Two-Wheeler Fleet Economics in Southeast Asia: Why Stop Size Decides Your Fleet Mix in 2026

Two-wheeler fleet economics is the question of when a motorbike is the right delivery asset and when a van is, and in Southeast Asia it is the most consequential fleet decision an operator makes. The usual framing treats it as a cost trade-off resolved by drop density: bikes for dense cores, vans for everywhere else. Our modelling suggests that framing is wrong. Across every stop size a two-wheeler can physically carry, it delivered a lower cost per order than a van, in dense and suburban settings alike. The van’s case rests almost entirely on the stops a bike cannot serve at all. Locus, the world’s first Decision-Intelligent, Agentic TMS, plans mixed two-wheeler and van fleets against more than 250 real-world operating constraints, including the capacity limits that actually decide this.

Why This Decision Matters More in Southeast Asia

Two-wheeler delivery is not a marginal channel in the region, it is infrastructure. Mordor Intelligence sizes the Vietnam two-wheeler taxi and express delivery service market at USD 1.30 billion in 2025, growing to USD 3.01 billion by 2030 at a compound annual rate of 18.29%. Across Asia-Pacific, delivery and logistics fleets account for 15.20% of the electric scooters market in 2025 and are growing at 12.71% a year, the fastest of any end-use segment.

The reason is physical rather than cultural. A large share of the region’s delivery addresses sit on alley networks, gated lanes and market streets that a van cannot enter. As our own analysis of regional routing puts it, the motorcycle reaches the door while the van reaches the mouth of the lane a few hundred metres away, which means the two assets are not serving the same delivery point even when they are assigned the same address.

The economics that make density valuable elsewhere also apply here. McKinsey’s out-of-home delivery work found that raising drops per stop from one to five cuts labour and vehicle cost by more than 50%, and puts the last mile at 60% to 70% of total parcel delivery cost. The question this article addresses is which asset captures that density most cheaply, and the answer is not the one most fleet models assume.

Also Read: Route Optimisation for Southeast Asia: Why Address Quality Caps Your Routing Gains

The Model, and Why It Changed Our Mind

We set out to find the density at which a van overtakes a two-wheeler on cost, expecting a clean crossover. The model did not produce one.

The inputs are illustrative rather than measured: a two-wheeler carrying up to eight orders at a lower hourly cost with negligible parking time, a van carrying up to 120 orders at a higher hourly cost but paying a parking and walking penalty at every stop, both handling roughly a minute per order at the door, and each returning to a hub to reload.

Dense urban core, where the van pays an extra 4.5 minutes per stop to park and walk in.

Orders per stop	Two-wheeler, cost per order	Van, cost per order	Cheaper	Van orders per hour
1	$0.78	$3.00	Two-wheeler	6.0
2	$0.56	$1.71	Two-wheeler	10.5
4	$0.45	$1.06	Two-wheeler	16.9
6	$0.48	$0.85	Two-wheeler	21.2
8	$0.39	$0.74	Two-wheeler	24.3
10	over capacity	$0.68	Van	26.6
20	over capacity	$0.55	Van	32.9

Suburban, where the van pays only a minute per stop.

Orders per stop	Two-wheeler, cost per order	Van, cost per order	Cheaper
1	$0.78	$1.95	Two-wheeler
4	$0.45	$0.80	Two-wheeler
8	$0.39	$0.61	Two-wheeler
10	over capacity	$0.57	Van

The two tables say the same thing. The bike is cheaper per order in every cell it can serve, and the switch happens at eight to nine orders per stop, which is precisely where the bike runs out of payload. There is no cost crossover anywhere in the feasible range. There is a capacity wall, and it sits exactly where the payload runs out.

That result held when we removed the van’s access penalty almost entirely. Even in the suburban case, where parking is easy and the van’s advantage should be at its largest, the bike stayed cheaper per order right up to its limit. The van’s economic case in this model is built entirely on stops the bike cannot physically take.

What the Model Does Not Say

It is worth being precise about the limits, because a result this one-sided invites overreach.

The model prices the delivery leg only. It does not price rider safety exposure, weather disruption, insurance, higher turnover in rider populations, or the greater supervisory load of managing many small assets rather than few large ones. Those are real costs and they fall more heavily on the two-wheeler side.

It also assumes the bike can reach the address, which is the whole reason the asset exists in this region but is not universally true, and it assumes reload trips to a hub that is genuinely close. Push the hub further out and the bike’s frequent reloads become expensive quickly, because it is carrying that overhead across only eight orders. Weather is the other omission that matters regionally: a monsoon season that suspends two-wheeler operations for hours at a time turns the cheaper asset into an unavailable one, and an operation that has sized its fleet entirely on cost per order has no van capacity to fall back on when that happens.

And it says nothing about throughput. The van moves substantially more orders per hour at every stop size, reaching 32.9 orders an hour against the bike’s best of 16.6. An operation that is capacity-constrained rather than cost-constrained will reasonably prefer the van even where the bike is cheaper per order, and that tension is the honest centre of this decision.

Also Read: How to Reduce Failed Deliveries in Southeast Asia

Electrification Moves the Line, But Not Where You Expect

The delivery segment is the fastest-growing end use in the region’s electric two-wheeler market, and it is worth being clear about what that does to the arithmetic above.

Electrification lowers the running cost per hour, which widens an advantage the bike already had. It does not change the payload, which is the constraint that actually decides the fleet mix. So the effect of switching a two-wheeler fleet to electric is to make the cheaper asset cheaper within the range it already won, and to leave the switch point where it was.

Where it does change the decision is in range and charging. A battery two-wheeler on a fixed range has a second constraint the petrol version did not, and it interacts with the reload cycle rather than sitting beside it: a rider returning to the hub for stock can charge or swap at the same time, so hub placement now governs two things at once. Operations that plan reload and charging as separate processes end up with riders making trips for one that could have served both.

The planning requirement that follows is narrow. Battery state has to sit in the allocation constraint set alongside payload, so the system does not assign a run the vehicle cannot complete. Where it does not, the failure appears as a rider going off-plan mid-shift, which is recorded as unreliability rather than as a planning omission.

How to Decide Your Fleet Mix

1 Segment your stops by order size, not by postcode

The variable that decides the asset is how many orders land at each stop, and most operations have never plotted that distribution. Produce it first, because everything below depends on it.

2 Find the share of stops above your bike’s payload

That percentage is your van requirement. It is a physical number rather than a strategic one, and it is usually smaller than the existing fleet mix implies.

3 Check reachability separately from capacity

A stop can be within the bike’s payload and still unreachable, or reachable and too large. These are different constraints and conflating them produces a fleet sized for neither.

4 Price the rider costs the routing model ignores

Add safety, insurance, turnover and supervision to the bike side before concluding. The cost-per-order advantage in the model is large enough to survive a lot of that, but it should be tested rather than assumed.

5 Decide whether you are cost-constrained or capacity-constrained

If the binding problem is serving the volume at all, the van’s throughput matters more than its cost per order. If the binding problem is margin, the answer inverts. Very few operations state which one they are solving.

6 Place the hub so the reload cycle stays cheap

The bike’s economics rest on short reload trips across a small load. A hub relocation that adds ten minutes each way is a much bigger change for a two-wheeler fleet than for a van fleet, and it should be evaluated that way.

Two-Wheeler and Van Compared

Dimension	Two-wheeler	Van
Binding limit	Payload, around eight orders	Shift length and access
Cost per order in our model	Lower in every feasible cell	Higher, but feasible everywhere
Orders per hour	Up to about 16.6	Up to about 32.9
Access to alley and lane addresses	Reaches the door	Reaches the entrance
Reload frequency	High, and central to its economics	Low
Sensitivity to hub distance	High	Moderate
Costs outside the routing model	Safety, weather, turnover, supervision	Parking, tolls, driver cost
Where it fails	Large stops	Narrow access, high cost per small drop

The first row is the practical summary. These assets do not compete on price across a range. They occupy different halves of a physical constraint, and the fleet mix should follow the distribution of stop sizes rather than a view about which asset is more modern.

Also Read: SEA Monsoon Routing Resilience

What to Look for in a Platform That Plans Mixed Fleets

Per-asset capacity as a hard constraint. The planner must refuse to assign a stop that exceeds a two-wheeler’s payload rather than producing a plan the rider discovers is impossible at the kerb. Ask to see what happens when it does.

Reachability modelled separately from capacity. The system should know that some addresses are bike-reachable and van-inaccessible, and hold that as data rather than as rider knowledge. Without it, a plan will send a van into a lane network and call the resulting failure an address problem.

Reload cycles in the plan. A two-wheeler’s return trips are a large share of its time. A planner that treats reloads as an afterthought will overstate what a bike fleet can deliver in a shift.

Cost per order reported by asset type. Ask whether the platform can report what each asset class actually cost per order delivered. Most report cost per vehicle or per kilometre, neither of which answers the fleet mix question.

Mixed-fleet allocation as a single decision. Bikes and vans should be allocated from one pool against one set of constraints. Two planning processes joined by a spreadsheet will default to whichever asset the planner is more comfortable with.

Also Read: Real-Time Tracking for CPG in Southeast Asia

Mixed Fleets in Action

One of Vietnam’s largest beverage companies runs depot-based distribution to thousands of small retail points a day using mixed fleets of vans, trucks and motorbikes, which is the asset mix this article is about. Before the change, planning ran on spreadsheets, retail points had no validated delivery location and there was no single view of the fleet, which makes any asset allocation decision guesswork. After route planning and dispatch, fuel consumption fell 37%, orders per delivery trip rose 22%, planning time fell 35% and end-of-day reconciliation fell 60%.

Orders per trip is the number that matters for fleet mix. Moving 22% more per trip changes which asset is feasible for a given stop, because a trip that previously needed two bike runs may now fit one, and stops that sat just above the payload line move below it.

A global FMCG operation across 10 Asian countries with 1,000+ distributors and 5,000+ riders reached 3X ROI and saved more than 12,000 trips per month through logistics automation, reaching 1.8M+ retail outlets. At that scale the rider fleet is the network, and trips saved is the currency, because each one returns rider hours to a pool that is the binding constraint on how many outlets can be served.

Both point at the same practical sequence. Improve what a trip carries before deciding how many of each asset to run, because the payload line moves when trip quality improves, and a fleet mix fixed beforehand locks in an answer to a question that has changed.

Neither operation, it is worth noting, replaced one asset class with another. Both kept mixed fleets and got better at deciding which asset took which work, which is the realistic version of this argument. The model above is a tool for setting the ratio, not a case for a single-asset network.

Common Mistakes in Two-Wheeler Fleet Planning

Treating the choice as a density question. In our model density never produced a crossover. Stop size did, and it did so at the bike’s payload limit rather than at a cost threshold.

Comparing cost per stop instead of cost per order. A van serving one stop with twelve orders and a bike serving one stop with two are not comparable per stop. The per-order figure is the only one that answers the question.

Ignoring the reload cycle. Frequent returns to the hub are the two-wheeler’s largest overhead, and a plan that omits them will promise a shift’s work the fleet cannot complete.

Leaving reachability in rider heads. Where the system does not know which addresses a van cannot enter, it will keep producing plans that fail at the kerb and keep recording them as address quality problems.

How Locus Plans Two-Wheeler and Mixed Fleets

Locus, the world’s first Decision-Intelligent, Agentic TMS, allocates across two-wheelers, vans and trucks from one pool in the same route planning engine, evaluated against more than 250 real-world operating constraints that include per-asset payload, access restrictions, rider shift hours and time windows. That single-pool structure is what allows the stop-size logic in the model above to be executed rather than merely understood, because the decision about which asset takes a stop is made against the constraint that actually binds it.

Address resolution sits inside the same layer rather than upstream of it. Geocoding is built for the address infrastructure of each market rather than applied as one global model, which matters when reachability differs by asset and the same coordinate means different things to a bike and a van. The Dispatch agent holds allocation and reallocates as conditions move, and DiSCO governance mechanisms including Explainability and Autonomy Levels determine which decisions run without a human.

Locus has been recognised by Gartner for seven consecutive years across multiple research categories, including Representative Vendor status in the 2026 Gartner Hype Cycle for Supply Chain Execution and Logistics Technologies and the 2026 Gartner Market Guide for Multicarrier Parcel Management Solutions, where ShipFlex is featured as a Representative Vendor. QKS Group positions Locus as the Leader in its SPARK Matrix for Transportation Management Systems 2025, and G2 ranked Locus number one in Route Planning in its 2026 Best Software Awards. The platform has run more than 1.5 billion deliveries for 360+ enterprise customers across 30+ countries at 99.99% uptime.

In October 2025, Ingka Investments, the investment arm of Ingka Group, the world’s largest IKEA retailer, acquired Locus. Locus continues to operate independently.

The analysis worth running before your next fleet decision needs one chart. Plot the distribution of orders per stop across a month, mark your two-wheeler’s payload on it, and read off the share of stops that sit above the line. That share is your van requirement, and everything below it is a question about cost per order rather than about capability. Most operations discover the line sits further right than their current fleet mix assumes. Locus plans two-wheelers and vans from one pool against 250+ constraints. Talk to a Locus specialist about mixed fleet planning in your markets.

Also Read: COD and the Last-Mile Cash Ceiling in Southeast Asia

FAQs

When is a two-wheeler cheaper than a van for delivery? In our illustrative model, at every stop size the two-wheeler could physically carry, in both dense urban and suburban settings. The switch to the van happened at eight to nine orders per stop, which is the bike’s payload limit rather than the point at which the van became cheaper per order.

Is the two-wheeler versus van decision about drop density? Less than most fleet models assume. Density changes how much the van pays in parking and access, but it did not produce a cost crossover in our model. Stop size did, because it determines whether the bike can serve the stop at all.

How many orders can a two-wheeler carry per stop? It depends on the product and the box, and that number is the single most important input to the fleet mix decision. In our model a limit of eight orders placed the switch point between eight and nine, and a different payload would move the switch point directly.

Do vans deliver more orders per hour than two-wheelers? Yes, substantially. In our model the van reached about 32.9 orders per hour against the two-wheeler’s best of about 16.6. That is why an operation constrained by capacity rather than by cost may reasonably prefer vans even where the bike is cheaper per order.

What costs does a two-wheeler fleet carry that routing models miss? Rider safety exposure, weather disruption, insurance, higher turnover and the supervisory load of managing many small assets rather than few large ones. The cost-per-order advantage in our model is large, but these belong in the decision before it is made.

How does hub placement affect two-wheeler economics? More than it affects van economics. A bike carries its reload overhead across a small load, so additional distance to the hub is amortised over far fewer orders, which makes two-wheeler fleets considerably more sensitive to where the hub sits.