case study

Coke One North America

Product Stocking Recommendation Engine

Retail & CPG

Business Impacts


Potential increase in revenue


Model accuracy


Potential reduction in operational costs

Customer Key Facts

  • Location : North America
  • Industry : IT & Services

Problem Context

To strengthen the overall business model of the Coca-Cola bottling refranchising in North America, the six largest Coca-Cola Bottlers announced the formation of an Information Technology services company, Coke One North America (CONA), in 2016. CONA runs huge supply chain operations and manages thousands of vending machines across the United States.

These vending machines are stocked heuristically and require manual effort to properly stock and operate. Product mix is rarely changed and a lot of products are never tried in the vending machines. Therefore, CONA wanted an interpretable model which could recommend products for each vending machine, based on similar vending machines and the most popular products in a particular geography.



  • Scaling the solution to cater to multiple products across different stores
  • Uncovering impact of holidays, seasonal and weather factors
  • Missing data, scarce data and low number of data points

Technologies Used

Microsoft Azure

Microsoft Azure



SQL Server

SQL Server

Apache Hive

Apache Hive

Developing a Stock Recommendation Engine for Vending Machines in Order to Improve Sales


Quantiphi and CONA jointly developed a custom Product Stocking Recommendation Engine, called VendPrime, that combines collaborative filtering and popular demand to generate real-time recommendations for Coca-Cola vending machines across North America. The solution analyzes large volumes of data and provides actionable insights for bottlers, enabling them to function efficiently with accurate forecasting. This also helped drive sales growth and reduce the amount of product that sat untouched in the machines. The recommendation solution further manifests into multiple use cases from small and midsize convenience retailers to full-service restaurants.


  • Improved stocking, reducing waste of products
  • Enhanced consumer experience
  • Higher revenue per visit

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