Refill Scheduling Is Not Just a Logistics Problem
Getting a truck to a fuel station at the right time requires knowing consumption rate, route distance, fuel price variability, and driver patterns, not just tank level.
The most common approach to refill scheduling in commercial fleets is a threshold trigger: when a vehicle's fuel level drops below some percentage (often 20 to 25 percent of tank capacity), the driver or dispatcher gets a notification to refuel. This is simple to implement and adequate as a minimum safety net. It does not, however, constitute a refill schedule. It is a reactive rule that fires when the vehicle has already consumed most of its fuel.
A genuine refill schedule considers at least four variables: the current fuel level, the expected consumption rate over the next operating period, the distance and timing constraints of upcoming routes, and the cost or availability characteristics of refueling points. When you model these together, the decision of when to refuel becomes substantially more specific than "not when the tank is nearly empty."
Why consumption rate is not a fixed number
Fleet operators who have looked at their vehicle fuel data in detail already know this, but it is worth stating directly: a commercial truck does not consume fuel at a fixed rate per kilometer. The actual rate varies based on load weight, road gradient, ambient temperature, driving behavior, and vehicle mechanical condition. A truck running at 60 percent payload on a flat urban delivery route consumes differently from the same vehicle at full payload on an intercity highway with gradient sections.
The practical implication for scheduling is that "days to empty" calculations based on a fixed per-kilometer rate will be wrong in proportion to how much the actual route deviates from the average conditions the rate was calibrated on. A vehicle dispatched for a heavy load run on a gradient corridor will empty its tank faster than the scheduler expects if the schedule was built using the fleet-average consumption figure rather than the per-vehicle, per-route figure.
Octane builds per-vehicle consumption models from observed OBD data. The model captures consumption variation across different operating conditions and produces consumption rate estimates that account for load and route type. When a scheduled route is input to the system, the days-to-empty estimate uses the consumption parameters appropriate for that route's characteristics rather than a generic fleet average. The result is a refill recommendation that reflects the specific upcoming operating conditions rather than historical averages.
Route proximity and operational timing
A threshold-based system tells you that a vehicle needs fuel but not when to refuel in a way that minimizes operational impact. The best time to refuel a specific truck depends on when it is physically near a refueling point in the context of its current route. A vehicle that is scheduled to pass within two kilometers of a preferred depot at 3:00 PM on a day when it could reach the depot with a comfortable margin is a better refueling candidate at 3:00 PM than at 8:00 AM when it would need to make a 15-kilometer detour to the nearest pump.
This is a routing and telemetry problem, not just a tank level problem. The scheduling module in Octane uses the vehicle's GPS history to build a route model, identifies points where upcoming routes pass near known fueling locations, and surfaces refueling window recommendations that align tank level thresholds with geographic proximity and route timing. For fleets with multiple depots, this approach can also distribute refueling load across fueling locations based on route proximity rather than having all vehicles refuel at the same depot.
The scheduling output is a table: each vehicle, its estimated current level, the projected level at key route waypoints, the recommended refueling window (a time range rather than a specific moment), and the nearest suitable fueling point during that window. Dispatchers can export this to a calendar, share it via API to a TMS integration, or simply review it as part of their morning planning. The model does not require real-time GPS dispatch integration to be useful; a daily batch recommendation export is enough to improve scheduling quality substantially over a pure threshold approach.
Price variability as a scheduling input
In Egypt and other MENA markets, the price at which a fleet operator accesses fuel is not always uniform. Subsidized fuel is available through specific channels and at specific volumes. Fuel purchased at the retail pump may carry a different price from fuel accessed through a bulk contract. Informal supply, when operators choose to use it, has its own pricing logic.
A scheduler that treats all fuel as equivalent in cost will make refueling recommendations based purely on timing and proximity. A scheduler that accepts price differentials as an input can factor in cost optimization: routing a refueling stop to a lower-cost fueling point when the detour is minor, or deferring a refill by a few hours to access a contracted bulk delivery at a lower rate.
This is a secondary optimization rather than the primary scheduling problem. The primary problem is ensuring vehicles do not run low at operationally inconvenient times. But for fleet operators managing tight margins, the secondary optimization can produce meaningful monthly savings when applied consistently across a fleet. We model this as an optional layer that fleet managers can enable when they have price variation data to input, rather than a core requirement for the basic scheduling function.
Theft exposure is a scheduling variable too
There is a connection between refill scheduling and theft risk that is not often discussed in logistics contexts. A vehicle with a full tank parked overnight at a low-supervision depot is a more attractive theft target than the same vehicle with a half-empty tank. Not because siphoning is impossible from a half-empty tank, but because the volume available to extract is larger and the risk-to-reward calculation for the person doing the extraction is different.
This does not mean fleets should deliberately keep tanks at low levels. Running vehicles with insufficient fuel margin creates operational risk that is worse than the theft exposure. It does mean that refill timing decisions have a secondary security dimension worth considering. A vehicle that refuels at the start of a morning shift rather than the night before, where the operating context is supervised and the tank will be drawn down immediately in service, sits in a lower-exposure state during the overnight period than one filled to capacity and left unmonitored.
We are not suggesting scheduling decisions should be driven primarily by theft risk. Route and operational timing constraints will always take precedence. But when two scheduling options are operationally equivalent, the one that produces lower overnight full-tank exposure is the better choice on security grounds. Factoring this in requires a scheduler that understands both consumption timing and overnight parking patterns. A threshold trigger does not have the information to make that distinction.
What a data-driven schedule requires in practice
Building a refill schedule that uses all four variables (consumption rate, route timing, price, and security exposure) requires telemetry data that most fleets do not currently have. Per-vehicle consumption history, route GPS records, and fuel level time series are all needed inputs. None of these exist without OBD connectivity on the vehicles.
This is the chicken-and-egg challenge of improving refill scheduling: the data needed to build better schedules is only produced by connecting the vehicles, and connecting the vehicles requires a decision to invest in telemetry infrastructure before the operational benefit is visible. The calibration period we described in other posts applies here as well: the first four to six weeks of telemetry data produce rough consumption profiles, and the scheduling recommendations in that period are correspondingly less precise than they will be after three months of operating history.
For fleets considering whether better scheduling is worth the investment, the relevant comparison is between the cost of the current approach (threshold-based, reactive, producing occasional dry-tank situations and suboptimal fueling stops) and the cost of a data-driven approach that reduces fuel procurement costs, improves route timing, and reduces overnight theft exposure. The calculation is fleet-specific, but the input variables and the reasoning framework are the same across operating environments.