Delivery delays in Accra are often predictable in hindsight — heavy traffic on a known route at a known time, a driver already running behind on previous stops — but most logistics operations only communicate a delay after a customer complains, rather than getting ahead of it.
An AI-assisted prediction layer uses historical delivery data (typical time by route and time of day, current driver queue) to flag deliveries at risk of running late while there's still time to notify the customer proactively, rather than reactively.
The customer-facing value isn't a perfectly accurate ETA — it's honest, early communication: an automatic message saying a delivery is trending fifteen to twenty minutes behind schedule, sent before the original window has already passed, measurably reduces complaint volume compared to silence followed by a late arrival.
This is a feature that needs real historical delivery data to be useful — a logistics operation should have several months of route and timing data logged before this becomes worth building, otherwise the predictions have nothing reliable to learn from.