A SoftServe Client Interview with Ray Gabriel, Director of IT Infrastructure McCoy’s Building Supply.
There are two great motivators for change: pain and the desire to grow. Many companies stay in an even-keel mode, unaware of what modernization could do for them, or simply lacking the people, tools, or expertise to act on it. Others take action because of real business pain like waste, missed opportunity, competitive lag. But sometimes a company that's already doing well just wants to do better.
McCoy’s Building Supply is one of those companies unwilling to rest on its laurels
Founded in 1927, McCoy's is a fourth-generation, family-owned supplier of lumber, building supplies, and farm and ranch equipment. The company serves a wide range of customers, including consumers, builders, contractors, repair and remodeling professionals, and farm and ranch customers. The San Marcos, Texas-based retailer is among the largest family-owned businesses in the building supplies industry.
We spoke with Ray Gabriel, Director of IT Infrastructure at McCoy's, to learn about the company's motivations for adopting machine learning (ML) for demand prediction.
For those unfamiliar with your brand, please tell us about McCoy's, your business goals, and the challenges that ML is helping to solve?
Ray Gabriel: "McCoy's is a family-owned building material retail business that is over 90 years old. We operate in five states: Texas, New Mexico, Oklahoma, Arkansas, and Mississippi. We have 88 retail units, two door manufacturing facilities, and distribution sites.
Our goal is to use machine learning to help us control our inventory levels and cash flow more effectively. We want the ML models to help us predict optimal inventory levels for our major product categories, without tying up cash and floor space."
Good enough is never good enough
McCoy's continually invests in its information technology as a differentiating factor for the business, and for its customers. As part of this effort, McCoy’s identified a potential way to use emerging ML technology to improve how the company forecasts inventory demand at store locations.
The hypothesis was that a demand prediction approach leveraging Amazon Web Service (AWS) machine learning (ML) modeling services could further optimize the process for buying inventory and having it stocked at the right store, at the right time.
What attracted you to AWS for this project?
Ray Gabriel: "Our previous experience with AWS began with a proof of concept (POC) to move our data warehouse to AWS. We ran into latency issues while moving large amounts of data daily, so it didn't make sense to move forward with that shift. But even though we didn't pursue that POC, we were still curious about how AWS could help us find other process efficiencies."
Even the best-laid plans don't always work out as intended the first time. Fortunately, the conversation and collaboration between McCoy's and AWS continued thanks in part to Amazon's own success using AI/ML tools to serve its global customers intelligently and cost-effectively.
What made you decide on AWS machine learning services like Amazon Forecast to improve your retail solution and achieve your goals?
Ray Gabriel: "Amazon appears to do a great job managing its own inventory, so we assume that success comes in part from its own ML services and forecasting tools. With that in mind, it makes sense to take advantage of the data structure and modeling Amazon has already proven, and will keep improving."
As an Austin, Texas-based AWS Premier Consulting Partner with numerous competencies and an AWS Ambassador on its global team, SoftServe's ML/Data Science team was brought in to deliver the strategy and implementation for the project.
By using an AWS demand prediction model, McCoy's can now better:
- Free up previously locked capital
- Reduce excess inventory
- Lessen product shrink
- Reduce out-of-stock scenarios to improve sales performance
How long have you been working with SoftServe? What has your experience been like working with them?
Ray Gabriel: "We have been working with SoftServe for well over a year. We appreciate their approach to providing solutions for business-critical operations. Their step-by-step process, ability to collaborate with us, and external knowledge sources are what led us to partner with them."
SoftServe's Intelligent Enterprise COE teams, including experts in Big Data & Analytics, Experience Design, Cloud, Business Analysis, and AI/ML, as well as an APN Ambassador help companies become truly data-driven and not just data-aware.
In addition to predictive forecasting models, these cross-functional teams deliver solutions like Actionable Insights Ecosystems, Enterprise Data Platform, Big Data Cloud Migration, and Intelligent Automation powered by AWS.
Before we go, is there anything else you would like to share about SoftServe’s performance with solution design and implementation?
Ray Gabriel: "SoftServe listened to our needs and took into account our current capacity and how we would like to use our data more effectively. They provided more than one approach to the challenge and gave candid opinions on each solution. We would definitely recommend SoftServe as an AWS Consulting Partner to others, without giving away our secrets, of course."
Let’s talk about where you are with machine learning today, and how AWS and SoftServe can help you turn demand data into smarter inventory decisions.
