Teaching a marketplace what to pay
The question
How do you balance transportation cost against the risk of failing to procure supply?
Middle-mile transportation runs on a marketplace of carriers. Price a load too low and you fail to procure the truck you need; price it too high and you pay more than you should. Amazon already had a reinforcement-learning pricing model for spot truck procurement in the US. As owner of middle-mile transportation strategy and roadmaps across India, Japan, Mexico, Brazil, Australia and the Middle East, my question was whether that model could make good decisions in a very different market.
The people behind the problem
Procurement teams live with a daily trade-off: protect cost or protect coverage. Carriers, on the other side, respond to price in ways that differ from market to market.
Before anything could be built, global product and technology teams also needed a shared view of what each region actually required.
Reframing the real problem
The easy assumption was that a model proven in the US would simply transfer. But a marketplace model is only as good as the market it learned from, and carrier behaviour, lanes and pricing norms in Japan are not the US.
So the product question wasn't "should we use reinforcement learning?" It was "what has to change for this model to make good decisions in Japan, and how will we know before it goes live?"
Making progress responsibly
I partnered with science and engineering on historical-data preparation, market-specific model inputs, simulations and acceptance criteria for Japan and India, so the model was evaluated before rollout rather than after it.
We launched in Japan, balancing transportation cost with successful capacity procurement, and improved spot-procurement costs by 11%. Defining what "good" looks like before a model ships is the same discipline I now apply to building AI products.
Outcomes
- Launched reinforcement-learning pricing for spot truck procurement in Japan, improving costs by 11%
- Adapted an existing US model to regional marketplace needs, balancing transportation cost with successful capacity procurement
- Defined data preparation, market-specific inputs, simulations and acceptance criteria for Japan and India before rollout
- Aligned global product and technology teams around regional requirements across India, Japan, Mexico, Brazil, Australia and the Middle East
Headline impact: 11% improvement in Japan spot-procurement costs
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