AI Route Planning for SG Delivery Delays (Jun 2026)

A small change on paper can ripple through an entire delivery network. From 1 June 2026, Singapore’s new rules for personal mobility aids (PMAs) lower the speed limit from 10km/h to 6km/h, introduce size limits on public paths, and require some users (including mobility scooter users) to obtain a medical certificate of need. Platform operators like Grab and Foodpanda have already warned that customers should expect longer delivery times during the transition.

If you run operations, ecommerce, or a delivery-heavy business in Singapore, the headline isn’t “slower scooters.” The real story is this: regulation has become a variable you must model, not just a compliance box to tick. And the most practical way to respond is to upgrade how you plan routes, assign jobs, and forecast demand using AI in logistics and supply chain.

I’ve found that when businesses try to “manage delays” with manual dispatch tweaks, they burn time and still disappoint customers. There’s a better way to approach this: treat the new PMA rules as a planning constraint, then let AI do what it’s good at—optimising under constraints.

What Singapore’s new PMA rules change for delivery ops

The direct operational impact is straightforward: a lower maximum speed expands travel time for the same distance. Even if platforms don’t enforce a strict per-order time limit (as Grab noted), a slower average speed means riders can complete fewer trips per hour, which tightens supply during peak periods.

What’s less obvious—but more important—is how these rules create second-order effects across your delivery system:

Slower last-mile speed changes your peak-time maths

When rider throughput falls, the system’s “buffer” disappears first during:

• Lunch and dinner peaks
• Rainy evenings (February is firmly in Singapore’s wetter season patterns)
• Big promo days and weekends

That’s when small increases in travel time stack up into late orders, cancellations, and higher support volume.

A useful way to think about it:

If average rider speed drops ~40% (10 → 6 km/h), the system must recover that capacity somewhere else—better batching, shorter routes, denser zones, or more accurate ETAs.

You don’t need every rider to slow down for the effect to show up. You just need enough of your fleet to be affected in certain estates or time windows.

Eligibility verification introduces workforce volatility

Foodpanda has said it will verify medical certification via LTA’s OneMotoring platform and reclassify those who are not eligible to continue using PMAs as walkers. Operationally, that creates volatility:

• Changes in mode (PMA → walker) alter serviceable radius
• Mode changes affect on-time probability and batching feasibility
• Uncertainty around who is eligible can disrupt scheduling

In plain terms: your capacity isn’t just shrinking—it’s becoming less predictable.

Why AI route optimisation is the most practical fix

AI-powered route optimisation isn’t just “faster routing.” The value comes from handling real constraints that human dispatchers struggle to track consistently, such as:

• Mode-specific speed profiles (walker vs bicycle vs PMA)
• Path restrictions and “slow zones”
• Time-of-day congestion and lift/wait time patterns
• Prep time variability by merchant type

A strong AI logistics tool will continuously search for better assignments as conditions change—because it’s built around the idea that the plan is always slightly wrong.

What to optimise first: distance is not the right metric

Most teams start by minimising distance. With PMA speed capped at 6 km/h, time becomes the primary cost, not kilometres.

That means your optimisation target should shift to:

• Minimise end-to-end delivery time, not just travel distance
• Reduce variance (predictability beats occasional speed)
• Protect peak periods with smarter pre-positioning and batching rules

One stance I’ll defend: a slightly longer route with fewer crossings, fewer lift bottlenecks, and better handoff timing will outperform the “shortest path” in dense HDB clusters.

Constraint-based planning: the hidden superpower

The June 2026 rules create hard constraints (speed limit; size limits; eligibility requirements). AI is especially good here because modern optimisation approaches can treat constraints as first-class inputs:

• If a rider is tagged as PMA, cap speed assumptions and restrict certain paths.
• If a rider is reclassified as walker, shrink their delivery radius automatically.
• If a zone is high pedestrian density, bias routes to safer, wider connectors.

That’s how you reduce late deliveries without pushing riders into unsafe behaviour.

Three AI plays to mitigate delivery delays (and protect margins)

Here are three concrete ways Singapore businesses can use AI tools to keep delivery reliable under tighter mobility rules.

1) Dynamic ETA prediction that actually reflects the new reality

Customers don’t rage about a 45-minute delivery as much as they rage about a promised 25 minutes that becomes 45.

AI-based ETA models use signals like:

• Historical travel times by estate and time of day
• Weather and demand spikes
• Rider mode (walker/bike/PMA)
• Merchant prep time patterns

The key update for 2026: retrain ETA models with the new PMA constraints and stop treating “two riders in the same zone” as equal. They’re not.

Actionable step this month: build an internal dashboard that shows:

• ETA accuracy by zone
• ETA accuracy by rider mode
• Late delivery root cause split: prep vs travel vs handoff

If you can’t see those three, you’ll keep arguing about anecdotes.

2) Smarter batching (but only where it won’t backfire)

Batching (one rider handling multiple orders) is how platforms recover capacity. But with slower PMA speeds, batching can either save the day or torch your SLA.

AI can decide batching eligibility based on:

• Expected prep time alignment (don’t batch a fast drink with a slow cooked dish)
• Drop-off proximity and lift patterns
• Predicted handoff time windows

A practical rule I’ve seen work: batch only when the model can hold the “second customer penalty” under a strict threshold (e.g., +6 minutes). If you don’t cap that penalty, you’ll “optimise” cost by sacrificing repeat purchase.

3) Zone design and pre-positioning for PMA-affected areas

When travel speed drops, the best fix is often structural: change where you start from.

AI can help you redesign zones by clustering demand into tighter service areas and recommending rider staging points near:

• High-density dinner demand pockets
• Merchant clusters with consistent prep time
• Connectors with easier navigation (fewer crossings, fewer bottlenecks)

This matters in Singapore because micro-geography is everything. Two blocks can differ massively in lift wait time and footpath flow.

If you’re a restaurant group or retailer running your own fleet, consider a “micro-fulfilment mindset”: pre-stage high-frequency SKUs or prep-heavy items closer to demand.

Compliance isn’t the enemy—unmodelled compliance is

The platforms’ public messaging makes the intent clear: the rules are meant to improve footpath safety and reduce misuse of PMAs by able-bodied users. That’s not something businesses should fight. But businesses do need to plan for it.

Here’s the operational lesson worth keeping for the rest of the “AI dalam Logistik dan Rantaian Bekalan” series:

In Singapore, regulatory shifts are predictable in one way: they will keep happening. Your advantage comes from building systems that adapt quickly.

Practical checklist for ops teams before 1 June 2026

Use this as a short, high-impact runbook:

1. Segment your fleet by mode (walker, bicycle, PMA) and quantify share by zone.
2. Stress-test peak periods (Fri dinner; Sat lunch) using a slower-speed assumption.
3. Update ETA logic to reflect mode-based speed and zone bottlenecks.
4. Adjust batching rules with a hard cap on second-customer penalty.
5. Monitor capacity volatility caused by certification/eligibility transitions.
6. Communicate proactively: set customer expectations early; offer alternatives (pickup incentives, scheduled delivery windows).

Do these and you’ll avoid the classic trap: trying to fix systemic change with more customer support agents.

People also ask: will food delivery get more expensive in Singapore?

Direct price increases aren’t guaranteed, but cost pressure is real when throughput drops (fewer orders per hour) and customer support volume rises. The strongest counterweight is productivity: better routing, better batching, better ETA accuracy, and smarter zone design.

If you’re a merchant, watch two indicators:

• Cancellation rate (late ETAs cause drop-off)
• Basket size (customers add items when ETAs are reliable)

Reliability is revenue.

What to do next: build an “AI-ready” delivery operation

If your team is feeling anxious about June 2026, that’s rational. But the path forward is simpler than it looks: treat the new PMA rules as constraints, then use AI tools to optimise around them.

For Singapore businesses, the goal isn’t perfect speed—it’s predictable service, sustainable rider workflows, and margins that don’t evaporate during peak demand.

The next question worth asking is operational, not theoretical: which part of your delivery stack is the least adaptable today—routing, ETA, batching, or workforce planning—and what’s the fastest AI upgrade you can ship before June?

Source article (landing page): https://www.channelnewsasia.com/singapore/platform-operators-grab-foodpanda-warn-longer-food-delivery-times-personal-moblity-aids-pma-law-5912171