Direct to Consumer

Cost per drop reduction and unlocking scale via automation


  • 99.52% on-time, in-full ETA accuracy
  • 80% reduction in back office scheduling time
  • 653% growth rate

There are many delivery management platforms and route optimization systems that help companies automate and optimize their route planning. But few, if any, are designed for high density, high volume operations that are growing quickly. Home Delivery Services, one of the biggest nationwide 3PLs in Australia, was already using a modern cloud-based delivery management platform but was hitting a wall with certain limitations.

Firstly, the software was limited to only two thousand deliveries at a time, and processing the routes took up to an hour. For a quickly scaling operation, this meant there was a long delay between when the next day orders could be finalized and when they could get wheels on the road. By reducing this time, they could keep shopping carts open later, attract new types of customers, and scale faster.

After a trial of Adiona FlexOps, they saw how order-to-depatch time could be slashed and more efficient routes could be created at the same time. Adiona's proprietary machine learning algorithms result in dense delivery clusters instead of long, acring routes. This led to an 11% cost-per-drop decrease and over $3M in annual savings.

By prioritising the potential value of optimisations available using FlexOps, it was clear that our predictive genetic scheduling algorithms were a great match for their existing delivery scheduling process. As this customer relationship has continued to develop, they have been able to drive further efficiencies by customizing their own software on top of FlexOps. This has been easy, fast, and reliable.

Importantly, the FlexOps API was easy to integrate into their existing IT stack by writing a few scripts to import and export the necessary data.  This allowed FlexOps to be completely customized and also remain standalone for fast implementation.

During the pilot, Jordan saw first-hand how Adiona’s AI-based algorithm generated routes that were far more efficient and less wasteful than those created by competing platforms.

Integrating with the HDS system

All companies have their own unique information systems, and the HDS ecosystem was particularly robust given their advanced use of technology. Fortunately, one of Adiona’s core features is the ability to quickly and seamlessly integrate into any company’s existing platforms.Integration begins during the trial phase, when Adiona takes a sample of the client’s data (in any format that they provide) and plugs it into a simulation engine. The process is quick and painless, thanks to Adiona’s proprietary machine-learning technology. Next, the Adiona team members learn as much as they can about the client’s business to see whether any customization is required for the full-scale implementation.

Since the initial go-live, HDS has continued to improve and evolve their own technology. Since Adiona’s APIs are flexible and customizable, HDS has been able to continue adding new features and innovations using their own developers, without needing additional professional services from Adiona.

Implementing Adiona's Software

Once HDS transitioned to using Adiona for theirroute planning and optimization, they saw immediate improvements. HDS transformed their delivery strategies using the following Adiona products.

As the two companies grew, their partnership proved to be a two-way street. Adiona supported HDS’s growth, while HDS helped improve Adiona’s software by providing feedback for continuous improvement.

Adiona helped us achieve 653% growth over the past 3 years, and we finished 11th in the Deloitte Fast 50 in 2020. We’ve since expanded to Europe and the United States, and we’re excited to expand our reach even farther in the coming years.

Jordan Muir
Co-CEO, Home Delivery Services


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