Case study

YETI improved efficiency while saving over 10% on nonstrategic purchases

Fairmarkit rapidly improved YETI's tail spend management processes by enabling buyers to leverage Fairmarkit's automated processes and machine learning algorithms to increase the number of vendors invited to requisitions.

Industry
Consumer Goods Manufacturing
Location
Austin, Texas
Employees
640
“In essence, Fairmarkit is turning the traditional catalog procurement approach on its head by providing us with a ‘competitive-shopping’ environment with the best pricing and little management,”
Cristina Machado
Procurement Manager

The company

In 2006, YETI Coolers was founded with a mission to build a cooler for everyday use. Over 10 years later, YETI products are sold all over the country by retailers such as West Marine, Bass Pro, Cabela’s, REI and Dick’s  Sporting Goods. The company's durable and affordable products quickly grew in popularity at a rapid pace. However, as YETI scaled, there were areas of their procurement spend that was left unmanaged.

The challenge

YETI was struggling to determine how to properly manage their smaller area spend. Like many companies, they were facing a tradeoff between saving time or money. Before Fairmarkit, they had two options: source and send out RFQs to multiple vendors or default to a vendor that may be overcharging them. Spending hours manually sourcing for smaller purchases seemed like a waste of time but defaulting to specific vendors gave them little spend transparency or bid competition. For years, YETI had no process to manage their tail spend. YETI end users would place orders from spot buy catalogs when they needed a one-time, off-contract purchase. This caused YETI’s procurement department to have limited visibility into their tail spend data and couldn’t identify procurement risk, ROI, or realize actual monetary savings. For their Fairmarkit pilot, YETI had two main goals: standardize and streamline YETI’s small purchase processes to ensure procurement can effectively manage and source tail spend and invite more competition to drive purchase prices down without increasing manual effort.

The solution

YETI chose Fairmarkit with the hope of finally setting an organized and defined tail spend management process. After the four-week implementation process, Fairmarkit was able to help YETI streamline their entire sourcing and RFQ process using their patented machine learning technology. Fairmarkit used all of YETI’s documented purchasing and vendor history to automatically create RFQs. YETI’s buyers were automatically presented with a list of their own vendors, as well as a list of vendors from Fairmarkit’s vendor ecosystem, that could supply the items YETI was looking to purchase. With only one click, the buyers were able to automatically send chosen vendors an invitation to bid on the RFQ. The vendors would then receive an email from Fairmarkit with all the RFQ information, the option to submit a bid and the amount of time left remaining to submit their bid. Once the bid time closed, the YETI buyers used Fairmarkit to easily compare the bids side-by-side and choose which vendor to award the deal to.

The result

Fairmarkit’s solution was able to turn the sourcing and competitive bidding process for YETI from an onerous process that took multiple people hours to complete manually to an efficient process that took one person a couple of minutes to finish. In their first transaction through the Fairmarkit platform, YETI realized over 25% in savings and over 53% by the fourth. After signing with Fairmarkit, YETI was able to find an average of 10.25% savings per RFQ pushed through Fairmarkit.

“What I love about Fairmarkit is that it has actually solved the problem of making the time spent on item-level bidding negligible which is where we all wanted to be in the first place.”

Cristina Machado, Procurement Manager

Fairmarkit’s machine learning and competitive bidding process were able to completely eliminate the savings versus time tradeoff YETI had previously faced. Additionally, YETI no longer had to worry about making the end user's life more complicated because Fairmarkit plugged in seamlessly to their existing procurement systems. Fairmarkit was successfully able to achieve the original goals set by YETI using machine learning technology to invite more vendors to bid with less work, as well as streamline their entire sourcing and RFQ process.