Grocery and Meal Kits
Adiona FlexOps for Refrigerated Deliveries
reduced cost per drop (CPD)
reduction in backoffice scheduling time
in annual savings
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. This refrigerated courier was 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.
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Post Covid-19, the value of FlexOps has increased even more for this customer. With drivers now harder to recruit, efficiency with existing resources is more crucial than ever. The ability to model processes, data flow, and automatically identify efficiency bottlenecks is a competitive advantage and continually uncovers new potential improvement initiatives.