PSA International cuts empty truck trips nearly in half with AI route optimization at Singapore ports
“PSA International cuts empty truck trips nearly in half with AI route optimization at Singapore ports” documents a Route & Fleet Optimization deployment in Logistics & Freight at PSA International. www.cnbc.com reports empty truck trip reduction: Nearly halved (from ~35% to ~17–18%); this directory has not independently verified that result.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 3 cited below
- Directory entry published:
- Source link checked:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: www.cnbc.com
The Challenge
Singapore's port ecosystem ranks among the world's busiest transshipment hubs, yet the haulier industry that supports it operated with chronic inefficiency. Manpower shortages, rising fuel costs, and poor load coordination left up to 35% of trucks departing port terminals running empty — a condition logistics operators call deadhead or empty-leg movement. For individual hauliers working on thin margins, each unloaded trip represents direct fuel expenditure and driver hours with zero revenue return. Multiplied across hundreds of operators and millions of container movements annually, that 35% empty-trip rate imposed a systemic drag on profitability, port throughput, and the competitiveness of Singapore's broader supply chain infrastructure.
The Solution
PSA International partnered with HERE Technologies, a Dutch location technology company, to deploy OptETruck — a transport management system built specifically for port haulage. The platform applies automation and real-time route optimization to match available loads against trucks that would otherwise depart empty, using optimization algorithms to evaluate trip combinations dynamically across the active fleet. Critically, OptETruck covers the full operator workflow: trip planning, trip execution, and through to invoicing and billing. By addressing the complete operational loop rather than just the dispatch function, the system reduced the friction that had historically slowed digital adoption among smaller, independent hauliers. Onboarding scaled progressively, and each additional truck joining the network improved load-matching quality for all participants — creating the compounding returns typical of marketplace-style logistics platforms.
Results
The headline outcome was a near-halving of empty truck trips across the platform:
- Empty trip rate: reduced from approximately 35% to 17–18% — roughly a 50% improvement
- Fleet onboarded: 400+ trucks integrated onto the platform
- Market penetration: approximately 20% of Singapore's total haulage market covered
Reaching one-fifth of the haulage market signals that OptETruck addressed genuine operator pain points rather than only the port's efficiency goals. Fewer empty runs also translate to lower fuel consumption and reduced emissions per container moved, adding a sustainability dimension to the operational gains.
Key Takeaways
- Real-time load matching can reduce deadhead rates by roughly 50% even in dense, high-variability port environments — the complexity is addressable with the right optimization approach.
- Full-stack coverage (planning → execution → billing) drives adoption faster than point solutions; operators won't change dispatch behavior if invoicing still runs on a separate system.
- Network effects are central to load-matching AI: each truck onboarded improves match quality for all others, making early ecosystem growth a strategic priority.
- Achieving ~20% market share appears to be a meaningful threshold for load-matching effectiveness — fragmented adoption limits the algorithm's ability to find viable matches.
- Port operators have a structural advantage as platform anchors due to their visibility across all inbound and outbound movements.
Explore Related
Details
- Industry
- Logistics & Freight
- Use Case
- Route & Fleet Optimization
- AI Technology
- Reinforcement Learning & Optimization
- Company Size
- Enterprise
- Company
- PSA International
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
- Source link checked
Cited source
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