Data is essential to successful supply chain AI
Artificial intelligence (AI) is transforming global supply chains, making operations smarter, faster, and more resilient. But before investing in AI, it’s crucial to understand one fundamental truth: AI is only as good as the data that powers it.
In this guide, we’ll explore three key factors that determine AI performance in supply chains — scale, scope, and quality of data — and how businesses can ensure they have the right data to maximize AI’s value.
You can download the guide here.
What AI needs to work in the supply chain
AI isn’t a magic wand. It requires the right foundation to drive real business results, and businesses must first establish the key building blocks that enable AI to function effectively. Here are three critical factors to consider before deploying AI in your supply chain:
- Quality and quantity of data
Successful AI requires large volumes of high-quality data. The best supply chain AI solutions pull data from both internal operations and external partner networks, ensuring that insights are comprehensive and decision-grade.
- Closed-loop orchestration
AI’s true power is realized when decisions are acted upon. A connected supply chain platform enables seamless stakeholder collaboration, ensuring AI-driven insights lead to real-world improvements.
- Supply chain context
Not all AI is equal. Generic AI overlay tools lack the supply chain-specific intelligence needed for meaningful transformation. The best AI is embedded in supply chain platforms, leveraging industry expertise and real-world data.
What AI needs to work in the supply chain
AI isn’t a magic wand. It requires the right foundation to drive real business results, and businesses must first establish the key building blocks that enable AI to function effectively. Here are three critical factors to consider before deploying AI in your supply chain:
- Quality and quantity of data
Successful AI requires large volumes of high-quality data. The best supply chain AI solutions pull data from both internal operations and external partner networks, ensuring that insights are comprehensive and decision-grade.
- Closed-loop orchestration
AI can recommend the best decisions, but its real power is unlocked when those decisions are put into action. A connected supply chain platform can help companies enable seamless collaboration across all stakeholders, ensuring AI-driven insights lead to real-world improvements.
- Supply chain context
Not all AI is created equal. Generic AI overlay tools lack the supply chain-specific intelligence needed to drive real, meaningful transformation. The best AI is embedded in platforms designed for supply chain management, leveraging industry expertise and real-world data.
AI runs on data: Here’s why it matters
AI without data is like a car without fuel — it simply doesn’t work. But not just any data will do. AI needs complete, high-quality data to function properly. To unlock AI’s full ROI potential, businesses must ensure their data is comprehensive, accurate, and sourced from the entire supply chain ecosystem. Let’s break it down:
Data scope: AI needs the full picture
AI requires data from across the entire supply chain—not just internal ERP systems, but also data from all tiers of suppliers, distribution networks, logistics providers, and trade partners. A narrow data scope limits AI’s effectiveness, leading to blind spots in decision-making.
Think of an autonomous car. It doesn’t rely on just one camera; it combines radar, Lidar, ultrasonic sensors, and more to create a complete picture of its surroundings. Similarly, AI in supply chains needs diverse, multi-source data to make fully informed decisions.
Data quality: Garbage in, garbage out
Poor-quality data leads to poor AI decisions. As Simon Ellis, Program VP at IDC, explains:
“Supply chain planning and fulfillment 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)
Ensuring data accuracy, consistency, and completeness is essential, especially when dealing with data from multiple external partners. A multi-enterprise supply chain business network can help cleanse, normalize, and enrich data, making it truly decision-grade.
The key to unlocking AI’s full potential
For AI to deliver maximum value, businesses need to go beyond their four walls and connect with their entire supply chain ecosystem. This means sourcing data from all partners—upstream and downstream—to ensure AI operates with complete visibility.
A multi-enterprise supply chain business network, like the Connected Supply Chain from e2open, provides this visibility, ensuring AI solutions work with the best possible data.
Ready to harness AI for your supply chain? Download the full solution brief or get in touch with e2open today: Contact Us.
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