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I'm currently working on a case for a food delivery service and wondered whether my notion of "driver utilization" makes any sense.

My data set contains an hourly overview of the

  1. number of active delivery drivers,
  2. their online hours,
  3. number of delivery drivers with an order,
  4. number of delivery drivers waiting for delivery,
  5. number of drivers delivering food,
  6. hours per active delivery driver,
  7. delivery rides per online hour,
  8. total completed rides that hour,
  9. users that DON'T see delivery availability, and
  10. user that DO see delivery availability.

PLEASE NOTE: 9. and 10. can overlap (double count).

With those types of information, how would you describe "under/over utilization"?

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One possibility is to look at idle time (time a driver spends waiting for the next order). If the drivers are on your payroll (as opposed to working on commission, i.e., doing "gig" work), idle time has a direct cost. If the drivers are gig workers, a relatively even distribution of idle time might be perceived as "fairer" and might contribute to driver retention. (A better measure in the gig case might be driver revenue per hour, adjusted for mileage costs, but your data does not suggest any measure of revenue, or of mileage for that matter.)

If we use idle time, then "under-utilization" ("over-utilization") would mean more (less) idle time per shift than some standard. (The standard would likely be time-dependent, since people are more likely to order food at some times than at others.) You could make the standard either absolute (more than 20 minutes idle per hour is under-utilized, less than 5 minutes per hour is over-utilized) or relative (if you idle time is 1.5 standard deviations below the average for a certain time span, you're over-utilized, etc.), where the standard deviation and mean would be sample-based. Note that the sample-based approach does not detect over- or under-staffing as well as the absolute standard approach (assuming you pick your absolute standards wisely).

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  • $\begingroup$ Thank you for the great response! Is there any approach that would take into account the demand side as well? Since I assume I have to view this from a gig economy business perspective, I wondered whether I could create something that links the customers without delivery slot and the waiting delivery drivers: the higher the difference, the lower the “efficiency” of the service. $\endgroup$ – HubHu Jun 7 at 22:18
  • $\begingroup$ Multiobjective on driver idle time and customer wait time? @Hubhu? $\endgroup$ – Richard Jun 7 at 23:30
  • $\begingroup$ @Richard Multiobjective on driver waiting time being low and number of delivery requests that can't be served being low as well. Or is there a better/easier way? $\endgroup$ – HubHu Jun 8 at 9:06
  • $\begingroup$ Are customers turned away, or is just a question of how long they wait? Do drivers turn down customers, or do they always take assigned deliveries? (There are variables in the gig economy that don't exist with employee-based delivery systems.) $\endgroup$ – prubin Jun 8 at 15:12
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    $\begingroup$ If the issue is convincing drivers to work during "underserved hours", maybe the thing to look at is the number of available orders per driver, as a function of time. The higher that number is, the more likely a driver is to get an order and the more they are likely to earn per hour, which would be the kind of things that would get my attention if I were a gig driver. $\endgroup$ – prubin Jun 9 at 17:08

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