AI Logistics for Singapore Delivery Under New PMA Rules

Singapore’s new Personal Mobility Aid (PMA) rules are a rare thing in operations: a regulation change that instantly alters your “physics”. When the legal speed limit drops from 10km/h to 6km/h from 1 June 2026, the same route simply takes longer. Grab and foodpanda have already warned customers to expect longer delivery times during the transition.

If you run a delivery-heavy business—or you’re on the hook for customer satisfaction, rider supply, and cost per order—this matters beyond food delivery. The real lesson is about resilience: how quickly your operation can re-plan when constraints change overnight.

This post is part of our “AI dalam Logistik dan Rantaian Bekalan” series, where we look at practical AI for optimasi laluan, ramalan permintaan, and day-to-day execution. Here, the angle is straightforward: AI can’t change the law, but it can reduce the impact of the law—by tightening dispatch logic, improving ETAs, balancing incentives, and preventing avoidable minutes from piling up.

What changed in Singapore’s PMA regulations—and why it hits delivery ops

Answer first: The new PMA restrictions reduce available rider capacity and increase travel time per kilometre, which directly pushes up delivery times and cost per order unless platforms redesign routing, batching, and workforce planning.

From the CNA report (published 6 Feb 2026):

• The PMA speed limit on public paths will be reduced from 10km/h to 6km/h starting 1 June 2026.
• Size restrictions will apply to PMAs on public paths.
• Mobility scooter users will need a certificate of medical need.
• Platforms expect an adjustment period; riders relying on PMAs may complete fewer orders per hour.
• Platforms are preparing operational changes, including reclassification of riders who can’t meet eligibility requirements.

Operationally, this creates three immediate pressures:

1. ETA accuracy breaks first. If your ETA model learned from historical travel speeds, it’s now miscalibrated.
2. Supply/demand balance shifts. When riders deliver fewer orders per hour, the system needs more riders (or fewer orders) to keep service levels.
3. Compliance becomes a workflow. Medical certification verification is now a gating step, not a nice-to-have.

This is exactly the kind of disruption where AI and automation pay for themselves—because you’re not “improving efficiency” in the abstract; you’re preventing a measurable service-level drop.

The hidden math: why a 40% speed drop doesn’t mean 40% longer delivery

Answer first: The travel portion of delivery time increases sharply, but the total delivery time depends on waiting, pickup, batching, and handover—which AI can compress to offset slower travel.

A speed reduction from 10km/h to 6km/h is a 40% drop in speed. For the same distance, travel time becomes:

• Old time = distance / 10
• New time = distance / 6
• Increase factor = (1/6) / (1/10) = 1.67×

So the travel segment can be ~67% longer.

But most deliveries aren’t “pure travel”. A typical order includes:

• Order acceptance + rider assignment
• Travel to merchant
• Waiting at merchant (queue, prep time variability)
• Travel to customer
• Building navigation + lift waiting + handoff

If AI can shave even 2–4 minutes from assignment and pickup friction, that can partially neutralise slower travel—especially in dense areas where trips are short but delays stack up.

Here’s the thing: most companies get this wrong. They treat regulation shocks as “unavoidable” and only adjust customer promises. The better approach is to attack the non-legal constraints: dispatch latency, batching mistakes, pickup congestion, and forecast error.

Where AI helps immediately: routing, batching, and ETA recalibration

Answer first: The fastest wins come from AI route optimisation, dynamic batching, and next-generation ETA models that re-learn new constraints before customer trust erodes.

AI route optimisation under new speed constraints

Traditional routing engines often assume stable speed profiles. When the legal limit changes, your route cost function changes too. You want models that can:

• Re-weight path types (footpaths vs connectors, ramps vs stairs)
• Include micro-penalties for known bottlenecks (crossings, lift lobbies, crowded malls)
• Learn “time-of-day friction” (lunch peaks aren’t the same as late-night)

In practice, high-performing platforms run continuous route cost learning: travel times update from fresh telemetry rather than quarterly recalibration. If you don’t have telemetry, you can still use proxy signals (delivery timestamps, merchant prep times, zone congestion) to update weights daily.

Dynamic batching that doesn’t backfire

Batching (one rider, multiple orders) is tempting when rider throughput drops. But naive batching increases late deliveries if it ignores prep-time variance.

AI batching works when it’s constrained by:

• Predicted merchant readiness windows
• Maximum detour ratio (e.g., second drop-off can’t exceed +X minutes)
• Customer promise tiers (priority vs standard)

A simple, practical rule I’ve found effective: batch only when predicted prep-time overlap is high. If one merchant is consistently late, don’t use it as a batching anchor during peak.

ETA models: re-learn fast, or your CS costs spike

After 1 June, the “old world” data becomes partially misleading.

What to do:

• Use online learning or frequent retraining for ETA
• Separate components: to-merchant, merchant wait, to-customer
• Add a “policy change” feature flag so the model can learn a new regime

The payoff is immediate: better ETAs reduce “where is my order?” tickets and refund pressure.

Demand forecasting and workforce planning: the quiet way to protect margins

Answer first: When riders complete fewer orders per hour, the cheapest fix is not always “hire more riders”—it’s better demand forecasting and smarter shift/incentive planning.

The CNA article highlights a key platform reality: even without strict time limits, riders may earn less per hour if they complete fewer orders. If earnings fall, supply can drop further. That’s a feedback loop you don’t want.

AI helps in two linked areas:

Ramalan permintaan (demand forecasting) with operational constraints

Better forecasts allow you to:

• Pre-position riders in zones before spikes
• Adjust delivery promise times by zone (not blanket longer ETAs)
• Coordinate with merchants for prep-time management

Forecasting should incorporate:

• Calendar effects (weekends, paydays)
• Local events (concerts, sports)
• Weather signals (Singapore rain patterns can swing demand quickly)

Even modest improvements matter. If forecast error drops, you reduce expensive last-minute incentives and avoid over-promising.

Incentives that respond to throughput changes

If the system knows throughput is lower (due to speed limits), incentive design needs to change:

• Target coverage (zones/time slots) rather than raw order count
• Reward on-time performance under updated ETAs
• Avoid incentive rules that push risky behaviour

Done well, you keep supply stable without turning incentives into a blank cheque.

Compliance automation: medical certification and eligibility checks at scale

Answer first: Regulatory changes create new verification workflows; automation keeps them from becoming a manual bottleneck that hurts rider onboarding and dispatch accuracy.

The report notes that platforms plan to verify eligibility via official channels (e.g., LTA’s OneMotoring) and reclassify partners who can’t continue as PMA users.

For operations teams, this is a classic “small admin step” that becomes huge at scale.

A practical compliance automation stack looks like this:

• Document intake automation: structured capture of certificate data (OCR + validation rules)
• Workflow routing: exceptions go to human review, clean cases auto-approved
• Eligibility flags in dispatch: if a rider isn’t eligible, the app should prevent selecting PMA mode (no ambiguity)
• Audit trails: time-stamped decisions for internal and regulator queries

This isn’t glamorous, but it’s where AI in logistics proves value: fewer tickets, fewer disputes, fewer wrong assignments.

A 30-day AI playbook for Singapore delivery businesses

Answer first: Treat June 1 as a model shift—update data, re-tune dispatch, and adjust customer promises by zone, not by guesswork.

Whether you operate a platform, a fleet, or a chain doing your own deliveries, here’s a realistic sequence.

Week 1: Measure what’s actually changing

• Baseline current metrics by zone: on-time rate, average travel time, pickup wait
• Segment by mode (walker, bicycle, PMA where relevant)
• Identify top 20 “delay merchants” (high variance prep times)

Week 2: Fix ETAs and customer promises first

• Retrain ETA with new speed assumptions and a policy-change flag
• Update promise logic by zone and peak period
• Add proactive messaging for affected zones (reduce inbound support load)

Week 3: Optimise routing and batching constraints

• Update route cost weights using fresh delivery traces
• Turn on batching only where prep-time predictions are reliable
• Add detour caps and late-risk penalties

Week 4: Stabilise supply with forecasting + incentives

• Improve demand forecasts using calendar + weather inputs
• Shift incentives toward coverage and on-time delivery
• Monitor earnings per hour to avoid unintended supply drop

One-liner that holds up in real ops reviews: “Speed limits slow riders; bad dispatch slows everyone.”

What this means for AI dalam Logistik dan Rantaian Bekalan in 2026

Singapore’s PMA changes are not just a mobility story—they’re a live example of why AI in supply chain and last-mile logistics is becoming a baseline capability. Regulations change. Roadworks happen. Labour constraints tighten. The winners aren’t the companies that predict every disruption; they’re the ones that adapt fastest.

If you’re building or buying AI capability in Singapore this year, focus on systems that can:

• Recalibrate quickly with fresh data
• Explain decisions (why this rider, why this route, why this ETA)
• Embed compliance into workflows instead of bolting it on later

Regulation won’t be the last shock to your delivery timelines. The question is whether your operation learns in days—or in quarters.

Source referenced: CNA report on platform operators warning of longer delivery times following tighter PMA laws (published 6 Feb 2026).