Innovative Strategies for Digital Freight Matching: How to Find the Perfect Fit with Automation

Freight matching is a critical process that links shippers needing to transport goods with carriers who have the capacity to move them. To facilitate this connection, many shippers and freight forwarders or LSP utilise digital load boards, where they can post their shipping needs and receive inquiries from interested carriers.

For carriers, the challenge is equally complex. They juggle a mix of committed loads, spot loads, and intricate route planning, all while trying to optimise their capacity and operations. The pursuit of the perfect load to complete their routes is a constant endeavour.

This issue isn’t new. For decades, both shippers and carriers have relied on load boards to bridge the gap between supply and demand. In Europe alone, over ten providers offer load board services, each covering diverse geographic areas. However, despite their widespread use, these tools fall short of providing a seamless, automated solution for freight matching.

In this article, we will explore why load boards often fall short and explore additional strategies that can help carriers and freight forwarders create an efficient freight matching system. Our goal is to uncover methods that enable automatic management of 80-90% of all loads, streamlining operations and optimising efficiency.

In (desperate) need of carriers? A definition of perfect freight match for LSP

The Logistics Service Providers (LSP) maintain their own fleets, but frequently rely on external fleets to manage peak periods and fluctuating demand. When additional support is required to meet shipment demands, fleet teams dedicate 70-80% of their workload to the time-consuming task of sourcing carriers and securing capacity for spot loads. This labour-intensive process involves countless emails, phone calls, requests, WhatsApp messages, and as stated before, the more pressing side of freight matching.

To alleviate this burden, it's crucial to understand the priorities of both shippers and external carriers. What do shippers prioritise in freight matching? How can logistics operators identify and partner with the best carriers to build efficient external fleets? Answering these questions is essential in developing automation strategies that have the power to revolutionise the freight matching process.

What is important for shippers and clients when valuing the transport service?

As a transport operator, when developing technical solutions at Ontruck, we meticulously analyse shippers' needs to pinpoint key factors and fine-tune our algorithms accordingly. Each factor is carefully weighted based on geographical considerations and specific strategies. The primary elements integrated into both our Cost and Routing engines include:

• Economics. Evaluating the cost and resulting margin proposed by carriers post-negotiation.
• Risk of delay. Prioritising time-sensitive orders to minimise the potential for delays by ensuring these orders commence carriers' daily routes.
• Vehicle specifications and compliance. Ensuring carriers meet loading/unloading location requirements and merchandise specifications while adhering to legal standards.
• CO₂ impact. Emphasising proximity to pickup points and minimising idle time or empty kilometres, particularly impactful in urban areas for CO₂ reduction in regional transport.
• Carrier strategy and segment. Understanding carrier strategies, such as load pickup percentages, to align the time carriers are willing to dedicate to or need to fill with jobs.
• Carrier engagement. Fostering loyalty by offering consistent job opportunities, thereby enhancing familiarity with the marketplace and ensuring strategic goals are met.

What is important for the external carrier when choosing partners?

For external carriers, selecting the right partners is crucial for achieving operational efficiency and growth. Additionally, partnerships should be mutually beneficial, offering optimised routes, reduced empty kilometres, and competitive pricing. 

By prioritising these criteria, external carriers can establish partnerships that not only support their business goals but also streamline their operations, creating a win-win scenario: 

• Economics. The total price and economic conditions of the load, including payment terms.
• Pick up location and time. Carriers seek to minimise their idle time and empty kilometres.
• Knowledge of a specific route or pick up location and customer. Familiarity can streamline operations.
• Good fit with another route that the carrier already has. Opportunities for backhauls are highly valued.
• Ease of use with the matching platform. Considerations include the need to call or email to place a bid or show interest, the speed of decision-making, the certainty that the load is real, and the medium (registration requirements, mobile support, and mobile app availability).

The ideal matching platform would allow for external carriers to communicate their preference beforehand and would take them into account when proposing and guiding negotiations, making this process as user-friendly as possible.

The Golden Solution in Freight Matching Strategies: the Automated Prioritisation Model 

Drawing on the needs and demands of both shipper and carrier, at Ontruck we are now in a position to look at strategies that will create a smart, autonomous match. But what are the strategic considerations in building an automated freight procurement system that solves the pitfalls of digital load boards? Let's have a look.

1. Collecting carrier's preferences

Before implementing optimisation rules, we need to get to know our potential assigned carriers, an informed working relationship is a successful one. For external carriers, the first step involves inviting them to define their preferences such as availability, special interest in specific lanes, preferred times, or even specific clients. With a more targeted approach, we can ensure carriers relate how and when they prefer to be contacted, so communication is streamlined, easy and timely.

This data is then processed by our AI algorithms to identify the highest probability of interest for each carrier over time, enhancing their user experience and massively increasing the likelihood of a match that makes sense. Our AI algorithms don’t just operate on these data fields, they apply concise optimisation rules that consider essential factors such as the following:

• Urgency. If integrated with a routing tool, the system can determine the proximity of the carrier based on GPS information.
• Overlapping. If they have already assigned jobs, AI algorithms will calculate if the new job fits in their schedule.
• Operational Area. Where the vehicles typically operate.

These optimisation rules lead to improved offer acceptance by external carriers and reduced supply costs, as the most suitable carrier is selected automatically, making communication fog a thing of the past.

2. Applying carrier quality rules as an engagement driver

To achieve optimal freight matching while ensuring the engagement of the best carriers and service level, quality rules are taken into account. In this regard, how do we ensure the deployment of top-tier carriers? Quality rules play a crucial role and central to this effort is carrier quality scoring, which serves as a parameter in the prioritisation rating system. To harbour an environment of fairness and quality, when all other factors are equal, matching algorithms prioritise sending offers to carriers with higher quality scores.

Likewise, the preferences of each carrier segment being considered, as well as receiving support in achieving their financial and operational objectives. A culture of transparency in decision-making processes and ratings is fundamental in this regard. Although most platforms or load boards incorporate a rating system and evaluation process, which should be transparent to carriers to manage expectations and empower them to influence their ratings, this is frequently not the case.

Quality scoring rules can be tailored based on key performance indicators critical to operational success, such as seniority, on-time performance, acceptance rates, or proof of delivery upload rates. These highly valued parameters ensure that carriers are not only matched efficiently but also incentivised to maintain high service standards, fostering long-term partnerships based on mutual benefit and trust.

3. Implementing automatic bidding to enable agile freight matching with external fleets

Implementing an automatic bidding system has an immeasurable streamlining effect on processes with external carriers, characterised by two key factors:

1. Anticipated and optimised cost management

Nowadays, the majority of loads are posted without a specified price, as freight forwarders seek the best available option and often need to negotiate. However, for carriers, especially those dealing with non-digitalised processes, negotiating each load can be an absolute drain on time potentially spent on delivering or collecting loads. In this regard, for example, Ontruck’s AI-powered freight matching algorithms can identify the most straightforward routes and suitable carriers, proposing predetermined prices to increase the likelihood of immediate matches. Thus, saving a lot of time for both parties.

2. Automated freight bidding

For LSPs that frequently outsource fleets, freight bidding poses a significant challenge in transportation and logistics management. To meet the challenge head on, a successful strategy must consider: enabling negotiation options, and automating negotiation processes.

By integrating the above strategies, autonomous freight bidding becomes a feasible solution. And finally, to further add to the streamlining process, throwing digital bids into the feedback loop enhances the efficiency of the negotiation process.

This approach not only accelerates the matching of loads with carriers but also promotes agility and responsiveness in the logistics chain, ultimately optimising operational efficiency and cost-effectiveness.

Streamlining freight matching: overcoming the limitations of load boards

We have outlined the advantages of freight matching automation. Load boards, designed to help find the right carrier and secure desired loads, essentially serve as publication tools for freight forwarders or shippers, leaving carriers to do the heavy lifting of searching, contacting, and deciding. This often leads to restricted success, especially among small to medium-sized carriers and owner-operators.

Indeed, load boards come with significant limitations, and carriers have constrained time availability due to their operational demands. Instead of having the perfect job recommended based on their preferences, they are burdened with searching, filtering, selecting, and negotiating. This process is extremely time-consuming for both LSPs and external carriers.

In this regard, implementing digital freight matching addresses these challenges by improving both financial and operational aspects. Teams can focus on more meaningful tasks than sending emails and making phone calls to find capacity, while prioritisation algorithms analyse carrier data to determine the best assignment options. When combined with data from routing tools, assignment rates increase dramatically, boosting team productivity. For instance, at Ontruck Transport Services, team productivity has more than doubled due to the integration of digital freight matching and AI routing.

By adopting these modern solutions, the logistics industry can overcome the frustrating limitations of traditional load boards and make freight matching more efficient, reliable, and above all, cost-effective.

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References:

Polaris Market Research. (2024, January 1). Digital Freight Matching Market Share & Growth Report, 2032. Polaris. https://www.polarismarketresearch.com/industry-analysis/digital-freight-matching-market 

Perry, G. (2024, May 17). Digital Freight Matching Platforms: Leveraging AI for load Optimization | Business Tech Innovations. Business Tech Innovations. https://businesstechinnovations.com/logistics/digital-freight-matching-platforms-leveraging-ai-for-load-optimization/ 

Grand View Research (2023, January 1). Digital Freight Matching Market Size, Share & Trends Analysis Report By Service, By Platform, By Transportation Mode, By Industry, By Region, And Segment Forecasts, 2023 - 2030.

https://www.grandviewresearch.com/industry-analysis/digital-freight-matching-market-report