intesol.kr
Cases / Logistics · Transport
Automation CASE 08

Delivery cost down 42%, efficiency up 58%

Route optimisation, demand forecasting, and warehouse automation rolled out across a large logistics operator over 20 months. To win driver acceptance, every route change shows its reasoning.

42%
Delivery cost cut
58%
Logistics efficiency gain
38%
Faster delivery
Duration
20 months
Team
5 people
Started
2024 Q2
Market
Korea 3PL
SITUATION

What was blocking them

ISSUE 01
Branch managers planned routes by hand
Routes were assembled by hand every morning, branch by branch, with wide variance between them, and empty running made up 19% of all distance driven.
ISSUE 02
Peak volume was never matched
With no forecasting, vehicles were assigned from last month's actuals, so peak-season overflow kept recurring.
ISSUE 03
The field did not trust systems
An earlier dispatch system had failed to reflect real road conditions, and drivers had simply ignored it.
APPROACH

The order we worked in

Each phase began from what the previous phase measured. We kept that order because without the earlier step there is no way to verify the next one's effect.

01
Route optimisation with visible reasoning
Constraint-aware route optimisation went in, and the driver app shows why this order, stop by stop. The reasons drivers deviated were collected and fed back into the constraints.
7 months
02
Forecast-driven dispatch
Volume forecasts by region, item, and period place vehicles and crews in advance. Forecast error is retrained weekly.
5 months
03
Warehouse flow automation
Inbound, sorting, and outbound flows were redesigned and work orders distributed automatically. Three bottleneck stations were handled first.
5 months
04
Rollout to all branches
Results from two validated branches became a standard operating procedure and rolled out network-wide. Branch-level exceptions are managed separately.
3 months
RESULTS

Baseline at kickoff, measured after delivery

Metric
Before
After
Cost per delivery
baseline
→
-42%
Empty running rate
19%
→
7%
Average delivery time
baseline
→
-38%
Peak-season overflow
2,100 / month avg
→
260 / month avg

How each figure is measured is set out in the measurement notes on the cases page. Revenue and cost figures come from the client's own accounting and are published only within the scope they approved.

“When drivers ask why this order, the app answers. That is when they started following it.”
Logistics · Transport · SVP of operations
Route optimisation Demand forecasting Logistics automation Visible reasoning in the field Standard operating procedure
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