Digital Freight Matching. Building an AI-Based Tool for Personalized Load Matching Recommendations

The freight forwarding industry is under pressure. Margins are tight, and the digital revolution is changing how business is done. Forwarders need to step up in the digitalization process and find new ways to optimize their operations and improve their bottom line. One area where significant improvements can be made is in the digital freight matching process. In this article, we will discuss how machine learning solutions can automate carrier matching and enable a more sustainable and secure future of supply chains with optimized logistic networks.

The State of Supply Chain Management

Supply Chain Management (SCM) is the process of planning, executing, and controlling the operations of supply chain networks. It includes the coordination and collaboration of all activities from suppliers to manufacturing to logistics, and finally, to customers. The goal of SCM is to fulfill customer demand in a timely manner while minimizing cost.

The digitalization of SCM started in the early 2000s with the introduction of enterprise resource planning (ERP) systems. These systems helped companies automate their internal processes and better overview their supply chains. However, they did not solve the issue of connecting different parts of the supply chain or managing external partners.

In recent years, this has changed with the rise of new technologies such as big data analytics, the internet of things (IoT), and machine learning. These technologies enable the development of new digital platforms that automatically connect different parts of the supply chain and make SCM more efficient. Businesses that can smartly leverage these modern technologies will have a significant competitive advantage in the future. And with the growing shipper demand and number of loads to be transported, the time to do so is now.

Traditional Load to Carrier Matching

In the traditional load to carrier matching process, freight forwarders source loads from shippers and match them with carriers. This is a manual process that requires a lot of time and effort. The process starts with the shipper asking for a freight quote (or request for a proposal) by sending load and route details. First, the forwarder needs to find carriers for each load. Then, they need to bid on loads with shippers and carriers. Finally, they need to organize shipping, assign truck drivers and provide the track and trace part of the management.

Freight forwarding is a high-paced and competitive industry, so the forwarder needs to be quick and efficient in this process. The manual process is time-consuming, and when you cannot provide a proper quote to the shipper or find a suitable carrier, you’re likely to lose this deal.

How Does Digital Freight Matching Work?

Digital freight marketplaces are platforms that enable direct connection between shippers and carriers. Usually, shippers submit their loads, which carriers can find through dedicated search engines and bid on these loads directly through the marketplace. Shippers can then select the best bidder and finally order shipment. This process is already much faster and more efficient than the traditional way of load to carrier matching. In addition, digital freight marketplaces often offer other features such as fleet and transport management systems (TMS), real-time tracking, and payment management that make SCM even more efficient.

Digital freight matching is a step forward by cutting the middleman, but it is still mostly manual. A carrier must search for loads that match their criteria, and a shipper must select the best carrier from the list of bids. This process can be further automated with machine learning.

Automating Digital Freight Matching with Machine Learning

Machine learning is a subset of artificial intelligence (AI) that enables computers to learn from data and experience. In the context of digital freight matching, machine learning can be used to develop algorithms that automatically match loads with carriers. These algorithms consider various factors such as load size, route, carrier preferences, and capacity. As a result, they can instantly provide more personalized and accurate recommendations than manual processes.

There are many potential applications of artificial intelligence in digital freight matching. Here are some examples:

Load to Carrier Recommendation

If you’re in the transportation or logistics business, then you know that having multiple systems and relying on manual processes often leads to inefficient routes and taking unprofitable loads. This not only causes fewer profits for carriers and too expensive solutions for shippers but also creates a bad experience overall. However, by leveraging the power of recommendation engines, businesses in this industry can assess a range of variables and automatically make more intelligent decisions when automatically matching loads.

The load to carrier recommendation system considers the load characteristics (size, route, etc.) and compares them with the carrier preferences to automate the process of load matching. Based on this information, the algorithm recommends each load to selected carriers. This way, the load is more likely to be matched with an appropriate carrier, and the process is much faster.

The machine learning recommendation engine makes it possible for the system to proactively suggest new loads to the carrier even before they ask for loads on the digital freight marketplace. It allows digital freight matching companies to get a head start on their competition and win more transactions for themselves as the service provider. It is also crucial from the carriers’ perspective - the better the load matching engine, the more quality deals they can make through the platform, and the higher their engagement rate and overall availability of trucking companies will be.

Capacity Management

Another application of machine learning in digital freight matching is capacity management. Capacity planning is critical to any business, especially in the transportation and logistics industry.

Predictive analytics algorithms can be used to forecast the demand for digital freight services. The capacity management algorithm analyzes historical data, monitors the number of trucks available in a particular area, and predicts future demand. This information can then be used to optimize the use of resources and ensure that there is always enough capacity to meet customer demand. It also helps them avoid overbooking and underbooking, leading to lost business and unhappy customers. In addition, machine learning can be used to identify trends in the data and make recommendations about how businesses can adjust their operations to meet future demand better.

Shipment Consolidation

Shipment consolidation is another area where artificial intelligence can be used to improve digital freight matching. In LTL shipments (less than truckload), when shipments are consolidated, it means that multiple small shipments are combined into one larger shipment. This can often lead to cost savings for shippers and more attractive deals for trucking companies, opposite to driving solo with unused truck space.

Manual shipment consolidation is tricky; you must compare routes and dates, truck capacities, and cargo sizes. Machine learning can instantly identify consolidation opportunities, make recommendations about how best to combine shipments, and automatically generate consolidation proposals for carriers. The shipment consolidation algorithm groups loads that are going along similar routes, have matching delivery time frames, and proposes route optimization opportunities. With the use of dynamic pricing models, LTL shipments can also be instantly quoted.

Related case study: Delivering a dedicated IT system to manage and sell freight deals and plan transportationA major logistics company approached us to create a dedicated IT system to handle their core business process – managing and selling logistics deals.Our challenge? The key challenge in the freight forwarding sector is cutting the time of concluding deals to an absolute minimum. The tool has to be very responsive and help in the smart matching of carriers and freight, fleet management, and other logistics operations. The platform helps shipping agents minimize fuel consumption, maximize operational efficiency, and optimize fleet performance by matching multiple loadings on a similar route with a single carrier. Read more about this case study.

Headhaul Optimization

To reduce empty kilometers and deadheads, digital freight matching platforms use headhaul optimization. A deadhead is when a truck travels from its current location to pick up a load and then returns empty after delivering the shipment. This creates unnecessary emissions and wasted fuel, which hurts the environment and carrier profitability.

Using machine learning, businesses can identify opportunities for headhaul optimization and make recommendations on load matching and how best to route shipments. Predictive analytics applied to supply chain data can forecast the probability of finding the next load within a specific location and timeframe. This way, digital freight matching companies can suggest the best places for carriers to find their next shipment, reducing empty kilometers and deadheads. It is also a crucial aspect of adequately pricing each load - the pricing within a logistics network needs to reflect the actual transport cost, including empty kilometers.

A more efficient logistics market that reduces the number of deadhead kilometers also improves the sustainability of a shipper’s supply chain.

Dynamic Pricing

Dynamic pricing is the last missing piece of a fully automated freight matching platform. Dynamic pricing is when the price of a product or service fluctuates in real-time based on demand. Dynamic pricing models are heavily applied in logistics and shipping. In digital freight matching, it is used to optimize load prices for both shippers and carriers.

By using machine learning, the platform, together with load recommendation, can suggest prices to the trucking company. Instead of the lengthy bidding process they may or may not win, carriers can instantly accept the recommended freight rate and get an order. Similarly, from the shippers’ perspective, they can immediately get accurate pricing and sell the load whenever they ask for shipping quotes. The key to success here is to ensure that shippers always get the best possible price for their shipment and that carriers are fairly compensated for their services.

Real-Time Visibility, Predictive, and Proactive - Features of Modern Transportation Management System

Digital freight matching platforms that use machine learning can provide load matching automation, real-time visibility into the status of shipments and optimization opportunities, predictive analytics to forecast demand, and proactive recommendations to manage transportation.

These features are all part of a modern transportation management system or digital freight marketplace. Companies that will take this innovative approach and utilize the power of novel technology will be able to get more customers and more shippers on the platform to create the largest network. The more loads and drivers available in one logistics space the more opportunities for efficiently providing the best matches and a more sustainable logistic network.

If you’re looking to boost your digital freight platform by offering AI-based features for your customers and see the benefits of machine learning in action, contact us today. Our team will be happy to help you find the perfect solution for your business.