Modernized Technology, Enabling Digital Transformation for Retail Organizations

Modernized Technology, Enabling Digital Transformation for Retail Organizations Image

Modernized Technology, Enabling
Digital Transformation for Retail Organizations

Client: Global Convenience Retailer

Project Overview

The largest convenience store chain in the world, with over 71,100 stores in 17 countries, partnered with RTS to help execute and implement several digital transformation focused projects. The goal was to improve customer satisfaction and enhance competitive advantage.

The retail partner used the expert team at RTS to streamline and automate business processes, improve efficiency, reduce cost, and improve the customer experience.

Modernized Technology

AI/ML Based Stock Ordering

Maintaining inventory is a cumbersome yet critical task for any retail business. RTS enabled their client to build an intelligent and modernized stock ordering system that would lead to better productivity and higher profits. RTS performed the required work to create an AI solution that would give their retail client the best outcome and created a system to forecast the future product requirements of every store, which helps them place the proper orders. In doing this, stores globally can be appropriately stocked until the next cycle starts, which reduces out-of-stock rates and overstocking.

AI/ML Based Stock Ordering

Key areas of this project included:

  • Collecting the correct input data
  • Performing required data transformation
  • Training the model based on the transformed data
  • Creating the forecasted dataset with the prediction
  • Making the prediction available to the stores via API

Efficiencies Created

  • Reduced labor costs: AI/ML automated ordering inventory, freeing human employees to focus on other tasks.
  • Reduced shrink: AI/ML helped the retailer track their inventory better and identify potential problems, such as overstock or understock. This also helped to reduce shrinkage from theft, damage, and errors.
  • Improved customer experience: AI/ML personalized customers’ shopping experiences by recommending products that they are likely to be interested in. This increased customer satisfaction and loyalty.
  • Improved forecasting: Integrating AI/ML strengthened a retailer’s demand forecasting, helping to ensure that it has the right amount of inventory on hand. This reduced costs and helped to avoid stockouts.
  • Improved decision-making: AI/ML provided the retailer with insights into their inventory data, which helped them make better decisions about pricing, promotions, and other aspects of their business.

Cashier-Less Store Operations

Cashier-less stores offer convenience and speed for customers while providing customer behavior data to business. Our client knew that by implementing this, they could reduce labor costs, improve process transaction efficiency, and collect valuable data that could enhance their marketing efforts and product lines. One of the significant challenges with a cashier-less store is technology reliability. Our client knew they needed to work with a trusted and proven partner to ensure the success of this project.

RTS helped our client complete this project using an app where customers gained entry to the cashier-less store using a credit card linked to the app for purchases and receipts.

Cashier-Less Store Operations

During the project RTS:

  • Designed, developed, and implemented novel computer vision algorithms using deep learning frameworks such as TensorFlow, Keras, PyTorch, Caffe, etc.
  • Trained neural nets to solve problems like human pose estimation, object detection, and face recognition.
  • Used object contours and backgrounds to generate augmented data in Python.
  • Pre-processed training image datasets using tools such as OpenCV, skimage etc.
  • Designed and developed production-ready code in Python.


RTS and their client partner forged a path from data ingestion to data visualization by enabling AI and creating new business opportunities with IoT. By collecting real-time data and improving inventory operations, our end client was able to identify new products and services for their retail customers.