Fortune 500 Retailer | Data & Analytics

AI & Machine Learning Saves Millions for Fortune 500 Electronic Retailer 

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Streamlined Supply Chain Management with Machine Learning (ML)

Today buying online is effortless…find what you like, add to cart, pay online, and it shows up promptly at your doorstep. However, with the increased online purchasing and overall product consumption, back-end fulfillment capabilities must be effortless and cost-efficient.
A National Fortune 500 Retailer needed to redesign and elevate its current order and fulfillment system to keep up with increasing consumer demands. The team performed an analysis of its upgrade costs and found that this was a critical improvement area. The organization partnered with AIM Consulting to integrate Artificial Intelligence and Machine Learning (ML) to produce an “Upgrade Risk” score for each eCommerce order and incorporated it into the rules-based engine decision criteria.

Key Results

 

  • New Machine Learning Deployment Pipeline
  • 30% Reduction in Supply Chain Upgrades
  • $12 Million in Cost-Avoidance in Perpetuity
  • 900+ Stores Benefitted

Situation

For years the organization operated with a rules-based engine, and while it performed the necessary needs at a base level, several hurdles had come to light with this type of system. First, the system is not adaptable or easily maintained. Second, and more importantly, the rules-based engine was focused solely on minimizing labor and shipping costs. With customers having the ability to quickly and easily change retailers, the organization needed to maximize a positive customer experience while reducing total supply chain cost—increasing customer lifetime value while reducing costs amongst other benefits.
Another unintended consequence of the rules-based engine criteria was upgrading to specific products being sold. These upgrades would occur when the rules-based engine would choose a distribution center to fulfill an order because it passed certain criteria. While the idea to deliver value to customers was well-intended, the result was $50 million in annual upgrade costs for shipping to customers by a promised in-hand date.

The Results

AIM Consulting’s solution saved the organization millions while providing multiple benefits across teams. The results are supported by the cost-avoidance metric created against the baseline rules-based engine. The technical team that owned the Sourcing Engine now has a Machine Learning (ML) deployment pipeline that works for their application. The order fulfillment teams have since reported drastically reduced percentages of orders requiring human intervention and have increased the percentage of deliveries made on time to their customers.  
The entire organization is enjoying an improvement to the bottom line as operating expenses have decreased. The project resulted in $12 million in cost-avoidance in perpetuity, a 30% reduction in the supply chain upgrades, and new Machine Learning (ML) deployment architecture.   

Project Timeline

AIM’s Approach

With an experienced analytics team and data-centric approach, AIM Consulting quickly jumped into the project by first creating a modernization roadmap for the order allocation engine. AIM Consulting integrated Machine Learning (ML) to produce an “Upgrade Risk” score for each eCommerce order and incorporated it into the rules-based engine decision criteria.  
Over the next 12 weeks, AIM Consulting followed the “Discover, Design, Deploy” methodology and created a probabilistic classification model with recalls & precision scores.
These recalls and precision scores would have equated to $15 million in cost avoidance with the implemented model. The modeling took place on Google Cloud Platform, and the MVP model was made available via a Kubeflow endpoint. 

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