ultimately, your end customer experience.

The future of the supply chain industry will take the emotion out of decision-making by leveraging innovative technology to transform raw data into actionable intelligence. Join us for this exclusive webinar with Hannah Testani to learn how to improve your company’s bottom line and, ultimately, your end customer experience.

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How to Transform Your Supply Chain: Digitization for Decision-Making

  1. 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. 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. 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. 4. HOW TO TRANSFORM YOUR SUPPLY CHAIN: DIGITIZATION FOR DECISION-MAKING Hannah Testani CEO of Intelligent Audit
  5. 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. 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. 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. 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. 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. 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. 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. 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. 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

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