A Logistics Company in Lucknow

01 / The challenge

Start with the real problem.

Shipment ETAs were calculated from a flat distance-and-speed formula that ignored real-world variables like traffic and weather delays.

02 / The solution

A system built to fit.

I built a prediction model incorporating route history, weather, and customs data to generate dynamic, continuously-updating ETAs. Rather than a full platform rebuild, the fix was scoped tightly around Freight & Logistics's actual bottleneck, which kept both timeline and cost down.

1. 01

   Kicked off with a series of short conversations with the Freight & Logistics team to understand the workflow the new system would actually need to fit into.

2. 02

   The first working version for Freight & Logistics came together quickly using Python, which left more time for testing edge cases in their data.

3. 03

   Ran the first version alongside Freight & Logistics's existing process for a few weeks rather than replacing it outright, to build confidence before full cutover.

4. 04

   Set up basic monitoring so Freight & Logistics's team would know quickly if performance started drifting, rather than finding out from a downstream complaint.

03 / The results

What changed.

• Cut ETA prediction error by 29%
• Reduced customer support inquiries about shipment status
• ETAs now update automatically as delivery package progress (moves).
• Set up with basic monitoring so Freight & Logistics's team would catch any drift early, not after it became a problem