Proprietary applications use predictive pricing and automation to drive value for shippers and carriers through improved accuracy, enhanced service, and increased velocity NILES, Ill. - Dynamic third-party logistics provider AFN Logistics today launched two new proprietary technology tools. AFN’s Suggested Booking Price (SBP) tool automates the freight quoting process using a predictive cost model driven by advanced machine learning technology. The tool determines the suggested booking prices of shipments with significantly higher accuracy and consistency than other widely-available tools. AFNgine, a web-based task management tool, helps coordinators more efficiently prioritize and track shipments from pick-up through delivery. AFNgine allows AFN’s logistics managers to analyze shipment data by customer, carrier, and team member in order to deliver insights that drive process improvements, greater efficiency, and more accurate resource allocation.    Team members now use the tools to serve customers and partners across the business, from truckload (TL) and less-than-truckload (LTL) customers to managed transportation customers and its carrier network. Both tools came to fruition as a result of AFN’s culture of entrepreneurial innovation. “These new tools illustrate AFN’s strategy to build our own IT solutions when the tools we need are not readily available in the marketplace,” said Rob Levy, CIO and CFO of AFN Logistics. “In the hands of AFN’s highly-trained team members, the SBP and AFNgine tools will enhance the service and value that we provide to our customers and carriers alike.” Suggested Booking Price Tool – Outperforming Industry Tools on Pricing Accuracy and Consistency AFN’s data scientists and business intelligence teams recognized an opportunity to automate the quoting process. The teams collaborated to create the SBP tool, which instantly determines suggested load booking prices based on market conditions like lane, commodity, fuel price, and distance, among others, layered against AFN historical data and external market data. The model uses machine learning to continuously update prices in real-time each time a new load is booked, automating the previously-manual rate-sourcing process. The SBP tool was beta-tested over the past several months, and predicts AFN’s actual costs per shipment with 30 to 40 percent greater accuracy than commonly-used industry tools. “The SBP tool allows our team to quote freight rates instantly and more precisely. This saves time, increases booking velocity, and gives our customers a greater degree of confidence that they’re paying the right price in a dynamic marketplace,” said Levy. AFNgine – Enhancing Visibility, Efficiency and Service Similarly, AFN’s client service teams identified an opportunity to improve accuracy and efficiency within the shipment tracking and management process. Both client service and IT teams collaborated to develop a proprietary web-based platform which provides visibility and task prioritization to logistics coordinators. Greater visibility allows AFN team members to quickly identify and resolve potential shipping issues, thus raising customer service levels for both shippers and carriers, eliminating redundancies, and enhancing efficiency and productivity. Since launching, AFN’s logistics coordinators have been able to service an average of 21 percent more unique shipments per day. “We’re a people-powered company with a thriving entrepreneurial culture. We want to empower our team members to find new and better ways to create value for our customers and carriers,” said Ryan Daube, co-founder and CEO of AFN Logistics. “We’ve built an in-house development team that takes a highly collaborative approach with the operators of the business, enabling us to rapidly turn ideas into solutions.”