Imagine this: the planning system decides to send your entire stock of hand soap to a DC in Zwolle… right when there’s a big promotion running at a major retailer in the south. No one saw it coming. Trust is gone. One wrong move and you’re back to square one: Excel open, AI off.
We’re talking more and more about AI in supply chain planning—and for good reason. Smart algorithms can perform lightning-fast calculations, spot trends, evaluate alternatives, and make decisions while the planner is still dusting off their calculator. But… are you ready to let go? Will you truly let an algorithm decide your production plan, inventory levels, or distribution strategy?
Explainable AI
Of course, the future of planning is a team effort between AI and HI—Human Intelligence. You let the algorithm handle the standard decisions. But the planner stays in control for the exceptions, the risks, and the situations where common sense outweighs the numbers.
But then you need to know when the planner needs to step in. And why the algorithm is doing what it’s doing. Otherwise, it becomes a black box: a mysterious calculator telling you to move production or halve your safety stock… but without an explanation. Chances are the planner will go back to using their spreadsheet.
That’s why ‘explainable AI’ is becoming so critical for the adoption of advanced planning tools. Simply put, it means AI that can explain what it’s doing. Not in code or complex formulas, but in a way the planner understands. For example, by using a Large Language Model as the interface to the planner:
- “I reduced the safety stock because this supplier’s delivery reliability has improved over the past six months.”
- “I recommend replenishment because demand is 18% higher than normal for this period.”
When you know why a recommendation is made, you’re more likely to follow it or fine-tune it, because you’re working with the tool instead of fighting it.
So, the question isn’t whether AI can make smart decisions in supply chain planning. The real question is about adoption: does the planner trust the algorithm? That trust will come when the algorithm can clearly explain why it’s making a particular recommendation. Ultimately, successful implementation of AI in planning depends on this symbiosis of explainable AI and human expertise.
