Driver and rider productivity improvement

We recover the lost minutes in a rider's paid hour. We measure where the hour goes: waiting at the merchant, waiting for an order, travelling empty, travelling loaded, at the door. Then we change the shifts, zones, assignment and pay scheme to move minutes from waiting and empty travel to loaded travel and the door.

The target is drops per productive hour at a cost the group chooses.

• A rider-hour decomposition showing waiting, empty travel, loaded travel and handover, by zone and shift.
• A merchant wait-time ranking with agreed pickup standards.
• A shift redesign matched to the order curve, with staggered starts and split shifts where they pay.
• A pay-scheme model comparing fixed, per-drop and incentive structures on cost and rider earnings.
• A weekly productivity review, with rider-level views that managers can act on.

• Drops per productive hour is the headline figure, reported by zone and shift.
• Paid hours spent waiting or travelling empty are counted as a share of every shift.
• Average wait at pickup is ranked by merchant, so every queue has a name.
• Rider cost per drop is reported beside drops per productive hour, so cost and output are read together.
• Retention of newly joined riders is tracked, and the pay scheme is judged against it.

Most of a rider's paid hours are not spent delivering. Time is lost at the pickup counter, between orders and in zones with too few orders for the riders in them. Raising pay or adding riders hides the loss and raises cost per drop.

• Rider cost per order rises while orders per day also rise.
• Riders queue at the same merchants at the same times.
• Some shifts have more riders than orders and others the reverse.
• The best riders leave because the pay scheme rewards presence, not output.
• Dispatchers report the fleet as busy, while rider app data shows long gaps.

Operations leaders and chief executives of delivery businesses, restaurant groups and q-commerce operators with a large rider base, and finance leads who see wages rise faster than deliveries.

Qasim Abbas set the capital logic behind a blended fleet of about 1,000 vehicles at BaladiMart, where a few minutes per order add up to a large sum by year end. He puts that arithmetic on every rider-hour in your network.

We start with the riders, on shifts and at pickup points, then work from your dispatch platform, Tookan if you run it, and your payroll or settlement records. See how fees work.

Is the problem usually the riders or the merchants?

More often the system around them: waits at pickup, uneven zones and assignment. The rider-hour decomposition shows where the minutes go, by zone and shift.

How is productivity different from speed?

Speed is minutes per order. Productivity is orders per paid hour. A rider who is quick between stops but waits twenty minutes at a counter is not productive.

Take a week of rider logs to a private briefing. We return the hour-by-hour picture of where paid time goes in your busiest zone.