We have been working hard since early 2019 to prepare for the role of central data clearinghouse for the DFM industry. We started out with the basic premise that the service we intended to offer is sorely needed – and that software is only part of the solution. We recognized that because we are asking competitors to work together, we would also need to develop the governance structure required, along with an online platform to accommodate cooperation and collaboration. The initial work is done. We are ready to serve DFMs. We are ready to onboard the early adopters who understand the imperative and are ready to lead the industry.
When phantom data is gone from a DFM’s data set, they can perform frictionless matches nearly 100% of the time. This operational improvement will virtually eliminate, confusion, delays and cancellations caused by matches with phantom loads and capacity—saving countless staff hours and creating happier, more loyal customers.
The tipping point is near. We anticipate that the majority of companies providing freight matching services will be data sharing through our platform by the end of 2021. Together, through industry-wide interconnectedness, DFMs will inspire significantly more repeat business on their own platforms and will ignite an unprecedented increase and the rate of adoption of dynamic freight services by shippers, carriers and brokers nation-wide.
Our message to all dynamic freight matching service providers is that “NOW IS THE TIME”. Our platform brings the only realistic way to clear the path to 100% frictionless matches unencumbered by phantom data. However, it will require the participation of virtually all DFMs to fulfill the potential. While we are well on the way to bringing our dynamic freight data-sharing ecosystem into wide use, there is urgency to onboard DFMs as soon as possible, because the problem of phantom data only gets worse with time.
The growing adoption of dynamic freight matching by shippers, carriers and brokers inevitably grows the density and impact of duplicated market capacity data in every DFM dataset. This means more and more friction added to the customer experience, and more and more distance from the holy grail of fully-automated matches, fully-optimized marketplaces and maximized profitability for DFMs.
The post-phantom data era brings more payoffs. The interconnectivity that our platform facilitates between DFMs will lead the way to the potential for other services and innovations that will further enhance the profitability and growth of the DFM industry. The initial data-sharing required for the automated purging of phantom data requires only 6 freight process steps. There are nearly 90+ more process steps that when shared will ignite a transition from Interconnectedness to true DFM-to-DFM interoperability
For instance, we envision collaborating with DFMs to facilitate the development of co-brokering between DFMs. We can add functionality to the platform that enables the option for DFMs to make their idle capacity visible to other DFMs. Unlike double-brokering, with the aid of governance and blockchain smart contracts, co-brokered loads will be safe, legal and transparent to all stakeholders—and will increase the speed with which loads can be matched as well as to provide shippers with the driver-tractor-trailer that truly is the closest available.
Ultimately, this implies that our platform will also provide a real-time, predictive window into the entire digital freight market. DFM industry data-sharing via our platform creates a unique, multi-faceted transactional data profile of the entire market, which will prove to be invaluable to DFMs and their customers.
The aggregation of anonymized data from DFM’s enables a dataset completely new to the world, This new dataset yields the ability to provide industry performance metrics and insights. Participating DFM’s can use these data and analytics to chart their company’s progress, plan for growth, and to innovate their future. The freight industry overall will finally have a reliable spot market index driven by real time data from DFM transactions. We will also be able to calculate carbon offsets created by digital matching which results in significantly fewer empty miles driven. Additionally, the data will be explored by data scientists ongoing for use by DFMs to run smarter and leaner.
In freight movement, in order to quantify carbon emissions improvements you need to identify a decision, or a choice, that is connected to a given impact on your green/emissions footprint–and you be able to document each occurrence, by the specific truck and load. Major US companies have publicly committed to aggressive net zero carbon emissions goals, yet while trucks have become significantly more efficient in recent years, the freight transportation industry cannot provide sufficient documentation of emissions improvements via reductions in deadhead miles and other per load route optimizations. Soon, DFM interoperability will be able to help.
The most fundamental description of what a dynamic freight matching algorithm is designed to do is to match a load to the nearest truck that fits the requirements. The better you are at finding the closest truck, the fewer miles an empty truck needs to travel to pick up the load. Some estimate that, on average, 35% of tractor/trailer miles are driven with an empty trailer (aka “deadhead” miles). The carbon emissions from the trip to pick up a load must be included in the calculation of the net emissions to deliver the load. Some DFMs report the dynamic freight matching consistently reduces deadhead mile by 25%-30%. That reduction