How Can AI Transform the Freight and Logistics Industry?

After three solid years of constant disruptions to regular trade and supply lines, the global supply chain is undoubtedly in a better state than it was before. But, lingering complexities, as well as the emergence of new, unpredictable challenges mean the global supply chains are still under strain from the push-pull of market-related conditions and customer expectations.?

On top of this, many supply chain organizations, like freight forwarders and carriers have long been operating on paper-based analog systems to coordinate their shipments, and still do so despite having embraced some digitalization as a means of survival.?

Despite this welcome change, the pace is still slow and many supply chain players face consistent challenges impeding their ability to deliver accurate, on-time shipments. A lack of visibility into shipment status, location, routes and rates, and more competitive shipping strategies all put pressure on freight and supply chain companies which, in turn, all put pressure on the global supply chain.?

Digitalization of processes and connecting disparate data is the first step to solving this complex interplay of problems, but alone it’s still not enough. The global trade industry, which includes the freight and logistics sector, changes too rapidly and there’s a lot of unpredictability. For organizations to succeed, they need the heavyweight capabilities of AI and machine learning to drive their strategies and decision-making.?

The current challenges of the global supply chain

The past half-decade of global economic and geopolitical events have pushed toward reducing obstacles to the free flow of goods and shipments across the world. Now, the overreliance on free-flowing trade with minimal management and guardrails in place has left its vulnerabilities exposed in the wake of the pandemic. The events of the last three years have shifted the course and structure of global trade conditions. Now, we’re seeing more of a rise in barriers to trade entry, tariff increases, sanctions, geopolitical instability, and a decreasing reliance on offshore supply chains.?

These seismic shifts in operational ability mean that supply chain organizations need to be more resilient and responsive than ever before. Unfortunately, this is both the solution and the problem. Freight and logistics organizations, carriers, NVOCCs, and even Customs Brokers are struggling to maintain a sense of control over their operations due to vast amounts of disparate, unstructured data sitting in silos. At every point in the supply chain, an inefficiency like this has a knock-on effect down the line.?

A lack of data centralization and structure means that supply chain organizations lack the visibility they need to identify problems as they emerge and flag potential issues for risk mitigation. They’re also unable to deal with the vast onset of incoming data from both up and down the supply chain, meaning they’re unable to respond swiftly to shippers and other stakeholders.?

How can AI transform and enhance the freight industry?

While many freight and supply chain organizations have embraced more digital solutions as a survival mechanism, digitalizing analog processes is still not enough to provide the quick wins and efficiency gains they need. The only way they can master and gain control of every moving part of their processes is to leverage AI and automation to utilize their data, so it becomes an asset instead of a liability.?

Real-life use cases for AI in freight and logistics

Across the supply chain, the use cases for AI, automation, and machine learning are endless. Anywhere there’s a data disparity or lack of visibility, AI can help thanks to the speed and accuracy it can process vast datasets. Generative AI, in particular, can produce even more refined, granular insights and recommendations for optimizing workflows and operations.

Some on-the-ground use cases for AI include optimized fleet management, supply chain management visibility, demand forecasting, and intelligent route planning and optimization. AI and machine learning capabilities can be used to help supply chain organizations plan more efficient routes, reduce idle waiting time, and minimize fuel consumption. It can be leveraged to consistently monitor the real-time status and location of shipments in transit for risk management and better delivery estimations.

AI can help organizations anticipate demand for inventory across the supply chain network by using predictive analytics to assess historical data, market trends, and other factors. It can also provide quick insights into where organizations are making unnecessary losses and how they can improve their operational efficiencies.?

Conclusion?

It’s important to remember that while AI does present numerous opportunities and benefits for the freight industry, it’s still a tool - one that needs to be monitored and managed by people. AI should not be replacing human teams across the supply chain, it should be equipping them with the capabilities they need to excel in their roles and deliver more value to organizations which, in turn, helps to enhance and improve the entire supply chain.?