Aftermarket Analytics Selects Gurobi as Exclusive Partner to Revolutionize Supply Chain Optimization Application Development

BEAVERTON, Ore. and PUEBLO, Colo. — September 28, 2021 – Gurobi Optimization, LLC – which produces the world’s fastest mathematical optimization solver, the Gurobi Optimizer – today announced it has been selected by supply chain software solution provider Aftermarket Analytics as its exclusive mathematical optimization solver. This partnership will embed the Gurobi Optimizer as a key component of the Aftermarket Analytics’ Supply Chain Optimization (SCO) platform, therefore providing access to the most flexible and robust supply chain optimization solution on the market.

Data scientists, business analysts, and operations research professionals have complex roles and requirements – and they must be able to customize their applications while avoiding internal barriers, approvals, and extensive education. In a market with large players and limited competition, the Gurobi and Aftermarket Analytics partnership introduces an easy-to-use solution for those building and deploying SCO applications – maximizing value and increasing ROI.

As a result of this partnership, data scientists, business analysts, and operations research professionals can quickly access essential SCO capabilities at scale, including the ability to:

  • Conduct mixed-integer programming pre-configured for supply chain network design and multi-echelon inventory optimization
  • Implement a cap usage and pricing tiers to manage users’ time on the SCO platform
  • Build analytical applications, in addition to solving

“Supply chain network design optimization software has become unaffordable to all but the largest companies,” said Justin Holman, PhD, Chief Executive Officer at Aftermarket Analytics. “Aftermarket Analytics is excited to team with Gurobi to level the playing field in the SCO software space by offering the most robust optimization solver available in the industry. Scalable and easy to use, the platform will allow users to rapidly deploy their own secure custom optimization applications.”

“Paired with Gurobi’s best-of-breed solver, Aftermarket Analytics’ cutting-edge SCO platform will provide the long-awaited merging of customization and operational efficiency,” said David O’Keefe, Ecosystem Sales Director at Gurobi. “The industries and customers served by Aftermarket Analytics can now make data-based decisions and maximize profitability at a quicker rate than ever before.”

 

About Gurobi Optimization

Gurobi produces the world’s fastest and most powerful mathematical optimization solver – the Gurobi Optimizer – which is used by leading global companies across more than 40 different industries to rapidly solve their complex, real-world problems and make automated decisions that optimize their efficiency and profitability. As the market leader in mathematical optimization software, Gurobi aims to deliver not only the best solver, but also the best support – so that companies can fully leverage the power of mathematical optimization (on its own or in combination with other AI techniques such as machine learning) to drive optimal business decisions and outcomes.

Founded in 2008, Gurobi has operations across the USA, Europe, and Asia and has more than 2,500 customers globally, including SAP, Air France, Uber, and the National Football League (NFL). For more information, please visit
https://www.gurobi.com/ or call +1 713 871 9341.

About Aftermarket Analytics

Aftermarket Analytics produces a Supply Chain Optimization (SCO) platform that is revolutionizing the way companies approach network design and multi-echelon inventory deployment. SCO not only facilitates powerful network modeling and optimization, it also empowers data scientists and business analysts with the ability to rapidly deploy optimization applications throughout the enterprise.

 

Contacts

Nell-Marie Colman
(540) 952 9719
Gurobi Optimization
colman@gurobi.com

Justin Holman, PhD
Aftermarket Analytics
(719) 582-6555
justin.holman@aftermarketanalytics.com

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