Mastering Data in Freight: Challenges and Solutions

In 2005, the cost of disorganized data for American businesses alone was $600 billion. For the freight companies handling industrial quantities of data daily, including shipment details, fuel use, price proposals and route planning, that problem can cost up to $20 billion in revenue annually.

The Sources of the Data Problem

The sources of unstructured data are legion, including email inboxes, GPS devices, IoT and WMS/TMS/ERP systems. That's why it's so hard to manage. But it's worth tackling since this data problem costs freight, logistics and supply chain companies up to 20% in lost revenue annually.

The Challenge of Managing Freight Data

The problem? Two words. "Data silos." This happens when different departments or partners store their data separately, making it difficult to get a complete picture.

The other challenge is ensuring the data is accurate, which is impossible using manual data management, such as putting together price proposals. As if there weren't enough challenges, there's also the not-insignificant task of keeping this revenue-generating safe as houses.

(Learn more about GenAI and data security here.)

Why Manual Data Management Is Failing Your Supply Chain

Freight forwarders, for example, struggle with manual data processing. Handling proposals can take 20 minutes to 2 hours, and pricing accuracy is only about 60%, leading to costly errors and missed opportunities. Customer satisfaction suffers, with low conversion rates at around 25%, as frequent errors disrupt operations and harm client relationships.

The Ripple Effect of Poor Data Quality in Global Supply Chains

For the $9.41 trillion global logistics industry, poor data quality impacts the entire supply chain:

• Delayed shipments: Late deliveries lead to lost sales and dissatisfied customers.
• Incorrect pricing: Pricing errors result in lost revenue or increased costs.
• Regulatory violations: Non-compliance can lead to fines and penalties.
• Customer dissatisfaction: Poor data quality creates a negative customer experience.

The Impact of Generative AI on Supply Chain Data Management

Imagine a world where every shipment is tracked in real-time, invoices are processed instantly, and accurate, up-to-date data powers thousands of daily decisions. With Generative AI (GenAI), this vision is becoming a reality, boosting productivity by 27% and processing data up to 90% faster.

GenAI automates tasks like data extraction, cleansing, and structuring, freeing human resources for more strategic work and enabling businesses to operate with unparalleled efficiency and precision.

Stargo: Leading the Charge with GenAI

At Stargo, we recognize the challenges poor data quality presents in the freight, logistics, and supply chain sectors. Our GenAI solution automates and structures previously unusable data, transforming it into actionable insights improving efficiency, accuracy, and profitability.

Consider the impact of integrating Stargo's GenAI into your operations:

Want to dive deeper into the data issue?

Download our whitepaper on the challenges of poor data quality in the supply chain and discover how Stargo's GenAI can transform your operations.

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