Revolutionizing Invoice Processing for a North American Retail Giant

Industry

Retail

Region

North America

Client

Major Retailer

Executive Summary

This case study explores the challenges faced by a prominent North American wholesale retailer in managing a large number of invoices. Diaspark, a leading IT consultancy specializing in AI solutions, partnered with the retailer to implement an intelligent automation solution leveraging Robotic Process Automation (RPA) and Machine Learning (ML) technologies. The solution significantly improved the efficiency and accuracy of invoice processing, resulting in substantial time savings and enhanced productivity for the accounts payable (AP) team.

The Challenge

The retailer received a high number of invoices daily, ranging from 550 to 700, with peak days reaching even higher volumes. This high volume resulted in a manual processing approach that was not only time-consuming, taking 4-6 minutes per invoice, but also prone to errors. The slow manual process created a backlog in the accounts payable department, slowing down their work.

The Solution

Diaspark designed and implemented a comprehensive automation solution tailored to the retailer’s specific needs. This solution utilized a leading Robotic Process Automation (RPA) platform, in conjunction with Optical Character Recognition (OCR) and ML capabilities.

Steps of the Automated Solution
Email Processing
A bot scans emails for invoice attachments. Non-invoice attachments are flagged for manual review, while invoice attachments are saved for processing.
Invoice Digitization
The bot uses OCR to locate and separate the header and footer of each invoice page, ensuring correct page order and segmentation.
Data Extraction
Leveraging OCR and ML, the bot extracts key information such as invoice date, number, amount, and due date. Special training was provided to recognize bill-of-lading information specific to trucking services.
Queue Management
Invoices with a confidence score above 95% are sent to a queue for payment processing. Those below the threshold are forwarded to a human operator for validation via Power Automate bot. Feedback from these validations continuously improves the ML model.
Repetitive Processing
The bot repeats the process until all invoices are processed or queued for reconciliation.
Reporting
A comprehensive report is generated detailing the bot's performance, including the number of items processed and time taken. These reports are archived for future reference.
This flowchart shows the steps for automating invoice processing in retail, from receiving invoices to paying them.

Results and Impact

Efficiency Gains
Time Saved
The automated process reduced the time taken to process an invoice from 3-5 minutes to just 30 seconds, saving over 160 hours monthly.
High Accuracy
The automation achieved a 95% confidence score for data extraction, with 93% of invoices processed without manual intervention.
Improved Productivity
Resource Allocation
The AP team could now redirect at least 20% of their workforce to more strategic tasks, enhancing overall productivity.
Continuous Improvement
The feedback loop in the ML model ensures ongoing accuracy improvements, making the process more efficient over time.

Conclusion

This case study demonstrates the transformative power of intelligent automation in revolutionizing critical business processes like invoice processing. The collaboration between Diaspark and the North American retailer showcases how RPA and ML technologies can significantly enhance operational efficiency, improve data accuracy, and empower employees to focus on more strategic initiatives. By embracing automation, businesses can achieve greater agility and a competitive edge in today’s dynamic market landscape.