AI & Machine Learning Saves Millions for Fortune 500 Electronic Retailer


AIM Consulting streamlines part selection operations to save millions

Retail electronic repair services provide convenience, upsell opportunities, and customer loyalty – if effective. Without a centralized inventory, the process can be daunting – with inaccurate repair recommendations and wrongly ordered parts – thus delaying repairs, extending resource time allocation, and increasing costs for labor and materials. This process is not only frustrating to the customer but costly for the retailer. Streamlining an organization’s centralized process can be the difference in millions on the bottom line.

Minnesota-based Fortune 500 Consumer Electronics Retailer learned this all too well with their electronic repair business. The company produced nearly $1.5 Billion in annual revenue through its repair services, yet its profit margins were under constant strain from labor and material costs.

The AIM Consulting team identified the root cause of the issue and built a centralized custom Parts Recommender analyzer on Azure infrastructure. This created well-vetted repeatable data for product selection all repair employees could access during the initial assessment with customers, eliminating manual support device selection, improving customer experience, saving the company time, resources, and millions in misidentified parts.

Key Results

  • Built a highly scalable and flexible parts recommender designed
  • +20,000 service repair agents supported
  • Five Teams directly impacted
  • Over $10 Million in annual savings
+20 Thousand Agents Supported
+$10 Million Annual Savings

AIM Consulting streamlines supply chain management process with Machine Learning (ML).

I feel privileged to have worked on such a complex data science project with the client. We have a really talented team here at AIM Consulting and we’re excited to be delivering results that make an impact for the client. It’s great to work at an organization that not only values your skills, but also supporting our clients with successful solutions.

Senior Consultant, Data & Analytics AIM Consulting


The organization product diagnostics  –  processes for determining what was wrong with a product and what parts were required to repair it- was inconsistent and often wrong, leading to an increase in labor costs to identify the problem correctly.

Even if the diagnoses were correct, the company did not have a standard operating procedures (SOP) for many of the repairs; techs were responsible for making judgment calls regarding the appropriate parts for many repairs.

This led to substantial waste in replacement part costs and increased labor when the implemented repair did not fix the customer’s problem.

The Results

Leveraging world-class methodology and best practice development, AIM Consulting was able to produce results within four months from the start of their engagement. Providing a highly scalable, flexible end product that is operating seamlessly and well within the company’s project scope.

Now with a scalable, modern application architecture that provides valuable decision support using cleaned and well-vetted data, the logistics teams have less ordering throughput, the operations team gets to manage less redo work, and the service techs benefit from the focused parts recommendations to have more successful repairs for their customers.

Using a soft-attribution technique, the client saves, at minimum, $10 million a year in labor and parts costs associated with ordering.

2 Weeks

Discovery Phase

4 Weeks

Data Analysis

8 Weeks

Solutions Design

2 Weeks

Trainings & Handoff

AIM’s Approach

AIM Consulting worked with stakeholders to understand the demands for all teams involved, understanding this process affected across multiple departments, and directly impacted customer satisfaction.

After detailed discussions during the discovery phase, the team decided to leverage the existing data to design a centralized Parts Recommender analytics solution implemented on Azure. Providing the best solution for resolving the expensive material and labor costs.

The Parts Recommender essentially leverages an enormous amount of historical repair data, takes the product diagnostics, and determines the best parts and processes to fix the product.

This was substantially more effective than the previous solution of techs manually searching product catalogs and/or the internet for solutions related to product repairs.

Data & Analytics Offerings

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