How does the supply and demand side meet on the platform!
Transition in platforms step #2
The competition amongst third party independent digital platforms in European freight transport is on the rise.
In road transport mode, freight exchange platforms (marketplaces) such as Timocom (since 1997), raaltrans.ru (1992), further teleroute (since 1985), Wtransnet (1996), 123cargo.eu (2000) now owned by Alpega (2017) and trans.eu (2004) have existed since two decades.
The digital freight forwarding platforms such as sennder (2015), Instafreight (2016), Cargonexx (2016) have emerged more recently in the past six to seven years.
In inland water ways the bargelink (2001) is gaining traction and Freightlink (2004) is a popular for ferry transport and tunnel ticketing.
The multimodal platforms such as the Freightos (2012) is a popular digital booking platform for international shipping, and digital freight forwarders Flexport (2013), Forto (2016), Zencargo (2017) are active in supply chains.
The rail freight has finally joined the competition with platforms such as rail-flow (2020) in intermodal transport, modility (2020) in combined transport.
These trends are interesting since the freight market is heavily fragmented, and the search for the right transport customers and the right transport supplier is a daunting task for each side. Digital freight matching, which is the core function of these platforms, can make life easier for the supply and demand sides (the so called two sides of the platform) in freight transport by bringing them together in their most preferred way. The platforms may cater to long term and/or short term needs, but this really depends on the focus of the platform.
In this article, I share my point of view on the matching practices of platforms. For a more detailed insight on the practices of platforms regarding matching, you may find the data collection and analysis of selected platforms in Jain et al. (2020) on this topic in my publication with co-authors (open access) helpful.
Digital freight matching flavors
This is a highly complex topic, and one simple way of looking at it is to classify the matching in two flavours,
the manual matching and,
the automated matching (partial and fully automated).
In my nearly decade long research on freight transport platforms, I have observed that these have increasingly gained trust of the demand and supply side in sharing their transport related data. The matching thus supplemented by better visibility of demand and offers, has moved beyond manually connecting the two sides to partially automating and in some case fully automated matching.
The data sharing enhances visibility in the sales process, which, after finalization of the matching transactions, supports real time visibility (using IOT sensors. other telematics solutions) in the transport operations. In turn this improves the data quality of future matching requests. That is, hinderances can be avoided and efficiency can be improved in processes when both supply and demand are notified about the events (e.g. delays and expected time of arrival, weather related information) while the actual transportation is taking place.
As a result, this matching enables sustainable use of resources. For example, by reducing occurrence of empty returns in dynamic market conditions and delivering economic (e.g. reducing search time, access to new markets), social (e.g., less congestion) and environmental benefits (e.g., reducing CO2 emissions). And in this way support maintaining the use of resources for a longer term.
In my view, this function of matching is the heart of every freight transport platform, irrespective of its business model and the value proposition it offers. The freight transport exchanges do not take responsibility for the actual transport after the matching process is completed, but offer several support services in this regard (mentioned later in the post). The digital freight forwarders take responsibility for the transport finalized in the matching, thus the aforementioned support services form a part of their offer. A user’s (potential participant of a platform) choice of platform is basically dependent on its need of the relevant services.
Manual matching: The della.eu (1995) is a great platform that aims at providing relevant information for truck transport within Europe. Its a loadboard where the transport capacities and freight demands can be viewed anytime. The matching is left to the participant looking for a freight/transport capacity. In particular it offers a quick overview of prices for medium duty truck transport (between different destinations) in Europe based on data shared on the platform.
Automated matching: The freight exchanges, bring the two sides together, without participating themselves in the interaction. Usually, the shipper in this case, as a demand side requests for a quote (e.g. for a long term transport demand), and then chooses offers manually from the supply side (usually a freight forwarder or a carrier). The supply side can also search for a supplier to offer its transport services. In some cases the negotiation takes place outside the platform i.e. the price detail may not be available to them. The digital freight forwarders, in line with their value proposition to ensure execution of the transport, organize the matching that results in a price for the service that is decided on the platform.
Providing price signals in addition to quality of service it delivers, can offer a platform a competetive edge over other platforms in the market. The global container freight index from Freigtos (Fbx, the freightos baltic index) offers such indices for ocean freight related to the 40 feet equivalent unit (FEU).
It implies that for automated matching, a platform needs to overcome the level of digitalization in the participating firm as automating requires consistent data and standardized data formats, further the trust in the platform’s ability to find a safe counterpart in the matching. Several firms on the demand side therefore prefer a control on the matching by making the choice of suppliers themselves. There has however been a positive and significant change in this regard, but more needs to change to reach a full automation. In particular, such matching becomes complex in a multimodal environment (more on this in subsequent posts).
The challenge for participants arises when there is higher liquidity on both sides of the platform. Achieving an appropriate match from a large set of data manually becomes time consuming for the platform’s participants. This is when acceptance for technological innovation sets in. Hence, platforms, leverage data and apply machine learning for analysing participant's behaviour and historic information to improve the customer experience. In this way the search can be narrowed down e.g. to the most preferable set of offers and automatically suggesting the supply side results from previously accepted, or offers from a particular lanes. In other cases, the continued service of a supply side for a demand side can be implemented as automatic matching.
The potential for a fully automated matching on the platform exists, however, the market conditions will decide when this can take place. Till then we may see a rise in partial automation with certain cases being fully automated.
Having briefly discussed the importance of matching, both, the demand and supply side need more than the matching for their businesses. The platforms with sufficient liquidity i.e. organized balance on supply and demand side are capable of solving challenges faced by their customers outside the platform. For example, the freight exchanges, through value added support services offer e.g. real-time visibility, warehousing, payment, debt collection, tracking, and documentation. The digital freight forwarders on the other hand, for transport operations, may organize these with their service partners, and offer the aforementioned value added services as a part of their service offer.
I hope this article was helpful for you. Simply contact me if you wish to read any other topic on platforms (also read other articles here) or need professional support in this regard.
*These are author's personal views. The data collection is based on the public sources available on the internet. The author takes no guarantees for this information.