By Oscar Michel, Masters in Journalism, DCU

Deliveroo‘s delivery time has been reduced by 20% delivering food in 7 minutes.  Riders can now earn more by working fewer hours.

This information was known by the public on August 10 when Deliveroo released a unique insight into its latest technological advances.  Deliveroo’s “Frank” algorithm is based on powerful predictive technology that is able to evaluate the most efficient way of distributing orders based on the location of restaurants, riders and customers.

Liam Keenan, General Manager of Ireland at Deliveroo said: “Deliveroo’s technology is helping us to create the world’s best food delivery service. Our new algorithm will continually refine itself, improving the experience for our Irish riders, restaurants and customers.” 

The Algorithm “Frank” was named after the character Frank in the Tv show “it’s Always sunny in Philadelphia” played by Danny DeVito.

With Frank, machine-learning helps to predict the time needed to prepare a meal by analysing a specific type of order depending on a specific time of the day. Deliveroo delivers food for more than 500 restaurants in Ireland and has more than 400 riders. This new algorithm reduces to seven minutes the time of delivery and therefore riders can make more money without working extra hours.

Mike Hudack, CTO at Deliveroo added “Deliveroo’s custom-built technology is the most exciting thing being developed in Europe right now and we want our new London HQ to be where global innovations happen. We’re building an algorithm which can cope with millions of simultaneous orders, while continuously refining itself, improving the experience for our riders, restaurants and customers. Our investment in the engineering team is testament to that ambition, and we are working on new, trailblazing technology that will make that ambition a reality.

This “nerve centre” constantly looks at available riders and orders, then evaluates within seconds the most efficient way to dispatch them. After assessing all possible dispatch solutions, the algorithm picks the best one for rider, restaurant and customer. This decision process has been based on machine-learning predictive models of when the food will be ready, how long every part of the delivery process will take and which rider has the best characteristics to fulfil that specific order based on distance, type of location and other factors. The result is that customers get ever more precise details of when deliveries will arrive, with overall delivery times falling 20% lower than they were in January.

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