Beyond freight management Building continually improving networks
Customers want their managed transportation partners to deliver cost-effective reliable freight operations. But performance alone is no longer enough. They also need partners that can understand their logistics networks, identify opportunities for improvement and make the changes needed to get better results. And the knowledge and size of these partners should mean real benefits to every customer.
This is what we call Transportation Orchestration at Uber Freight. This is the next stage of managed transportation, which consists of ongoing network monitoring, identification of performance deficiencies, and incremental automation of decision-making and operational delivery. The company’s scale allows for improvements that individual businesses would find difficult to achieve on their own.
AI is expanding these possibilities. Even experienced logistics experts can only analyze so much information when faced with large volumes of data, but AI is able to evaluate many more data points from the network in real time, uncover alternative solutions and increasingly automate the actions needed to implement them.
As routine activities become increasingly automated, logistics specialists can focus more on customer advisory services and strategic network enhancements. The benefits from these initiatives can then be fed back into the system, improving recommendations in the future and helping identify similar opportunities across other customer networks.
“The focus is on building this continuous feedback loop so Uber’s tech and expertise improves with every client interaction,” says Uber Freight’s chief product officer.
Not just moving freight, but anticipating customer needs
Early conversations with customers at Uber Freight have made clear to the Chief Product Officer that the company must move beyond the day-to-day management of transportation. Reliable freight operations are still the name of the game, but customers get more value when their partners can foresee challenges ahead and help make the changes required to meet them.
Seeing each other is important in this relationship. “Logistics partners should be able to use operational information to predict disruptions, protect service levels and eliminate recurring problems at the source.
Changes in demand, freight rates or facility operations can impact transportation performance and budgets. In such circumstances customers require more than information, they require an assessment of the possible consequences, advice on the options available and support in implementing the most appropriate response.
Uber Freight, which supports shippers in a variety of industries, can take advantage of a wider view of operations. This experience helps customers understand what improvements are possible, where the greatest opportunities lie and which initiatives should be prioritized.
Data-driven smarter transportation decisions
Uber Freight describes three linked phases in the path to greater autonomous network planning and freight execution: the automation of routine activities, turning data into actionable intelligence, and the gradual automation of execution.
The first stage is meant to speed up and make more consistent planning, tracking and information gathering while reducing errors. The second is the use of intelligence to detect patterns, diagnose operational problems and recommend better solutions. The third connects these recommendations to execution tools, enabling actions to be taken with ongoing involvement of logistics experts in decisions that require a great deal of judgment.
The process isn’t over when an action is taken. Its results are logged and measured, and fed back into the system to broaden its transportation knowledge and enhance future decisions.
Repeated late deliveries are a demonstration of how this approach could work in practice. If a route is consistently taking longer than planned, the system can identify the pattern, modify the transport plan within agreed limits, and evaluate if the change leads to a better service.
Instead of someone having to manually go through reports, analyze the underlying data and decide what to do, the system can increasingly do this sequence automatically.
Once the adjustment is in place, the knowledge gained can also be used to discover similar issues in other customer networks. So an improvement made in one operation can be the foundation for better results in another.
Optimization extended to individual shipper networks
There are only so many opportunities for improvement in a company’s operations. Some require a more holistic view of the distribution of freight and transportation capacity across multiple shippers.
Historically, much of the logistics industry has focused on optimization of individual networks. A private fleet can be carefully tailored to the needs of a single shipper, while two different shippers may each have a satisfactory transportation plan involving underutilized trucks. In some cases they may coordinate shipments to use available capacity more effectively.
These chances are often missed, because optimization is performed separately in each of the networks. Improvements are often incremental and carriers are often left to connect the dots across operations.
Across multiple shipper networks, there is the potential to better coordinate freight and capacity in a holistic way that can reduce transportation costs, in a way that individual companies may not be able to do on their own. To achieve these benefits requires a deep understanding of each shipper’s operating constraints, and then a concerted effort at execution.
Uber Freight aims to combine the network insight and capability required to orchestrate this process. Uber’s management of customers’ logistics operations brings operational expertise; its marketplace connects shippers with available transportation capacity; and Uber provides the technology foundation on which these capabilities are being built.
So the product strategy is about making it easier to identify, assess and execute cross-network opportunities consistently. The aim is to deliver the advantages of Uber Freight’s scale to additional customers.
AI-powered logistics experts
More automation doesn’t mean there’s no need for experienced logistics professionals. Their knowledge of operational constraints and commercial trade-offs remains critical to whether a recommendation will work in real-world conditions.
As AI agents take over more repetitive execution, human expertise becomes more important for directing complex decisions and managing judgment-sensitive circumstances.
With access to more extensive data and analysis, logistics specialists can make stronger recommendations, advise customers on network strategy and oversee the implementation of meaningful operational changes.
This evolution must also revolutionize the relationship with the customer. Technology will increasingly manage the day-to-day execution, enabling experts to focus on understanding what each network needs next and to identify the strategic changes most likely to deliver measurable benefits.
Driving tangible progress in the autonomous transportation space
“Transportation Orchestration is where we’re headed long term for Uber Freight, but we need to show progress with real results, not just ambition.”
The company is working to improve the data that fuels its recommendations, strengthen the connection between insights and execution, and show that agentic execution can deliver tangible value and consistent results for customers’ operations.
These capabilities are evolving in an iterative fashion. Uber Freight will take what works from its operational results and scale successful improvements across the freight it manages.
Building this model will take time but each step is expected to have value along the way.
Ultimately, customers will measure progress in terms of those results that matter most to their businesses – lower transportation costs, reliable service and the ability to be more responsive as their needs change.
Uber Freight seeks to develop transportation networks that keep improving through systems that can run more and more autonomously and experts who keep applying high-judgment decisions and strategic change.
The goal is clear. Each improvement must make for a smarter, more efficient and more flexible transportation network.
