PFI:AIR - TT: Fast Multi-Echelon Optimization via Grouping

University Of Arkansas, Fayetteville AR

Investigators

Abstract

This PFI: AIR Technology Translation project focuses on translating optimization and inventory segmentation research to fill the need for fast multi-echelon inventory optimization within complex supply chains. The U.S. has over $2.2 trillion invested in business inventories; thus, companies require computationally efficient algorithms that can minimize the cost associated with managing these inventory assets. Multi-echelon inventory optimization determines the correct levels of inventory across a network based on demand variability at the various nodes, or echelons. It considers inventory levels holistically across the entire supply chain while taking into account the impact of inventories at any given point (or echelon) in the system. This project will result in prototype algorithms in the form of software services that result in a multi-echelon inventory segmentation analyzer. This multi-echelon inventory segmentation analyzer has computationally efficient optimization run times, near optimal solution quality, and the ability to perform fast "what-if" analysis. These features provide the following advantages: shorter times to find optimal solutions, higher quality solutions, and the ability to mitigate risks through "what-if" analysis when compared to currently available segmentation analytics techniques. This project addresses the following technology gap(s) as it translates from research discovery toward commercial application. First, this research addresses the best group size when applying the segmentation analytics to large-scale industrial datasets. Secondly, this research optimizes the parameter settings associated with the algorithms in order to determine the best settings with respect to the trade-off between computational speed and solution quality. Lastly, this research performs experiments in order to determine the best grouping criteria to use within the segmentation analytics. In addition, personnel involved in this project, including the graduate and undergraduate students, will receive innovation, entrepreneurship, and technology translation experiences through experimental research methods, interaction with a small business, and software development processes. The project engages Invistics Corporation in this technology translation effort from research discovery toward commercial reality.