Research

Research that turns loss into lessons

We study how freight is lost, stolen, or tampered with, and test ways to stop it under real conditions. Every project ends with something teams can use.

Focus areas

What we study

Six areas guide our work. Members vote on priorities each year at LIRG Kickoff.

01

Cargo theft patterns

Where, when, and how loads go missing, and which controls make a real difference.

02

Fraud and identity

Fake pickups, double brokering, and stolen carrier credentials. How to spot them before a load leaves.

03

Load and seal integrity

Tamper-evident seals, seal checks, and photo evidence that a load arrives the way it left.

04

Gate and yard operations

Driver and truck checks at the gate, yard visibility, and the points where custody changes hands.

05

Detection models and data

Risk scoring, anomaly detection, and computer vision, built in the open and measured on shared benchmarks.

06

Workforce and insider risk

Training, screening, and process design that help people do the right thing and spot the wrong one.

Our process

How a study works

Define

Members and researchers agree on the question and what a useful answer looks like.

Collect

We gather data from members and field visits, under clear rules on privacy and use.

Test

Ideas are tried in real yards, docks, and lanes through LIRG Labs.

Share

Findings come out as short briefs, open tools, and practical standards.

Publications

Latest from our research

Our first publications are in progress. Members see them before public release.

Research brief

[Title of first brief]

A short read on one finding and what teams can do about it.

Coming soon
White paper

[Title of white paper]

A deeper look at one problem, the evidence, and the options.

Coming soon
Field study

[Title of field study]

Results from a pilot run at a member site through LIRG Labs.

Coming soon
Principles

How we hold ourselves to account

Evidence based

We report what the data shows, including what did not work.

Practical

If a team cannot use it, the work is not done.

Privacy aware

Shared data is kept to what a study needs and is well protected.

Vendor neutral

We test solutions fairly and do not endorse products.

Seeing a problem worth studying?

Tell us what is happening in your operation. Good questions start good research.