Case Study
Automotive & Industrial Manufacturer
Introduction
Modernizing Invoice Processing to Increase STP and Reduce BPO Costs
A global automotive and industrial manufacturer delivering high-quality
components faced challenges with its outsourced invoice processing. The
company relied on a BPO to automate invoices, aiming to reduce internal
workload, but the solution underperformed and lacked flexibility. This led
to late payments, strained vendor relationships, and inefficiencies in
exception handling. To address these issues, the company partnered with
Lydonia to bring invoice processing in-house, modernize workflows,
improve accuracy, and free internal resources for higher value work.
Challenges
- The BPO-operated solution achieved less than 20% straight through processing (STP)
- Limited control made it difficult to resolve errors or adapt workflows
- Frequent late and missed payments strained vendor relationships
- Exception handling consumed significant employee time
Approach
To modernize invoice processing and improve efficiency, the company partnered with Lydonia to bring the process in-house and implement an AI driven automation solution. Key steps included:
- Repatriation of invoice processing: Moved operations in-house from the BPO to regain visibility, control, and agility.
- Implemented AI-driven automation: Extracted, validated, and routed invoice data automatically, reducing errors and manual effort.
- Workflow redesign: Customized processes to align with internal operations, enabling faster approvals and fewer exceptions.
- Continuous optimization: Improved accuracy and boosted straight through processing over time through ongoing process tuning.
- Staff enablement: Provided training and support for smooth adoption, freeing teams to focus on higher-value work.
Results
- 89% rolling average Straight-through-processing, up from under 20%.
- $165K annual savings from eliminating the BPO contract.
- Improved vendor relationships due to timely, accurate payments.
- Solution tailored to internal workflows, optimized for efficiency and scalability.
