This is the second post in our Supply Chain AI series. For an introduction to AI and how ML can automate freight logistics, read our first blog: How machine learning can help move your freight.
Chocolate and peanut butter. Lennon and McCartney. Machine learning and freight. A few things in this world were destined to be together.
Convoy uses machine learning throughout the lifecycle of a shipment to make operations more efficient and reliable. In fact, our use of ML starts in the procurement phase, when we price and tender freight.
Pricing contract freight accurately with machine learning
Most truckload rates are established through an RFP process in which a carrier bids on freight that a company (a shipper) needs to transport. You might wonder, how do carriers establish their prices for contracts?
Traditional carriers price their bids based on a rudimentary cost-plus model, according to MIT Supply Chain Management. Cost-plus is exactly what it sounds like: taking the cost to operate a truck and add a markup. Unfortunately, this pricing method is prone to error, as it may not factor in how fluctuations in operating costs and demand shifts impact transactional rates.
Convoy uses artificial intelligence to take a more comprehensive approach to pricing. Our supply chain machine learning models analyze millions of data points, including historical shipment records, near-time volume, capacity indicators, seasonality shifts, shipment time of day, macroeconomic factors, regional carrier density, carrier quality, and more.
We have seven distinct machine learning models focused on pricing alone. These models analyze this data to estimate truck prices over the full duration of the contract. Every bid that we submit is based on the output of these models.
By using machine learning, Convoy can forecast more accurately and price appropriately so that we accept a higher proportion of the loads we agree to take. How does this compare to the cost-plus option?
Convoy’s tender acceptance rate is higher than 95% for contract freight, even in tight markets. This is much higher than the rest of the industry: an MIT study found that the average tender acceptance across US truckload was under 75% for primary carriers. Another MIT report found 80% tender acceptance, and that each rejection increased shipment costs by an average of 14.8%.
This saves shippers overall transportation costs, and brings greater predictability and reliability to their supply chain planning.
Driving higher tender acceptance with AI
In addition to developing accurate pricing, machine learning boosts Convoy’s tender acceptance rates by using AI and ML to calculate the likelihood that we can match a load to a carrier in our network.
This calculation informs our pricing and tender acceptance for contract freight. It also applies to accepting tender for backup freight (loads allocated through a shipper’s routing guide) and spot market freight. Central to this is a machine learning model that calculates what we call the “supply availability score.”
We have a supply chain machine learning model that actively calculates a supply availability score for each load a shipper tenders to Convoy. This score determines the likeliness of our ability to find a truck and service the freight.
This involves analyzing both historical data and real-time variables that are constantly moving. The supply availability score is based on tender lead time, capacity in the market, the number of carriers in our network in the facility’s region, the required truck type, whether a lane can be batched with other Convoy loads, and other factors.
Because our machine learning supply chain models operate continuously, we establish a supply availability score immediately after a load is tendered. This has a few key benefits:
- We accept tender quickly for contract freight
- We guarantee coverage for backup and spot freight, so the price you see is the price you get
- We assign loads to carriers fast, so you spend less time wondering if your load is covered
Pricing and accepting the tender from the shipper is still only the beginning of the process. The next phase in machine learning is matching the load with the best truck for the job.
Finding a quality carrier for every shipment
Central to Convoy’s digital freight network is the matchmaking process of connecting loads with carriers. This is another area in which machine learning excels.
If you add up the potential combinations of trucks in Convoy’s network with the loads that need to be transported, you wind up with billions of possible outcomes. This calculation is further complicated when you account for:
- Identifying the highest-quality carrier for the load
- Ensuring we can offer shippers the best price possible
- Covering a load as quickly as possible
Of course, at some point a judgement needs to be made. The lowest cost carrier may not have the highest on-time performance record. Weighing these is a complex measurement that requires analyzing massive sets of data that are constantly changing. Said another way: it’s the perfect place where machine learning can optimize supply chains.
Prioritizing on-time performance and safety
Convoy’s machine learning models offer loads to carriers who are most likely to make pickups and deliveries safely and on time. When we tender loads, we’re willing to pay more to carriers who are the best match. This helps us source drivers who are less likely to fall off, more likely to be on time, and whose records show fewer safety incidents and cargo claims.
Each time a carrier hauls with us, we collect more data on quality and performance. This data informs our machine learning model. When carriers make successful pickups and deliveries, we reward them by offering access to desirable loads. This motivates better performance throughout our network, translating to more reliable service for customers who ship with us.
Using AI to assign better loads, faster
One of the pain points that truckload carriers face is securing backhauls for their trips back home. Once making a drop, carriers may spend hours sifting through load boards for the shipment that takes them in the direction they want to travel.
This is another pain point we addressed with machine learning. When a carrier uses Convoy’s app, our ML models curate the most relevant loads to them. We detect the driver’s current location, the type of trailer they’re hauling, and highlight nearby loads that will route carriers in the same direction as their homes.
What this means for you: faster response times and less time wondering your load is covered. How much time exactly?
We conducted a study of Convoy Connect data assessing the response time for carriers responding to contract tenders. The median tender response time for traditional brokers and assets is between 33 and 58 minutes minutes. With Convoy, the median response time is only 6 minutes.
Adding it up: Peace of mind with artificial intelligence
“If we plug it into Convoy, they’re doing the legwork for us and we know we can trust the system to get it taken care of. So we’ve been able to reduce the time we spend on a load almost by half.” – Encore Glass
By building our digital freight network on the foundation of machine learning technology, we’re able to rapidly advance supply chains by providing more accurate pricing, higher tender acceptance, and faster confirmed responses. This adds up to more certainty, stronger reliability, and more peace of mind for those who ship with Convoy — all before a load leaves their docks.
For more information about machine learning and freight:
- Book a Truck with Convoy to gain the benefits of machine learning in freight today.
- Read our blog: Supply Chain AI: How machine learning can help move your freight
- Read our Freight Shipping Guide to learn more about how technology can overhaul your transportation strategy.