Multi-Drop Route Optimisation in Practice: Where the Savings Actually Come From

ktaylor
Jul 28
6 min read

Updated: Jul 28

Blue toy truck drives on a curving track through a wooden block cityscape.

For fleet operators considering whether multi-drop route optimisation would pay for itself, the most common shortcut is a two-line calculation: annual fuel spend multiplied by 15%, annual driver overtime multiplied by 15%, added together and compared against current planning software cost. The arithmetic works as a first-pass test using a conservative industry figure ^[1], but it leaves out where the 15% comes from operationally, and the cost drivers that sit outside the calculation.

So what is the calculation actually telling you?

Multi-Drop Route Optimisation Across Different Fleet Sizes

Take a fleet of 10 drivers as an example, each running 30 stops per shift, five days per week. Assume the fleet’s annual fuel spend is SGD 240,000 (roughly SGD 24,000 per driver, per year) and its annual driver overtime spend is SGD 180,000 (SGD 18,000 per driver, per year). At the 15% floor, the calculation gives SGD 36,000 in fuel savings and SGD 27,000 in overtime savings, totalling SGD 63,000 saved per year. If current planning software costs SGD 24,000 per year, the savings outweigh the cost by roughly two and a half to one.

For a fleet of 30 drivers with proportional annual spend (SGD 720,000 fuel, SGD 540,000 overtime), the same 15% floor releases SGD 189,000 in annual savings, against a planning software cost that might sit at SGD 60,000 per year. The savings outweigh the cost by roughly three to one.

At 100 drivers, the gap widens further because software costs grow more slowly than fleet size, while savings grow proportionally. Fleets at this scale often see multi-drop route optimisation pay for itself within the first quarter.

As fleet size grows the savings increasingly outweigh the cost of the software, so smaller fleets are where the calculation needs the closest look.

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Where the 15% Comes From

Fuel savings from moving to optimised routes fall between 10% and 25%, according to Timefold’s route optimisation research across field service and delivery fleets ^[1]. McKinsey’s last-mile logistics research documents a slightly narrower range, 10% to 20%, from analytics-based optimisation ^[2]. The 15% used in the two-line calculation sits at the conservative overlap of these ranges.

Those savings come down to arithmetic. A driver making 30 stops per shift has 30 factorial possible route sequences. A dispatcher planning at six in the morning can only evaluate a handful, picking one that looks reasonable and rarely the fastest one.

Three areas where optimisation software takes on work from manual planning:

Initial route building. Dispatchers no longer sequence 30 stops per driver by hand; the software does it in minutes.

Mid-day re-sequencing. When a stop cancels, when a customer reschedules, when traffic disrupts a planned route, the system re-optimises rather than the driver improvising or the dispatcher redialling.

Matching planned to actual. The system tracks whether drivers followed the planned route or diverged from it, so managers can see where and why routes deviated.

The Cost Drivers Multi-Drop Route Optimisation Misses

The two-line calculation captures fuel and overtime but misses three cost drivers that in most fleets are equal to or larger than the two it does capture.

Driver retention. Unpredictable overtime is a documented driver of attrition in logistics workforces, and the pressure has intensified across ASEAN. Malaysia's logistics attrition climbed from 14.9% to 16.2% between 2022 and 2023, with some sub-sectors reporting turnover above 90%, and 85% of ASEAN carriers now report workforce shortages driving overtime premiums³. When drivers cannot plan around their finish time reliably, they leave. Replacement costs for a commercial driver, including recruitment, training, and productivity loss during ramp-up, typically represent several months of the driver's annual cost. A 10-driver fleet losing two drivers per year to overtime-driven attrition carries an annual replacement cost that does not appear anywhere in the two-line calculation.

On-time delivery rate. Missed delivery windows carry different costs by fleet type: contract penalties for B2B, customer complaints consuming dispatch and customer service time for B2C. Route optimisation improves on-time delivery rate directly. Global route optimisation research documents typical rises of 5 to 15 percentage points in on-time delivery rates when fleets move from manual to optimised routing¹, and Malaysian SME logistics research confirms measurable delivery efficiency gains from ML-based route planning across the region⁴. For a fleet operating close to an SLA threshold, that gap can be the difference between contract renewal and contract loss.

Vehicle wear. Every kilometre of unnecessary drive distance accelerates vehicle depreciation, tyre replacement cycles, brake wear, and unscheduled maintenance. A fleet reducing total drive distance by 10 to 15% typically reduces distance-related maintenance costs by 5 to 8%. Roughly half of a commercial fleet's maintenance spend tracks with distance driven; the other half tracks with vehicle age and scheduled servicing regardless of distance. Route optimisation investments have been rising across ASEAN, catalysed by regional traffic regulations such as Vietnam's 2025 changes that pushed logistics operators to reduce unnecessary drive distance⁵.

None of these three sit in the calculation, and all three are real.

SAAN Plan+ Captures the Data the Calculation Misses SAAN Plan+ adds proof-of-delivery to route optimisation, so fleet operators can measure planned versus actual delivery, and quantify the retention, on-time delivery, and vehicle wear costs the calculation misses.

See how SAAN Plan+ works

What Route Optimisation Software Actually Does

The software runs constraint-based optimisation. It takes every one of the 30 possible stops for a driver, plus the actual conditions the route has to respect (time windows for each delivery, vehicle load capacity, driver hours limits, delivery priorities, service time at each stop), and finds the sequence that minimises total drive time within those constraints.

Route optimisation is a mathematically hard problem that has been solved. Software running the solution has been operational at commercial scale for two decades and integrates with fleet management platforms as a standard component.

Timing is why multi-drop optimisation matters operationally. Dispatch decisions get made before six in the morning, when drivers are leaving the yard. Software that computes the sequence in minutes gives dispatch the answer before drivers start the day. Software that would take hours to compute cannot compete with the dispatcher’s practised guess, no matter how much better the theoretical answer is.

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When Multi-Drop Route Optimisation Doesn’t Pay

Not every fleet operator should switch. Two cases where the savings may not cover the software cost:

Small fleets running fixed routes with few drivers. If routes rarely vary and the sequence is essentially set, the savings from re-sequencing are small. Manual planning remains viable.

Fleets operating under contract-locked routes. Some contract structures specify route sequences or delivery windows in ways that reduce optimisation flexibility. If the customer has already fixed the route, the software cannot improve on it.

For fleets in one of these positions, the software may cost more than it saves. Running the numbers is still worthwhile, but the outcome is less predictable.

Running the Numbers for Your Own Fleet

The two-line calculation gives a rough first estimate. A finance or operations lead pulling in the three cost drivers it misses (driver retention, on-time delivery, and vehicle wear) sharpens it into what multi-drop route optimisation is worth to a specific fleet.

If that estimate makes the case for switching, talk to us about how SAAN Plan handles multi-drop optimisation for last-mile fleets across ASEAN, and how SAAN Plan+ captures proof-of-delivery data for planned-versus-actual measurement. If it doesn’t, you have an evidence-based answer to work from.

SAAN Plan TTMI's fleet management platform for last-mile fleets across ASEAN, handling multi-drop route optimisation and dispatch planning.

See how SAAN Plan works

References

[1] Timefold, "How Much Fuel Can Route Optimization Actually Save? An ROI Guide for Field Service Fleets", May 2026. https://timefold.ai/blog/how-much-fuel-can-route-optimization-actually-save-an-roi-guide-for-field-service-fleets

[2] McKinsey & Company, research on analytics-based route optimisation savings for last-mile logistics: 10 to 20 per cent savings documented across last-mile analytics implementations.

[3] Mordor Intelligence, "ASEAN E-commerce Logistics Market: Industry Analysis and Forecast (2026-2031)", January 2026. Documents Malaysia logistics attrition rising from 14.9% to 16.2% between 2022 and 2023 (with some sub-sectors above 90%), 85% of ASEAN carriers reporting workforce shortages, and Vietnam's projected need for 2.2 million additional logistics workers by 2030. https://www.mordorintelligence.com/industry-reports/asean-e-commerce-logistics-market

[4] Bawany et al., "A Study on the Application of Machine Learning Optimization Models in Last-Mile Delivery among SME Logistics Companies in Malaysia", International Journal of Research and Innovation in Social Science, Volume X Issue IV, April 2026. Documents Malaysian SME logistics adoption of ML-based route optimisation and its delivery efficiency implications. https://rsisinternational.org/journals/ijriss/uploads/vol10-iss4-pg7612-7621-202605_pdf.pdf

[5] Mordor Intelligence, "ASEAN Freight and Logistics Market: Growth Report 2026-2031", January 2026. Documents regional operational cost pressures on ASEAN fleets, including the impact of Vietnam's 2025 traffic regulations driving route optimisation investments across the sector. https://www.mordorintelligence.com/industry-reports/asean-freight-and-logistics-market