Reduction in invoice processing time and average invoice approval time.
Reduced data entry errors
Improved cash flow management through better visibility into outstanding invoices.
The Customer
A manufacturing company struggling with manual invoice processing, leading to inefficiencies and delays.
Challenges
Time-Consuming Manual Work: Data entry clerks spent hours manually entering invoice data into the ERP system, prone to errors and delays.
Slow Approval Process: Paper invoices required physical routing for approvals, extending payment cycles for vendors.
Limited Visibility: Lack of real-time data on invoice status hindered cash flow management.
The Objective
Frontlet technologies implemented an automated invoice processing solution.
Future Ready Solution
RPA Technology: A software bot replicated human actions to automate data entry tasks:
Downloading invoices from email attachments or a supplier portal.
Extracting key data points like invoice number, date, vendor, and amount using Optical Character Recognition (OCR).
Populating the extracted data into the company’s ERP system.
Machine Learning Integration: An ML model was trained to:
Classify different invoice formats based on layout and vendor.
Identify and handle exceptions like missing information or errors.
The Outcome
Increased Efficiency: RPA bots significantly reduced manual data entry time, freeing up employees for higher-value tasks.
Improved Accuracy: Automation minimized human error in data entry, leading to cleaner data in the ERP system.
Faster Approval Process: Electronic invoices with pre-populated data enabled faster routing and approvals.
Enhanced Visibility: Real-time access to invoice data in the ERP system improved cash flow management and vendor relationships.
Reduced Costs: Automation minimized labor costs associated with manual data entry and paper handling.
Technology Stack
Robotic Process Automation (RPA)
Machine Learning (ML)
Project overview
A well-defined process is crucial for successful RPA implementation.
Training employees on the new workflow and change management is essential.
Continuous monitoring and improvement of the ML model ensure accuracy over time.
Demonstrates the power of automation in streamlining business processes. By leveraging RPA and ML, organizations can achieve significant improvements in efficiency, accuracy, and cost savings. This approach allows businesses to free up valuable resources and focus on core strategic initiatives.
