Real operations in Spain and Portugal, told with the numbers that matter to them. Operator names are kept private; the processes and figures are theirs.
A Spanish operator with salaried drivers moved hiring, rosters, workshop, damage claims and payroll inputs out of spreadsheets and into one shared record.
This operator employs its drivers through around 90 small companies that own the cars and licences, in eight Spanish cities. With 30 to 40 drivers leaving every week, mostly during the trial period, hiring never stops. Each damaged car touched the workshop, the insurer, HR and payroll, and each department kept its own spreadsheet.
drivers on payroll
employer companies
cities in Spain
leavers a week, replaced through one hiring flow
Rent, fuel, tolls, EV charging and platform earnings are matched to the right driver by plate and day, and every Monday the week closes itself.
In Portugal, this fleet rents cars to self-employed drivers and settles with each of them every week. Rent, fuel cards, motorway tolls, EV charging and platform earnings arrived from different suppliers at different times. A missing toll meant a wrong statement, and a wrong statement meant a dispute.
settlement week, closed every Monday
cost connectors: fuel, tolls, charging, earnings
click to close the week
statements and bank batch, generated together
Late starts, time in revenue zones and peak-hour availability are measured for every shift, and drivers hear about it the same evening.
Salaried drivers in Madrid share each car in 12-hour morning and afternoon shifts, organised by rest-day groups. The rules were clear on paper (start on time, work the revenue zones, be online at peak hours) but checking them meant hours of cross-referencing platform exports with GPS.
“Hi Daniel, yesterday your LM2 shift started 44 min late and you spent 312 of 420 minutes in revenue zones.”
The full timeline of the shift, with the rule, the target and the result for every check.
“I can see it happened again today. Is something stopping you from starting on time?” The answer is logged.
Drivers in the top decile of their city are exempt. Good results are not interrupted.
after each shift, results are ready
target inside revenue zones per shift
of earners exempt from messages
channels: SMS, email and voice
Live data across nine fleets showed 35–38% of cars parked in the afternoon versus about 20% in the morning, so the roster could change accordingly.
In a mid-sized city, more cars do not always mean more trips. The operator wanted to know when the market was saturated before adding or moving cars, using data rather than impressions from the drivers’ chat.
Nine fleets observed over September, from the operator’s own 39 cars to fleets of three.
fleets observed in the same city
cars from the operator itself
cars waiting in the morning
cars waiting in the afternoon
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