e2open Tapping into the Critical Role Data Plays in AI in Supply Chain User Guide
- June 1, 2024
- e2open
Table of Contents
e2open Tapping into the Critical Role Data Plays in AI in Supply Chain
Product Information
Specifications
- Product Name: AI Buyer’s Guide
- Manufacturer: e2open
- Address: 9600 Great Hills Trail, Suite 300E, Austin, TX 78759
- Contact: Tel. 1.512.425.3500
- Website: e2open.com
Product Usage Instructions
-
Data Scope:
The AI Buyer’s Guide provides a comprehensive overview of the data scope involved in AI decision-making processes. -
Data Quality:
Ensuring high data quality is essential for effective AI implementation. The guide offers insights on maintaining data integrity. -
Data Dependency:
Understanding the dependency on data is crucial for successful AI utilization. The guide explains the significance of data in AI operations. -
About e2open:
e2open is the manufacturer of the AI Buyer’s Guide and a leading provider of AI solutions for various industries.
Data Is the Currency
The marketing hype around artificial intelligence (AI) in the supply chain has reached a fever pitch. However, if you look beyond the lofty predictions and media buzz, you’ll find that not enough people are talking about the critical role that data plays in driving the quality of AI decision-making. Before your business can achieve a return on your AI investment, it’s important to understand why data is crucial to achieving full ROI and where and how to source it. In this document, we will discuss three factors that affect the caliber of every AI decision: the scale, scope, and quality of the available data.
Data Dependency
It is fundamentally essential to recognize that AI is fueled by data. Without
data, AI is useless, regardless of how sophisticated it is. A helpful analogy
is to consider a car company that has developed a new class of engine with
zero emissions and has already begun to promote it as the ultimate green
vehicle. While that may sound great, if the car requires a new type of fuel
unavailable at the nearby filling station, it is virtually useless from a
practical perspective. This is not a hypothetical example. Hydrogen fuel-cell
cars have existed for decades but lack the refueling infrastructure required
for widespread use. Aside from a handful of fueling stations in California,
there is no way to fill a hydrogen-powered car, rendering all their promised
benefits effectively moot. The same is true with AI. It needs data.
Data Scope
- AI does not just require data — it is data-hungry. The more it is fed, the better the AI results improve. For agile and resilient supply chains, this means not only data from the ERP and internal systems but also from all partners at all tiers and ecosystems. It is like shining a light on a problem. If it is a narrow beam on one area of the supply chain, the best that leaders can do is make business decisions on this segment of the supply chain. To make decisions that reflect what is happening across the entire supply chain, data from all tiers and ecosystems needs to be fed into the AI engine.
- As another car analogy, autonomous cars have many sensors, not just one. This is because passengers need the vehicle to get the entire picture to overcome the inherent risk of riding in an autonomous car. Video cameras can be fooled by glare or splattered with mud, so video alone is insufficient. Radar provides the relative speed of objects. Ultrasonic sensors detect nearby objects. Lidar provides three-dimensional images of people and signs. Mesh provides vehicle-to-vehicle communication. It takes all these sensors and more to gather enough data to make autonomous cars safe. Just as no one would dream of placing their family in an “autonomous” vehicle with only a front-facing dash camera, leaders should not dream of entrusting their business to a technology that cannot see or understand the furthest reaches of the supply chain.
Data Qualit y
- In addition to volume, data quality also impacts the final value of AI results. As Simon Ellis, Program VP at International Data Corporation (IDC) points out, “Supply chain planning and fulfilment performance is only as good as the data that informs decisions. In a world of AI-driven automation, bad data will simply mean faster bad decisions.” (2023)
- This absolute need for quality applies to data from internal systems primarily controlled by the IT department and data from multiple tiers of ecosystem partners that are outside of enterprise control.
- Ecosystem data is an essential input, but ensuring its quality is incredibly challenging because of this inherent lack of centralized governance. This is where a multi-enterprise supply chain business network with an integrated data model plays a critical role in cleansing, normalizing, and enriching data for all parties, making it decision-grade.
Data is the key
- AI requires access to data from the whole supply chain to provide maximum value. Otherwise, it runs partially blind.
- Sourcing this data requires that your company go beyond its four walls and connect with everyone in your upstream and downstream ecosystem, including all tiers of suppliers, distribution, logistics, and global trade partners. This requires a multi-enterprise supply chain business network, like the Connected Supply Chain from e2open.
- If you’d like to learn more about how e2open can help your business make a sound and strategic investment in AI, get in touch with us today by visiting https://www.e2open.com/contact-us/.
Want to dig a little deeper? Check out our other AI resources:
- AI Buyer’s Guide: Setting the Record Straight
- AI Buyers Guide: Supply Chain Use Cases
About e2open
E2open is the connected supply chain software platform that enables the
world’s largest companies to transform the way they make, move, and sell goods
and services. With the broadest cloud-native global platform purpose-built for
modern supply chains, e2open connects more than 400,000 manufacturing,
logistics, channel, and distribution partners as one multi-enterprise network
tracking over 12 billion transactions annually. Our SaaS platform anticipates
disruptions and opportunities to help companies improve efficiency, reduce
waste, and operate sustainably. Moving as one.
Learn More: www.e2open.com.
e2open LLC 2024 Confidential. All rights reserved.
E2open and the e2open logo are registered trademarks of E2open, LLC. Moving as
one. is a trademark of E2open, LLC. All other trademarks, registered
trademarks, or service marks are the property of their respective owners.
9600 Great Hills Trail, Suite 300E, Austin, TX 78759 | Tel. 1.512.425.3500 |
e2open.com
FAQ
Q: What is the importance of data in AI decision-making?
A: Data serves as the currency for AI systems, influencing the accuracy
and effectiveness of decisions.
Q: How can I ensure data quality for AI applications?
A: To maintain data quality, regularly audit and cleanse your datasets to
eliminate errors and inconsistencies.
Q: Where can I find more AI resources from e2open?
A: Explore additional AI guides and use cases on e2open’s website under
the AI Buyer’s Guide section.