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The Challenge
A mid-sized logistics company that offers freight audit and payment solutions was responsible for processing thousands of freight invoices daily, ensuring accurate billing and cost optimization for its clients. As the company expanded its client base, its manual invoice processing systems struggled to keep pace with growing demand. Their operations team wanted assistance handling the following challenges
| High-Volume Processing Bottlenecks |
|---|
| The company was processing over 60,000 freight invoices daily, with each invoice containing an average of 42 line items. The manual data entry required for this volume created significant bottlenecks, with processing times extending to 3-5 days per invoice – far too slow for their service level agreements. |
| Data Extraction Accuracy Issues |
| Freight invoices arrived in various formats from hundreds of different carriers, each with unique layouts and data structures. The manual extraction process resulted in a 12% error rate, leading to payment discrepancies, client disputes, and strained carrier relationships. |
| Inefficient Invoice Classification |
| The operations team struggled to efficiently sort and route invoices based on carrier, service type, and client specifications. This classification challenge created workflow inefficiencies and prevented the company from prioritizing critical invoices that required immediate attention. |
The Solution
Phase 1: Tech and Feature Planning
The implementation began with custom OCR models trained to recognize diverse freight invoice formats, identify industry-specific data fields, and flag potential errors using company validation rules.
Phase 2: Integration & Workflow Automation
The solution was seamlessly integrated with the company’s existing systems by establishing API connections with the company’s transportation management system. Intelligent workflow automation allowed for intelligent invoice routing and prioritization, and real-time dashboards helped the company manage these workflows and track KPIs.
Phase 3: Employee Training & Change Management
A comprehensive training program included hands-on training for operations staff, the establishment of new exception-handling procedures, and knowledge transfer sessions to equip the internal IT team for routine maintenance and system monitoring.
Phase 4: Performance Optimization & Scaling
The solution was designed with machine learning model retraining to continuously enhance recognition accuracy, performance analytics to identify additional automation opportunities, and capacity planning to ensure the system could scale with the company’s growth.
Results: From Bottleneck to Breakthrough
The implementation of ITD’s AI-driven solution delivered the following transformative results:
| 75% Increase in Data Processing Accuracy | 60% Reduction in Manual Data Entry | 50% Boost in Overall Operational Efficiency |
|---|---|---|
| Data extraction precision improved, particularly for complex charges and special handling fees, with error rates dropping from 12% to less than 3%. | Staff time dedicated to invoice processing decreased from 120 hours to 48 hours weekly, and the company handled a 30% increase in invoice volume without additional headcount. | Average invoice processing time decreased from 3-5 days to less than 24 hours. Payment cycle times improved, and cost recovery from billing discrepancies increased by 22%. |
| Enhanced Client Satisfaction | Competitive Advantage | Cost Savings & Scalability |
| Faster processing and higher accuracy led to improved client retention rates. | The company now highlights its AI-powered processing as a key differentiator in client proposals | The company achieved ROI within 7 months through reduced labor costs and improved cash flow management. The business can now onboard new clients without proportional increases in processing staff |
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IDS Commander iTech2021
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