APAC Data Centres: The Quiet Engine of AI Logistics

An 80 MW data centre is being built in Incheon, South Korea—and if you run logistics or supply chain operations in Singapore, you should care. Not because you’re planning to colocate racks in Korea, but because projects like this are a signal: APAC’s “AI-ready” backbone is expanding, and that changes what’s practical (and affordable) for AI in routing, warehouse automation, and demand forecasting.

ESR’s new facility—KR1, a nine-storey build expected to be operational in 2028—will be leased to Princeton Digital Group (PDG) for fit-out and operations. On paper, it’s a data centre development story. In practice, it’s part of a larger shift: AI in logistics and rantaian bekalan increasingly depends on where compute sits, how quickly it connects, and whether power is stable enough to run it 24/7.

This post sits within our “AI dalam Logistik dan Rantaian Bekalan” series, where the theme is simple: AI only performs as well as the systems underneath it—data, integration, and yes, infrastructure.

What the ESR–PDG Korea data centre deal really tells us

Answer first: KR1 shows that capacity, power, and location are becoming strategic assets for AI adoption across APAC.

According to the source article, ESR is partnering with Wide Creek Asset Management to develop KR1 in Incheon’s Bupyeong district, highlighting proximity to Seoul’s tech corridor, strong fibre connectivity, and stable power. PDG will lease the whole facility and handle fit-out and operations.

Here’s the bigger point: hyperscale-grade facilities don’t get built on vibes. They get built where there’s secured land + secured power + dense fibre + enterprise demand. When investors and operators commit to 80 MW at a single site, it’s because they believe AI and cloud workloads aren’t slowing down.

For Singapore-based businesses, this matters because supply chains are regional. Your WMS, TMS, OMS, and forecasting tools don’t stop at Tuas. The performance you experience—latency, uptime, cost stability—depends on how resilient APAC’s data centre network becomes.

Why Incheon is an interesting choice (and why it’s not “just Korea”)

Answer first: Incheon is positioned as a connectivity-and-power sweet spot near a major economic cluster.

The article calls out:

• Proximity to Seoul’s technology corridor
• Strong fibre connectivity
• Access to stable power infrastructure
• Nearby Songdo International Business District (a major smart-city development)

If you’ve been watching logistics digitisation across APAC, that list looks familiar: data centres follow dense enterprise activity, and enterprise activity increasingly follows where digital infrastructure is reliable.

Data centres are the hidden constraint behind “AI optimisation” projects

Answer first: Most AI logistics initiatives fail to scale because compute, data movement, and reliability were treated as afterthoughts.

A lot of AI supply chain discussions focus on models—forecasting algorithms, computer vision, optimisation engines. But the day-to-day blockers are usually more operational:

• Real-time route optimisation needs low-latency access to current orders, traffic signals, driver status, and customer constraints.
• Warehouse computer vision needs consistent GPU capacity and high-throughput storage for video streams.
• Demand forecasting needs predictable batch windows and clean pipelines, not ad-hoc manual extracts.

All of that depends on infrastructure that can deliver:

1. Stable power (brownouts and “temporary capacity limits” are brutal for 24/7 ops)
2. Strong network connectivity (both domestic and cross-border)
3. Scalable compute (CPU/GPU capacity without month-long procurement cycles)
4. Operational maturity (monitoring, redundancy, incident response)

When KR1 highlights features like building-integrated photovoltaics and fuel cells, it’s not just green marketing. It points to a broader reality: power availability and sustainability targets are now procurement criteria, especially for customers running heavy AI workloads.

The AI logistics angle: what improves when APAC capacity expands

Answer first: More regional data centre capacity tends to improve cost predictability, latency, and resilience—the three things that make AI practical for SMEs.

Singapore SMEs often assume AI is “too expensive” or “only for big players.” I don’t fully buy that. The truth is more specific: AI is expensive when your architecture is inefficient—when you’re shipping data across regions, overpaying for peak capacity, or relying on fragile integrations.

As APAC adds capacity, businesses typically gain more options:

1) Route optimisation that actually runs in real time

Answer first: Better infrastructure reduces the gap between “data arrives” and “decision executes.”

If you’re doing AI route planning (AI mengoptimumkan laluan pengangkutan), you want:

• Frequent re-optimisation (every 5–15 minutes in volatile scenarios)
• Integration into driver apps and dispatch workflows
• High uptime during peak periods (year-end, mega campaigns, CNY)

Regional capacity growth helps because it supports more distributed architectures—closer compute, better peering, and fewer congestion points.

2) Warehouse automation with vision that doesn’t crumble at scale

Answer first: Computer vision is a throughput problem, not just an “AI model” problem.

Vision-based QA, dimensioning, safety monitoring, and pick verification require:

• Fast access to storage
• GPU capacity when you need it
• Clear operational controls (who can retrain, who approves model changes)

More mature data centre ecosystems support colocation and cloud adjacency patterns where latency and data gravity are managed intentionally.

3) Demand forecasting that isn’t a monthly science project

Answer first: Forecasting improves when pipelines are reliable and compute is predictable.

Demand forecasting (ramalan permintaan) often fails because:

• Data is split between ERP, marketplaces, 3PL portals, and spreadsheets
• The “training job” competes with other workloads
• No one trusts the output enough to act on it

Infrastructure won’t fix messy data. But it does make it far easier to run repeatable pipelines and maintain SLAs that business teams can rely on.

Partnerships like ESR + Wide Creek + PDG are shaping AI-ready ecosystems

Answer first: The partnership model shows how the industry is splitting roles: developers secure land/power, operators run the facility, customers consume scalable capacity.

The article outlines a clean division of responsibilities:

• ESR: development manager; overall facility design
• ESR + Wide Creek AMC: core and shell construction
• PDG: tenant; internal fit-out and operations

For business leaders, this matters because it affects:

• How quickly capacity comes online
• How standardised (or customised) facilities are for AI workloads
• How pricing and service levels evolve across the region

It also hints at the next phase: AI is driving data centre specialisation. “General purpose” compute is fine for many workloads, but AI-heavy operations increasingly care about:

• Higher power density
• Cooling strategy
• GPU supply chain and maintenance
• Stronger interconnection options

Practical checklist for Singapore logistics teams planning AI in 2026

Answer first: Treat infrastructure decisions as part of the AI business case, not an IT detail.

If you’re exploring AI untuk logistik dan rantaian bekalan this year, here’s what I’d put on the whiteboard before picking tools.

A) Classify workloads by latency sensitivity

• Real-time (seconds): dispatch/routing updates, fraud flags, safety alerts
• Near real-time (minutes): inventory rebalancing, slotting recommendations
• Batch (hours/days): demand forecasting, network design, cost-to-serve

This determines whether you need edge processing, regional cloud, or a hybrid setup.

B) Decide where data should live (and why)

Ask two blunt questions:

1. Where is the system of record? (ERP/WMS/TMS)
2. Where will the model run in production?

If those are in different places, you’ll pay a “data movement tax” forever.

C) Build a reliability plan that matches operational reality

AI that fails during peak periods is worse than no AI.

• Define RTO/RPO targets for AI-dependent workflows
• Ensure monitoring is tied to business KPIs (late deliveries, pick rates)
• Document fallbacks (rule-based routing, manual waves, safety overrides)

D) Budget for integration, not just the model

In most projects I’ve seen, integration effort beats model effort.

Allocate time and cost for:

• Data mapping and master data cleanup
• API/EDI connections with 3PLs and carriers
• Ongoing model monitoring (drift, bias, seasonality)

People also ask: does a Korea data centre impact Singapore businesses?

Answer first: Indirectly, yes—because APAC capacity and connectivity influence cloud pricing, redundancy options, and regional performance.

You may never host a workload in Incheon. But when more capacity comes online across APAC hubs, it typically:

• Expands options for multi-region resilience
• Improves peering and cross-border connectivity
• Encourages more competition among operators and service providers

For logistics businesses that operate across SEA and North Asia, that flexibility is valuable—especially when you’re running customer-facing tracking, ETA predictions, or AI-driven exceptions management.

Where this leaves AI in logistics and rantaian bekalan

KR1 is scheduled for 2028 operations, and it’s part of ESR’s stated 3.2 GW APAC pipeline (secured land and power). That’s a long runway—and that’s the point. Data centre planning is slow because power, permitting, and construction are hard. Meanwhile, AI adoption is speeding up.

If you’re a Singapore business leader, I’d treat the next 12 months as the planning window: get your data foundations right, standardise integrations, and pick AI use cases with a clear operational owner. By the time new capacity across APAC comes online, the companies that win won’t be the ones “starting AI.” They’ll be the ones ready to scale it.

Want a practical next step? Choose one workflow—routing, warehouse picking quality, or demand forecasting—and map it end-to-end: data sources, decision points, failure modes, and latency needs. You’ll immediately see whether your bottleneck is the model… or the infrastructure underneath it.

What part of your supply chain would improve first if AI decisions were available in seconds, not hours?