Logistics Data Processing Services

Case Study 1

Freight Bill Data Processing.

Client.

Our client was a logistics company based in Dallas, USA. This logistics department specializes in comprehensive freight, supply chain, and transportation management. They have happy clients inside Dallas, and more was pouring in. So, they were managing a large volume of freight bills, operations, paperwork, etc.

Challenges.

With the increasing number of clients and partnerships, manual data entry was time-consuming and inaccurate. Managing a high volume of paper-based bills and documents led to data entry errors. Freight bills had to be sorted as the formats and structures were different. Moreover, they faced difficulty in tracking and reconciling freight bills according to the purchase orders.

Solution.

After addressing the issues, we decided to implement an automated freight bill processing model into the management. With our OCR-powered model, we can capture, process, and transfer critical logistics data across different platforms simultaneously.

Results.

After the successful integration of our automated freight model, our client experienced a significant reduction in collection, processing, and transferring claims. The need for human intervention and manual errors was reduced significantly. Our custom freight bill processing model enhanced visibility and real-time bill tracking efficiency. This helped our client streamline purchase orders and rate management data seamlessly.

Case Study 2

Invoice Data Processing.

Client.

Our client is a leading logistic and supply chain operator based in Ohio, US. They had a decent chain of clients and subcontractors that included clients, vendors, and subcontracts. So, a high volume of invoices has to be manually processed across different sectors.

Challenges.

Our client was stressed dealing with large bundles of invoices with different formats and structures. Therefore, manual entry and validation of different invoices caused delays and confusion in payment processing, leading to inaccurate purchase databases and mismatches.

Solution.

OODE stepped into the scene with an automated invoice data processing system that collects, processes, and stores invoices on a structured basis. Our proposed model used advanced machine learning and automation tools that accurately capture and organize data seamlessly into a structured format. In addition, we began integrating our OODE invoice model into their existing CRM for transparent invoice processing.

Results.

After the successful integration of the automated invoice processing model, our clients reported a massive surge in speed and accuracy in invoice processing. Our model brought organized and structured invoice formats that resulted in efficient payment processing and improved vendor relationships.