AI is having a tangible, physical impact on transportation by making fully autonomous driving of trucks and cars closer to everyday reality. But given legal concerns and constraints, this process will continue to be gradual, developing and expanding driver assistance systems.
In logistics services and supply chain planning, where AI has already made inroads, more advanced applications could likely provide the greatest value in the near term. The world is increasingly complex and uncertain for international shippers and their logistics partners. This makes the game of matching supply and demand (preferences) more challenging. Over the past two years, the reliability of international shipping has weakened due to shocks related to the pandemic, wars and geopolitical tensions. More extreme weather conditions also lead to ongoing uncertainty. All of this leads to fluctuations in supply and demand, with seasonal variations, while sustainability-related policies are also having an increasing impact. As a result, shippers and their logistics partners are looking to build resilience by diversifying or securing more buffer stocks to reduce risk. But smarter and more predictive analytics could also be part of the answer.
There is still much scope to improve decision-making by including more variables and (large) amounts of data, such as congestion and waiting times, weather conditions and environmental footprint. This could allow players to improve transport forecasts, find better alternatives for customers, enrich reports and save on equipment and personnel rental. Logistics is a cost-driven, low-margin business. When it comes to performance improvement, the potential gains are mainly focused on cost reduction and this is also where AI can make a difference. Optimising route planning and load factors still offers potential gains. In European road transport, for example, progress in reducing empty runs has stagnated over the last two years, with 20% of truck kilometres still running empty.
This sounds promising and companies can integrate internal and external data. But it must also be understood that cooperation within the supply chain and data exchange are essential factors. Mutual interests must therefore be at the forefront. Road transport in particular remains largely the domain of small and medium-sized enterprises (SMEs). Sometimes they can leverage the services of their freight forwarders, but to exploit the potential of AI, both investment and expertise in ICT are needed, which requires scale.
