What planning teams say after working with us

Challenge

A Klang Valley 3PL was spending roughly fourteen planner-hours per week manually chasing ETA updates on their Port Klang–Singapore corridor. Carrier data was fragmented across email, WhatsApp groups, and a TMS that hadn't been configured for visibility beyond bookings.

Solution

Lane & Node Diagnostic over three weeks. We reviewed 90 days of shipment records, mapped the ETA data sources available, and identified two carrier feeds that could be connected to their existing TMS without custom development. Delivered a written brief with four prioritised actions.

Outcome

The brief identified one carrier integration that reduced manual ETA checks by an estimated 60% on that corridor. The client implemented two of the four recommendations within six weeks of receiving the brief using their existing IT team.

~60% reduction in manual ETA checks

Challenge

A Penang-based electronics component distributor was managing over 200 active shipments per week across four trade lanes. Their planning team of five was spending most of the morning shift reviewing individual shipment statuses before any customer communication could begin.

Solution

ETA & Exception Co-Pilot with read-only TMS integration. Over six weeks, we supervised the rollout of an exception clustering assist that grouped shipment exceptions into three daily review batches. Weekly clinics helped the team learn when to override the AI groupings.

Outcome

Morning status review time reduced from approximately 2.5 hours to under an hour for the same shipment volume. The planning team described the main benefit as fewer interruptions during the afternoon shift when customer communication volume is highest.

Status review time: 2.5h → <1h

Challenge

A Johor Bahru logistics firm had introduced AI-assisted demand forecasting for their warehouse network in 2023 but had no process for reviewing whether the model was still reading trade patterns accurately as seasonal volumes shifted.

Solution

Network Stewardship Retainer from October 2024. Monthly model behaviour reviews are written as a two-page brief for the operations director. The Q1 2025 brief identified a seasonal drift in the Johor–Singapore truck corridor model that was under-reading peak festive volumes.

Outcome

The festive season model correction was applied before Chinese New Year 2025 volume peaked. The operations director noted that previous years had seen a three-to-four day response lag when the AI model failed to anticipate the volume spike. In 2025 the transition was managed without that lag.

CNY 2025 volume spike managed without lag