Real-time data insights transform logistics performance, reduce costs, and improve CO2 emissions management. As Descartes Vice President of Global Sales Elmer Spruijt (pictured below) explains, adding artificial intelligence (AI) to transportation management systems (TMS) further enhances the depth and breadth of data, expands automation, and enhances predictive insights.
Real-time visibility
The increasing availability of real-time data across global supply chains is transforming operational performance. Real-time shipment tracking allows businesses to automatically update end customers with accurate delivery schedules. Instant access to a global logistics network allows you to see carrier options in real-time, facilitating next-level decision-making based on cost, timing, performance, and even CO2 emissions. When used in conjunction with a TMS, real-time data can drive automation such as carrier verification, associated transportation document creation, and customer updates.
Of course, data gaps are inevitable in a complex, multi-layered global logistics network, and adding AI to this process can provide significant benefits. For example, if a truck’s telematics device goes offline for any reason, backup information is provided via the mobile app. However, if the driver has not downloaded the app, the vehicle cannot be tracked. Using an AI agent to automatically connect to drivers and request installation is a simple and cost-effective process to quickly resolve data gaps.
Streamline and automate
A similar approach can be used if a carrier’s data feed is compromised. AI agents can seamlessly resolve issues by automatically contacting carriers to request network resets, as well as including specific missing information such as proof of delivery that was lost during the outage.
It was previously possible to perform these processes manually, but in an industry enduring tight profit margins and significant disruption, this was rarely a cost-effective option. However, as customer billing is increasingly automated and facilitated by proof of delivery, missing tracking data can lead to payment delays. The closer your organization gets to 100% shipment tracking throughout the journey, the more automation you achieve, further increasing efficiency and reducing the need for manual exception management.
Expanding business value
Additionally, AI can also help improve the depth of scope 3 emissions reporting based on existing insights provided by TMS about the CO2 produced by each vehicle based on various factors such as size, weight, distance, and speed. When vehicle-specific information is not available and TMS defaults to generic estimates, adding AI to this process allows the system to explore more data sources, improving the accuracy of the emissions assessment process.
AI will also play a key role in combating the growing risk of cargo fraud. Measures such as enhanced carrier vetting and onboarding are key to improving carrier verification, monitoring insurance, and preventing fraud.
AI adoption across the transportation management industry is in its early stages. But the large-scale investments underway to build the value of real-time data resources to improve efficiency, add automation, and power predictive analytics are attractive. For organizations that still rely on outdated systems and intermittent data updates, the gap between agility and resilience has become a concern. As AI adds even more valuable intelligence to efficient transportation management, the introduction of innovative technologies is progressively transforming competitive advantage.
