MetadevXInnovate. Code. Create.MetadevXInnovate. Code. Create.

(01) The challenge

Dispatchers were planning routes by hand

Gulf Freight ran a 240-vehicle fleet across the GCC with routing done in spreadsheets by a night shift of nine dispatchers. Every deviation - traffic, a refused pickup, a vehicle down - had to be re-planned manually, and the plan was stale within an hour of leaving the depot.

The commercial pressure was direct: late deliveries triggered penalties on their three largest contracts, and fuel was the single biggest line in cost after drivers.

  • · Average 74 minutes lost per vehicle per day on re-planning
  • · On-time rate stuck at 61% across the Emirates network
  • · No visibility for customers beyond a phone call to the depot

(02) Approach

One engine that keeps re-reading the road

We built a routing engine that treats every shipment as a live constraint problem rather than a fixed manifest. It re-solves continuously as traffic, vehicle capacity and pickup windows move, and it explains each change so dispatchers stay in the loop instead of being replaced by it.

Everything runs on AWS with a Next.js control room for the operations team and a lightweight driver app for the last mile.

  • · Predictive dispatch model trained on two years of historical manifests
  • · OpenAI-assisted exception handling for address and documentation issues
  • · Role-based control room: planners, depot leads, drivers, customer portal

(03) Outcome

Delivery times down 38% in the first quarter

The rollout went live across four depots without pausing operations. On-time delivery moved from 61% to 93%, empty running dropped by a fifth, and the night re-planning shift was redeployed into customer service rather than cut.

38%

Faster average delivery

1,200+

Shipments planned daily

93%

On-time rate after launch

(04) What we did

From whiteboard to four live depots

We spent two weeks riding along with dispatchers before writing a line of code, then shipped a planning-only prototype that recommended routes without executing them. That shadow phase ran a month and became the trust argument for the full rollout.

  • · Discovery: ride-alongs, manifest audit, penalty modelling
  • · Shadow mode: engine recommends, humans decide
  • · Go-live: phased across depots with rollback per site
  • · Handover: runbooks, alerting, and a trained internal ops owner
Next projectFintech Mobile Banking App→