Generative AI and Transportation Management | Deloitte US has been saved
Transportation management has never been an easy business. Volatility in freight rates, driver availability, and rapid economic changes in consumer demand make service and cost optimization a continual challenge for supply chain executives. Macro shocks, such as the COVID-19 pandemic, have only further complicated matters.1
But the recent Generative Artificial Intelligence (GenAI) productivity shock, though no panacea, could indeed be transformative in enabling supply chain executives to create further efficiencies across the transportation life cycle. This blog entry will concentrate on how Generative AI can influence transportation logistics in the short to medium term. A future entry will address how AI can affect supply chain performance in the longer term—such as real-time optimization and arrival prioritization.
Short- to medium-term impact on transportation management
Generative AI can retrieve, parse, analyze, organize, and synthesize a range of disparate datasets, text, documents, and other readable/scannable content.
How do Generative AI’s capabilities apply to transportation management? Below, we outline the four key areas across the transportation management life cycle, where we anticipate the technology will make the greatest impact in the short and medium term.
Figure 1: Key areas where Generative AI can enable more streamlined transportation management
Click image to enlarge
Preparing for a future of AI-powered logistics
To summarize the above, in the short to medium term, increasing adoption of Generative AI will streamline and optimize a host of tasks across the transportation life cycle, from reducing the time to onboard new carriers, to simplifying the freight audit process. At a broader level, Generative AI will be the driver of the core transportation operations needed to achieve on-time service and customer service-level targets. This will, in turn, free up operations resources to focus on exceptions to normal operations, continuous improvement projects, and innovation.
In the long run, as Generative AI adoption becomes more widespread across transportation, one should prepare for a world where carriers are fully autonomous and operating on their own Generative AI engines. Simply put, one will likely need to prepare for a world of multiagent AI systems that all need to communicate with one another.
In such a future, one must design and develop Generative AI models keeping in mind how they will need to interact with other engines. For example, transportation managers will need to ensure their Generative AI models can communicate with the models deployed by both their customers and carriers, enabling a cohesive and intelligent transportation ecosystem.
By addressing these considerations, transportation organizations can pave the way for a future where Generative AI models speak the same (or similar) language. Indeed, as technical architects know, interface design is often one of the most important aspects of software implementation. Such a mindset will be crucial as AI adoption becomes more mainstream.
Authors:
Christian Riemann | Steve Ostendorf | Brad Umphres |
Endnote:
1 US International Trade Commission, The impact of the COVID-19 pandemic on freight transportation services and U.S. merchandise imports: COVID-19 disruptions in maritime shipping and air freight, 2020.