Simple Yard

AI in Transport & Logistics

An online platform built for learners who want to understand how AI is reshaping freight, routing, and supply chain decisions — not in theory, but in practice.
Interactive quizzes and scenario-based assignments that reflect real logistics challenges, from last-mile delivery to predictive demand.
Instant feedback on every answer, so learning happens in the moment — not after the fact.

AI systems displayed in a logistics control environment

What Simple Yard actually does

Simple Yard is an educational platform focused on one specific area: how artificial intelligence is being applied in transport and logistics. That includes route optimisation, warehouse automation, predictive freight demand, and driver behaviour analytics.

The platform is built around testing knowledge rather than just delivering it. Learners work through quiz assignments that present realistic scenarios — a fleet manager deciding whether to reroute mid-trip, a warehouse system flagging anomalies in picking patterns — and receive immediate, specific feedback on their reasoning.

We are based in Brossard, QC, and serve learners across Canada through a fully online infrastructure. The content is written by people who have worked in logistics operations, not just studied them.

The people behind it

A small team with direct experience in logistics operations and digital education.

Tobias Wren

Curriculum Lead

Tobias spent eight years working in freight brokerage before moving into education. He designs the quiz scenarios and makes sure they reflect decisions people actually face on the job.

Adaeze Obi

Platform Lead

Adaeze oversees the technical side of the platform and the learner experience. Her background is in instructional design, with a focus on feedback systems that help people understand where their reasoning went wrong.

Miroslav Hajek

Subject Advisor

Miroslav advises on content accuracy across the AI and supply chain modules. He has spent over a decade in logistics software implementation across Central and Eastern Europe.

Priya Nambiar

Content Reviewer

Priya reviews quiz questions for clarity and fairness before they go live. She previously worked in quality assurance for an e-learning company serving post-secondary institutions.

How we approach the work

Three principles that shape how content is built, reviewed, and delivered on the platform — not aspirations, but actual constraints we work within.

Logistics data visualisation on a screen

Specificity over generality

Every quiz question references a concrete situation — a specific vehicle type, a specific constraint, a specific trade-off. Vague scenarios produce vague learning.

We review all questions against real operational data before publishing. If a scenario could not plausibly happen in a Canadian logistics context, it does not go live.

Feedback that explains, not just scores

Getting a wrong answer is only useful if you understand why it was wrong. Our feedback messages explain the reasoning, not just the correct option.

Each incorrect answer has a dedicated explanation written by the curriculum team, not auto-generated. This takes longer to produce but makes a measurable difference in retention.

Team reviewing logistics planning on a tablet

Content that ages honestly

AI in logistics is moving fast. We flag when content was last reviewed and update modules when the underlying technology or industry practice has shifted significantly.

Learners can see the review date on any module. If something is more than 18 months old without a review, it is marked as pending update — not silently left in place.