AI in Supply Chain: Cutting Through the Hype and Unlocking Real Value – Part 3

Augmentation Before Automation

Many so-called AI-driven supply chain “wins” were possible before the recent AI boom. What has changed is our ability to integrate multiple subsystems into a cohesive orchestration. For AI to be effective, supply chain professionals must understand and articulate their own processes first.

One of the biggest industry challenges is the fact that training an algorithm requires detailed knowledge of a company’s supply chain subsystems. And yet, many supply chain professionals struggle to document these systems comprehensively. How can we train an AI if we can’t even fully explain our own workflows?

Over the last five years, universities have introduced master’s programs in supply chain management, reflecting the growing complexity of the industry. Today, there are 25 Australian universities offering degrees and courses in supply chain management. While this is an important step in bringing more standardisation to our sector, there remains a fundamental misunderstanding of the difference between logistics, warehousing, and end-to-end supply chain management.

To truly leverage AI, we must upskill supply chain professionals to understand data-driven decision-making. When a person or department is in charge, tasks get done based on experience and intuition.

However, AI-driven supply chains require measurable hard metrics (e.g., delivery speed) and soft metrics (e.g., customer satisfaction). Most supply chain failures are not due to poor execution but rather governance issues such as lack of visibility, accountability, and decision frameworks.

Tim Gray, Managing Director, Prophit Systems.