RPA Drives Logistics Company
in Top Gear

Client Profile

A 7-year old Dallas-based trucking company offering goods transportation services to reputed clients. They have a fleet size of over 250 transport vehicles and 4 operational facilities.


As operations started shifting gears, the cost of keeping the wheels turning smoothly started getting worryingly high. The problem areas bearing a red flag and demanding immediate intervention included appointment scheduling, shipment tracking, and invoicing.

Appointment Scheduling

RPA was used to introduce auto-scheduling to the workflow and autrouting of service requests.

The team was struggling to keep pace with the rapidly accelerating business growth. Accurate shipment booking was getting increasingly difficult because it included complex calculations and judgment for deciding the delivery sequence, assigning drivers and vehicles, and other factors involved.

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.

Manual force (Before) RPA-powered (After)
Scheduling accuracy 79% 98%
Headcount 5 1
Scheduling time 15 minutes 2 minutes

Shipment Tracking

RPA bots integrated with a Machine Learning module to collect and collate transition data before feeding it into the CRM.

Manual consignment tracking necessitated agents to go through stacks of emails, files, phone calls, and partner portals (that weren’t mostly updated). This got more tedious when the agent had to provide real-time information to the caller seeking shipment status.

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.

Manual force RPA-powered
Average calls to track a shipment 4 0-1
Headcount 3 1
ETA conformance 66% 96%

Invoice validation

RPA bots with OCR capabilities read incoming invoices, validate them, and generate invoice reports.

The 20-member invoicing team caused about 24% invoicing errors daily, in addition to their low efficiency and judgment mistakes. Low efficiency of the clerical staff resulted in erroneous accounting for expenses like fuel costs and toll tax. Manual storage of invoices also implied extra efforts, inconveniences, and costs.

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.

Manual force RPA-powered
Invoice data format Random Predefined digital format
Headcount 20 4
Invoicing errors 25% 3%
Average payment time 4 days Same day

Technology Stack

  • UiPath Robots
  • Google Tesseract
  • .Net
  • ML

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