How Can Freight Forwarders Better Manage Their Data?

Within the world of freight and logistics, freight forwarders play a key role in facilitating shipments across the supply chain. Not only do they liaise directly with shippers, they also coordinate shipments with carriers and NVOCCs, and are indispensable to safe, timely shipments that keep global trade running.

But, despite their invaluable role, many forwarders struggle to overcome both internal and external challenges that hinder their growth and revenue potential. Put simply, freight forwarders are struggling to remain relevant and competitive in a rapidly-evolving landscape.

Shippers have the luxury of choice when it comes to which forwarders they choose to partner with, so speed and accuracy have become key determining factors that help forwarders close deals. Unfortunately, so many forwarders can't leverage speed or accuracy due to gaping holes in their data management practices.

These inefficiencies don't just relate to how forwarders collect and store their unstructured data, as part of data centralization and visibility. It also relates to how the data is utilized strategically in their operations.

Freight forwarders are struggling to stay on top of sudden unexpected route, rate and market-related changes and, as a result, cannot respond swiftly enough to gain a competitive advantage. They have no deeper visibility and, as a result, lack the insights necessary to make speedy decisions. It's clear that forwarders (and other supply chain organizations) for that matter, need a complete overhaul of their data management practices - and urgently!

The larger problem of a lack of good data management

Poor data management impacts every aspect of a forwarder's operations. Unstructured data coming in from every direction, from shipping requests to buy prices from carriers, to shipment status data all complicate and hamper forwarders' ability to maintain control over their workflows.

As a result, they struggle to optimize and improve their processes and find it difficult to collaborate effectively with shippers as well as partners and other stakeholders. Unstructured data that's manually analyzed and processed stagger forwarder response time to both shippers and carriers, creating slow turnaround times and costing them business.

What's more, a lack of robust data analytics means forwarders are often left guessing what their ideal margins should be on both contract and spot rates for shipments, leaving room for costly errors and miscalculations. Limited or nonexistent forecasting and BI also make it difficult for forwarders to spot glaring inefficiencies and revenue drains in their operations.

All of this is costing forwarders in time, focus, and revenue and leaving their teams exhausted and overwhelmed.

How can freight and logistics organizations better manage their data?

The only solution for forwarders serious about bridging the gap between their existing output and desired output is to invest in digitalization. Automation, machine learning, forecasting and analytics and predictive analysis all work in conjunction to help forwarders better manage their data and leverage it more strategically for growth-driven decision-making.

Through machine learning, forwarders can process and structure vast amounts of incoming unstructured data in minutes, putting actionable data in their teams' hands. Forwarders can easily send structured RFQs on to carriers and also extract carrier buy prices, creating swift shipping proposals.

Advanced analytics uses historical data to advise forwarders on ideal rates and margins, boosting their revenue performance and bottom lines. Automation allows forwarders to run their proposal creation process on autopilot, ensuring proposals are competitive, accurate and free of data errors or omissions.

The key role of AI in effective data utilization

It goes without saying that AI plays a central role in deploying all of these digital capabilities. AI not only improves your data management as a forwarder, but it also helps you better utilize your data to improve business performance. AI is the foundation on which machine learning, deep learning, advanced analytics and forecasting all rest.

AI provides granular insights into operations and operational performance, historical and current margin performance, route optimization, process efficiency and improvement and other key business areas.

Additionally, generative AI is an exciting development beginning to influence the freight and logistics industry. Generative AI can be leveraged to improve a platform's machine learning capabilities and performance, improving depth and richness of results and response time.

For example, Stargo utilizes generative AI to further train its AI-powered platform StarDox, for even better performance results. Using generative AI, StarDox is able to process hundreds of thousands of auto-generated RFQs and price requests to optimize its recommended price request and shipment proposal generation capabilities.

Conclusion

Smarter, better data management is the only way forwarders are going to overcome their inefficiency hurdles and become more competitive, agile and profitable. But better data management doesn't happen in a vacuum. It goes hand in hand with strategic data utilization - which is only possible when powered by robust AI capabilities and digital platforms like Stargo.

Stargo harnesses the power of AI and deep learning to centralize and simplify your data management operations, helping you unlock more productivity, accuracy and revenue.

Book a demo to see how Stargo can help you unlock powerful efficiency gains through AI-driven performance.