How TNX leverages data

Our AI Pipline

In general, the software uses AI in the procurement process to automate pricing & tendering decisions. If you imagined AI as a very smart black box, the platform would look like this.

But obviously you want to know what is in the AI black box. The diagram below shows how the platform is architected. We call this our AI pipeline, because it shows how data comes into the platform, is progressively treated with different AI techniques, and ends with tenders to carriers. Also note the feedback loops embodied in the process.

Each of the boxes in blue is a specific data science tool or process which adds value.

The first step is to profile the approved carriers, understanding what they will be interested in and how this intersects with work they might be offered. Profiling combines 3rd party data, historical data, and the carrier's own reactions to offers made to them on an ongoing basis. Our carrier profiling is described in detail here.

Second, the software looks for logically superior ways to use a carrier's trucks. It ends with what we call “bundles”, which are specific ways to schedule, consolidate, sequence, and therefore execute a combination of loads. Bundles can be FTL multistop combinations, or LTL consolidations.
Obviously most loads won't be bundled, so 80% or more of loads are tendered out in the same way they appeared originally in the TMS: as a single pickup and delivery. But when bundling works, the savings are substantial. Our bundling is described in detail here.

Third, we predict prices for every single load or bundle. This prediction is dynamic and based on a combination of historical load data, carrier profiling, and 3rd party market indexing. Our price predictions are described in detail here.

Finally, we select a strategy of how to procure capacity for the single load or bundle. A strategy means a specific set of tactical moves around the carrier inclusion, price negotiations, and timing of offers. whereas bundling is about objectively better use of vehicles, tender strategy is about subjectively attractive offers to carriers that achieve the best margin possible for the broker. Our tender strategies are described in detail below.

Carrier Profiling

Machine Learning on Carrier Profiles

Profiling of carriers is crucial to understanding how their interests intersect with the loads available. This is a critical input to decisions such as if or when to notify carriers about new loads, what price to offer them, or how to consolidate single-moves into multistop offers to best meet their needs.

The image above shows an example of a carrier, and highlights where they are best suited for pickups and deliveries. Note that this isn't just where the moved loads in the past. This is showing where they were more price aggressive, faster to reply, or more likely to accept loads.

Bundling

Our bundling is about producing logically superior ways to use a truck. It leverages a branch of data science called "Symbolic AI". Symbolic AI means accurately describing the state of the world, the actions available, and goals we want to achieve. For the platform, this means knowing when its possible to combine road transport jobs and when that would lead to better carrier pricing.The end result is what we call “bundles”, which are specific ways to schedule, consolidate, sequence, and therefore execute a combination of transport orders. We offer bundling for FTL to create continuous moves, and also bundling for LTL to consolidate partloads.

Why bundle at all?

In theory, procurement should start with the best load combinations. In practice, this isn't often true. Business units or sites may procure in isolation from the same carrier, in effect overpaying. Partloads may be tendered when they could be successfully aggregated to save money. And the blending of private fleet with for-hire carriers often follows rules of thumb that would be best properly tested each day. In all these situations, bundling via TNX delivers a lower cost-to-serve.Bundling of work into more attractive offers is a core part of good road procurement. But its not the only AI that TNX brings to the problem.

Price Prediction

Predictive Power

Price predictions are a major part of our AI data pipeline. Predictions learn from past carrier behavior, and 3rd party market data, in order to predict market price and carrier-specific preferences or reactions. We bring these predictions into both automated tendering decisions and also recommendations for the carrier reps when they decide to contact carriers personally.

Tender Strategies

What are strategies?

A strategy is a distinct set of tactics, defining how to procure capacity for a given transport job. In a few seconds, the platform uses a strategy from its portfolio to intelligently decide on these points: The price range to bring to procurement, the timing of carrier offers, the offer to make, and the carriers to engage at each moment. The diagram below shows these decisions in sequence.

What is the Goal?

This area of the software uses a kind of machine learning called "Reinforcement Learning". A key question here is "what is the software learning to do". In general, the software is learning how to best achieve and balance three procurement goals:

Lower Spend

Find the carrier within your approved list who is willing to provide capacity at the lowest cost-to-serve.

Reduce Risks

Avoid delays in execution, rejecting of tenders, and cancellations of jobs after acceptance.

Manage Carriers

While staying in carrier-specific spend commitments, rightsize the carrier pool by laneway.

Our AI Methodologies

For the technical visitor who wants to know more, the software applies two AI methodologies to the needs of trucking.

Statistical AI - Machine learning

It is premised on the idea that a large volume of historical, current, and future data has patterns of importance. The platform discovers those patterns, and uses them to predict key outcomes. It is called machine learning because greater experience improves the predictive power of the platform.

Symbolic AI

Symbolic AI does not necessarily require learning from experience (although that can help). Instead of focusing on learning from experience, symbolic AI tries to accurately describe the state of the world, the actions available, and goals we want to achieve. With that, the AI acts as a rational agent trying to achieve goals with allowed decisions and with an expectation of how the world will react to them.

The symbolic AI side is mostly focused on bundling, while various forms of machine learning (reinforcement learning and supervised learning) are using in carrier profiling, price predictions, and tender strategy.

All of these AI methods are applied “in the middle”, between the load coming to the software and the tender given to the carrier. If the software does its work right, the users on either side  never see the complexity of the AI, just the quality of its results.

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