RPA Drives Logistics Company
in Top Gear

RPA was injected to introduce auto-scheduling to the workflow. While booking a consignment, the pickup and delivery points, estimated time frame, preferred vehicle, etc. are entered in a digital sheet. Based on these parameters, bots suggest a detailed delivery schedule. If the bots are unable to do that (for about 15% of the cases), the case is routed to a service agent who accesses the information gathered by the bots and takes charge. They can also tweak the parameters, set new rules, and rerun the bot for a second attempt at auto-scheduling.

RPA bots integrated with a Machine Learning module collect and collate transition data periodically from all multimodal carriers and third-party logistics. The bots then structure the data in a preset format before feeding it into the CRM that’s accessible to agents and AI chatbots. This also helps when the consignee wants to change the delivery date or destination.

In just over 3 months, the ML module has displayed impressive improvements in the precise tracking of shipments. Robots update the tracking portal and eliminate the manual work of the employees trying to help clients keep a close eye on their orders. The bots also send daily notifications to the consignee and consignor mentioning the estimated time of arrival until the delivery is successful.

RPA bots with OCR capabilities read incoming invoices from clients in various formats. After validating their format correctness using ML algorithms, the invoices are fed into the ERP system. For validation, bots pull details of the bill of lading, carrier invoice, etc. from CRM. For failed validations, concerned individuals are alerted through notifications. Payment processing is carried out by the finance team directly without the need for validating the invoices. The bots also generate invoice reports for future reference and alerts are thrown in case of exceptions.