$6B Retailer
Route Optimization

Supply Chain Optimization
Case Study


To significantly reduce the transport costs and Co2 output from freighting by optimizing the retailer's routing and DC ranging.

This client operates hundreds of retail locations. They had identified that there were likely to be inefficiencies in their freight routing and the ranging at the Distribution Centres, this present an opportunity: freight kms could be reduced. 

Prior to engaging Remi AI, the Retailer had attempted to optimize their routing internally, via analytical tools. These efforts had been unable to identify the best routing approaches.


As part of its push to optimize operations, the retailer leveraged the Remi AI Optimization application. By applying machine learning in conjunction with a digital twin to test different possible routing approaches to optimize the routing plan while maintaining range and availability.  

  • About the Client

    • AU$6 billion annual revenue
    • More than 500 retail stores
    • More than 100 million kilometres in freight per year
    • B2B & B2C customers
  • Project Objectives

    • Develop a Digital Twin 
    • Apply AI to dynamically optimize routing and DC Ranging to reduce kms travelled without reducing service level performance
    • Create a production-ready route plan


of potential value identified for freight reduction
reduction in freight kms
Co2 tonne reduction annually