Navigating the storm of the dispatching and delivery industry

A perfect storm of market disruption is coming to the e-commerce and last mile industry, according to Arthur Axelrad, CEO of Dispatch Science, speaking at Home Delivery Conference 2018 in Atlanta. Dispatch Science wants to help companies leverage the power of automation and artificial intelligence (A.I.).

There are four factors to this storm: (1) unprecedented customer expectations, (2) highly accessible technological innovations, (3) e-commerce giants consolidating the market, and (4) customer IT.

Customers have been trained for immediate gratification—and through the technological leaps, they are getting it. They want transparency 24/7 and with the right to make cancellations and changes immediately.

And while traditional competitors are investing in new technologies to increase their delivery capacity and drive down prices, at the same time the cloud is leveling the playing field. “You now have super computers without the cost. Now small-to-mid-size businesses can play the game without having to pay millions of dollars just for the computer power,” says Axelrad.

“It’s the Industrial Revolution 4.0 with massive distributions and robotization that make it all possible,” says Axelrad. “Technology acceleration is disrupting business models. It’s the modernization of infrastructure, networks and internet. It’s device-driven consumerization, Uber innovation trends, IoT expanding roots—connected objects feeding our updates and participating in the innovation—and pervasive connectivity in the field.”

Dispatch Science was formed in 2016. Their 20-person team is made up of software and mobile technology experts who were inspired by courier company veterans frustrated by rising costs, lower margins, and a lack of adequate tools. The team is comprised of proven veterans in developing successful technology solutions for the transportation industry, including mobile apps, as well as marketing and implementing enterprise software applications. Axelrad says the team has been working for the past seven years on creating and developing advanced cloud-enabled technology solutions for transporters, with a focus on web, GPS, and mapping technologies.

The result is a new Courier Management System that improves on all the key features courier operators already expect from their management systems. Axelrad says that what sets them apart is their state-of-the-art algorithms that use A.I. to automate the order dispatching process.

What is automated dispatching? It’s semi-complex decision-making, which makes the determination of the best “supply” for existing “demand,” and offers pertinent communication between all parties. “That’s a crucial part of it,” Axelrad says. “Everyone needs to be able to communicate.”

Automated dispatching makes decisions based on criteria. Essentially, it anticipates the best driver, and matches that driver to the best route based on the SLA (Service Level Agreement) through access to connected devices. It also assigns based on package size and volume with the best match vehicle.

It’s one thing when the orders and matching system is simple or merely “demanding.” It’s another when they get “complex.”

“It’s sort of like computer chess only your opponent doesn’t play by the same rules, your pieces can get stuck in traffic and break down, and it scales to playing thousands of games at the same time,” says Axelrad.

It’s also a mindset. Axelrad says you’ve got to believe it can be done, and that nothing is too complicated. “Importantly, separate the mundane from the complex,” he says. “Score your drivers, know whose on top of their game. The business rules must be clear.”

A first step to working with a new company is doing a triage, checking out the service levels of areas with our drivers. “We give them our algorithms and cross-referencing what they already do with mapping the zones. We change the paradigm so that when workloads are assigned they don’t have to go out all at the same time.”

What else differentiates Dispatch Science? “We have a configurable optimizer, which can run at preset intervals, as often as every five minutes,” Axelrad tells FreightWaves. “Each time the optimizer runs, it recalculates the entire solution. It takes into account the real-time position of drivers, all constraints, and updates the model.”

The software also has an underlying set of tools so that it’s more like job-based optimization. It looks at math-based solutions. “We now need to give it the ability show what it can do. It’s a new approach with new technology with constraint-based, problem solving capability with current traditionally based solutions,” says Axelrad.

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