AI in the freight exchange model – groundbreaking opportunities for road freight transport and competition?

As explained in the first part of this blog post, the freight exchange market is characterised by fragmentation and an imbalance between supply and demand – the keyword here is VUCA. Numerous platforms, both niche providers and established companies, are competing with each other. In addition, new competitors are emerging that are integrating freight exchange functions into their business model. This flood of platforms is leading to a lack of transparency and uncertainty for users. Industry-specific requirements and legal regulations such as driving and rest times are exacerbating the problem of empty runs. Companies often rely on proven partnerships rather than digital freight exchanges, as trust and reputation are crucial. Meanwhile, shippers and freight forwarders are operating on different platforms, making the image of an efficient and synergetic transport industry increasingly opaque. To reduce the number of empty runs, cross-platform information transparency must be ensured. Only through cooperation and collaboration can new players such as digital freight forwarders, 4PL providers or comparison portals challenge the established freight exchanges.

Orientation towards open-source principles instead of winner-takes-it-all

With the aspect of platformisation, the freight exchanges and thus also their users differentiate themselves from each other, so that the entire shipping and transport market operates on different platforms, which absolutely contradicts the ideal of a platform economy for logistics. If the entire mass of the shipping and transport market is to be networked and the visions of today's logistics are to be realised across multiple platforms, the boundaries of the freight exchange platforms must be opened up, similar to an open-source solution. Open-source technology offers the possibility of connecting different platforms and thus strengthening the entire ecosystem. The open provision of software can create a federal platform economy in which companies work together on solutions and facilitate their collaboration across platform boundaries.

The real challenge is to link existing freight exchanges instead of creating completely new structures. This would promote the exchange of information and enable closer collaboration. Such cross-platform cooperation could increase market transparency and reduce empty runs. Instead of several exchanges competing against each other according to the monopolistic ‘winner takes it all’ principle, synergies are achieved together and loads and transport services are consolidated, making the ‘big picture’ more manageable. Shippers and freight forwarders would have access to a broader range of services, including those of smaller niche exchanges, which in turn would counteract the problems of industry specialisation and platform saturation.

Artificial intelligence as a driving force for a consolidating authority of freight exchanges

To reduce the number of empty runs and increase the efficiency of the transport market, it is crucial that a carrier's available loading capacity is made accessible to all relevant freight forwarders and shippers – and vice versa. The more comprehensively the market is addressed, the greater the chance of finding ideal partners. Today, however, these possibilities are severely limited by the closed systems of freight exchanges.

The solution is a consolidating, overarching authority that networks the existing platforms without abandoning their business models. This instance enables a central, cross-platform search for transport offers and acts as a neutral intermediary. It has access to real-time data from tenders, catalogues and auctions – from niche exchanges to established market leaders – and makes all offers transparent and comparable for shippers and freight forwarders.

The role of AI

Artificial intelligence is at the heart of this overarching instance. It ensures that the fragmented and dynamic structure of the transport market is preserved by enabling precise and individual filtering. Shippers can use the AI instance not only to search for transport capacity that meets their requirements in terms of time, price and route, but also to define advanced criteria such as specific transport conditions, contract terms or platform features.

The AI not only takes into account the offers from the freight exchanges, but also analyses platform properties and company data. This way, the following parameters, among others, can be considered

• Dynamic pricing models (e.g. auctions or fixed prices)
• Award procedure (e.g. spot or contract market)
• Industry filters (e.g. food or forestry)
• Contract and payment terms (e.g. e-payment or contract durations)
• Sustainability aspects of the companies
• Reviews and trust marks of the providers
• Tracking (e.g. real-time tracking)
• Security standards of the exchanges and providers
• Cost models of the freight exchange (e.g. subscription or transaction-based fees)
• Service level agreements (e.g. customer support or response times)
• Available transport equipment categories (e.g. refrigerated trucks or low-loaders)
• Load specifications (e.g. weight, volume or hazardous goods)

This AI-supported matching enables shippers and transporters to ensure that all relevant offers are considered without having to consider platform boundaries. Even if there is no exact match, the instance suggests alternatives that come closest to the defined parameters. The following figure abstracts the groundbreaking AI instance for a synergetic freight exchange:

The advantages of a central instance

This type of solution creates transparency, promotes interaction between market participants and reduces a lack of transparency and mistrust. It also motivates smaller transport companies to switch to digital marketplaces, as filters such as ratings and safety standards strengthen trust. It makes it easier to establish an international and industry-independent partner network and shifts the focus from maintaining proven contacts to networking via nationwide platforms.

Access to all relevant offers at individual conditions significantly increases efficiency in road freight transport and significantly reduces the number of empty runs, as all potential capacity reserves of all parties involved in the transport are transparently offered on the market. Optimising logistics processes can relieve road congestion and make a positive contribution to sustainable transport development.

Innovation requires a strong impetus for transformation from all directions, be it for smart cities or AI-supported freight exchanges. The time factor plays a crucial role here, as infrastructural challenges, scepticism on the part of stakeholders and economic uncertainties often hinder progress.

Challenges and radical influences on competition

Data protection and security

Data protection and security are of crucial importance, as the disclosure of sensitive data such as transport capacities and freight demand could potentially be misused. To ensure user trust, strict data protection measures, monitoring and traceability mechanisms must be introduced.

Scepticism and trust

Another obstacle is the scepticism and mistrust of dispatchers. Many doubt the reliability of AI and its partner recommendations. Transparent processes and error-free implementations are crucial to building trust and promoting acceptance.

Change in market structures

An important factor is the change in market structures. Automation through AI could have a significant impact on traditional ways of working and business models, such as the role of freight exchanges. Dispatchers could be relieved by automated processes, while the competitiveness of individual freight exchanges could decrease due to cross-platform networking.

Technical hurdles

Added to this are technical hurdles such as standardising and integrating heterogeneous data sources. The quality, timeliness and dynamics of the data are crucial to the efficiency of the AI solution, but existing technical barriers and the different interests of the stakeholders make smooth implementation difficult.

Cooperation

The biggest and final challenge lies in the question of cooperation. In order to motivate the freight exchanges to work together, the economic benefits must be clearly demonstrated. In particular, the advantages and the pressure from the transport industry to work together in a synergetic way must be made clear to the market-dominating exchanges. A well-thought-out concept and visualisation of factors such as maintaining a competitive market, developing new business models and accessing an expanded customer base are the key to this. An accompanying model should inform both the users of the freight exchanges and the exchanges themselves about the advantages, profitability and data usage in order to ensure a successful market launch of the AI software.