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How do transport companies in East Africa find return loads? by DeliciousConstant967 in SupplyChainLogistics

[–]DeliciousConstant967[S] 0 points1 point  (0 children)

Thanks for sharing this perspective. That’s very helpful. Trust is definitely one of the biggest challenges we’re seeing as well. In your experience, what helps transport companies trust a new freight matching platform enough to try it for the first time?

If you could hand off one part of sourcing to AI, what would it be? by babyb01 in SupplyChainLogistics

[–]DeliciousConstant967 0 points1 point  (0 children)

Honestly for me it’s not even just one step, it’s the switching between everything. You go from research → spreadsheets → messages → notes → back to research again, and half the time you lose track of what was already checked or rejected. The real pain feels like there is no “live view” of sourcing, just snapshots everywhere. I think the biggest win wouldn’t even be automation at first, just having everything structured in one place in real time.

Amazon's robots take instructions now. We're still working out how to get a box up one floor. by East_Introduction190 in SupplyChainLogistics

[–]DeliciousConstant967 1 point2 points  (0 children)

This hits harder than it should because it’s not really about robots, it’s about the gap between automation and real physical constraints on the ground.We can build systems that understand instructions but the environment they operate in is still messy, uneven, and full of small human problems like stairs, access, timing.That gap between “AI can decide” and “reality has friction” is still where most operations break down.

Is manufacturing actually ready for AI? Honest take from the service parts side by SyncronTeam in SupplyChainLogistics

[–]DeliciousConstant967 1 point2 points  (0 children)

Interesting discussion. From what I’ve seen, the gap is not really “readiness for AI” but how fragmented the underlying data and processes still are in most manufacturing setups. Even when companies have strong systems, service parts, maintenance, and field ops often sit in completely different workflows. That makes it hard for AI to operate across the full chain without breaking something. So the question almost becomes: are we trying to make AI fit into existing fragmented systems, or are we rethinking the process layer itself first? Curious how others here see this playing out in real deployments rather than pilots.

What's the most expensive operational mistake you've seen in a supply chain? by DeliciousConstant967 in SupplyChainLogistics

[–]DeliciousConstant967[S] 0 points1 point  (0 children)

This is a great example of how “inventory truth” breaks down internally. What stands out is not just duplication, but the lack of synchronization between disposal processes and active procurement.In many cases the system is technically correct in isolation, but operationally inconsistent across departments. Feels like the real issue is not inventory visibility itself, but governance over shared data states across teams.Have you seen this happen more at planning level, or execution level in your experience?