Optimizing Inventory of Slow Moving Products Using SAS Optimization

About this paper

Aiming to use data and science to set service-level goals, Advance Auto Parts engaged Core Compete to deliver a fully integrated service-level (Inventory) optimization system using SAS® Inventory Optimization and SAS/OR® software. The system has the ability to run inventory simulations and execute large-scale optimization for service-level goal optimization, leveraging the batch services on Amazon Web Services. A very large mixed integer optimization problem for inventory cost reduction is solved using the OPTMODEL procedure.

The solution has the ability to recommend optimized service-level goals at the SKU/location level. System design integrates a simple Microsoft Excel user interface, data processing in Apache Hadoop, and optimization in SAS® in the cloud and the dashboards in SAS® Visual Analytics in order to review results. The end-to-end process flow for implementing simulations and optimization in large scale is discussed in this paper.

What You Will Learn

  • Inventory Optimization Problem
  • Solution Design
  • Technical Challenges and Resolutions
  • Business Challenges and Resolutions

About Core Compete

Founded in 2012, Core Compete is a pioneer in Cloud Analytics and is based in Durham, North Carolina with offices in Dallas, London and India (Pune and Hyderabad). We have 300+ employees and over 500 cloud and technology certifications, with highly specialized skills including: domain consultants, data scientists, and cloud engineers. Core Compete is an ISO 27001 and SOC 2 certified organization.

We partner with AWS, Google Cloud, Microsoft Azure, SAS, Snowflake and Tableau to deliver successful cloud analytics and big data transformations for major corporations worldwide.

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