Proof of Delivery (POD) is a document that confirms an order has arrived at its destination and was successfully delivered before the invoice can be billed for payment.

Lack of an electronic POD system leads to inefficient, manual processing due to varied legal and contractual documentation requirements, resulting in longer billing cycles. Diverse formats and layouts from different carriers complicate data extraction from paper-based PODs.

• Developed a Document Processing Bot with UI Path AI Center, leveraging Document Understanding and Optical Character Recognition for managing various carrier documents
• Optimized data models for major carriers, focusing on the top five document types that represent 80% of the volume
• Implemented UI Path Action Center's "Human in the Loop" to handle exceptions and conducted 6-8 weeks of rigorous training on the Document Understanding model to ensure accuracy and meet confidence targets

• Achieved a 95% reduction in POD turnaround time, dropping from 48 hours to 2 hours, significantly boosting customer satisfaction
• Enhanced productivity by 87.5%, confirming receipt and condition of freight efficiently
• Reached 80% process accuracy, with continuous enhancement via automatic retraining
• Cut the billing cycle by 35%, allowing immediate use of data for customer invoicing

Performing crucial supply chain logistics, a provider’s operations team was struggling due to the high volume of scheduling appointments with shippers, receivers, and carriers, which involve back and forth emails, phone calls, or manual data entry into multiple Transport Management Systems (TMS).

These appointment-scheduling complexities vary based on the parties involved, from sending an email requesting appointment times to accessing a TMS and selecting what’s available as per their schedule.

Lacking proper analytics, sales representatives were unable to pinpoint peak appointment times, track cancellation rates, or discern customer preferences, often leading to shipment delays and incurred detention charges.

• Deployed multiple rule-based, automated workflows to pull information from incoming appointment requests (from emails, web forms, etc.) and automatically input it into the various TMS used to book pick-up and delivery appointments
• Developed a Power BI dashboard to visualize appointment trends, peak times, and cancellation rates, providing insights into customer behaviors, including frequent reschedules, preferred times, and typical lead times for booking appointments
• Delivered a reusable solution that could be leveraged for other business areas

• Bots operating 24/7 have led to over 15,000 monthly appointments being scheduled, resulting in a 50% reduction in manual scheduling hours
• The productivity of the operations team has improved by 50%, enabling staff to concentrate on high-value tasks rather than manual appointment-booking
• The increased accuracy in scheduled appointments has significantly decreased detention charges, thereby boosting overall customer satisfaction