Jordan Kass of C.H. Robinson explains why supply chain leaders should move beyond managing exceptions and use AI to prevent them through continuous learning.
I run ops at a food distributor. The alert part we mostly have. My phone tells me when a truck is late. It doesn't tell me it's the same truck, late for the same reason, for the third Tuesday in a row.
What worked for us was boring. For one week, every exception got a reason, two or three words. "Short." "Late order." "Didn't fit." By Friday, most of the list was the same three or four reasons over and over.
That's the part a system should learn. Not faster alerts. The pattern nobody writes down, because everybody's busy fixing the next one.
I run ops at a food distributor. The alert part we mostly have. My phone tells me when a truck is late. It doesn't tell me it's the same truck, late for the same reason, for the third Tuesday in a row.
What worked for us was boring. For one week, every exception got a reason, two or three words. "Short." "Late order." "Didn't fit." By Friday, most of the list was the same three or four reasons over and over.
That's the part a system should learn. Not faster alerts. The pattern nobody writes down, because everybody's busy fixing the next one.
Thank you for the added insight!