CSCMP's Supply Chain Quarterly
Technology
November 20, 2017
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National Science Foundation awards grant to study multi-echelon inventory optimization

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University of Arkansas research seeks to develop software to quickly suggest optimal inventory levels for all nodes in the supply chain.

Typically you wouldn't think of supply chain management as an area that would receive funding from the U.S. government's National Science Foundation (NSF). But the agency recently awarded a grant for almost $200,000 for research on multi-echelon inventory optimization.

The grant, given to Manuel Rossetti, professor of industrial engineering at the University of Arkansas, is part of the NSF's "Partnerships for Innovation: Accelerating Innovation Research-Technology Transfer" program, which supports research that transforms science and engineering research into practical solutions that can help the business community.

Rosetti's project focuses on drawing on concepts developed in optimization and inventory segmentation research to help create software solutions that can quickly optimize inventory levels within complex supply chains. Multi-echelon inventory optimization determines the correct allocation of inventory across a network based on demand variability at the various levels, or echelons, in the network (such as centralized distribution center, regional distribution center, and retail store). It considers inventory levels holistically across the entire supply chain while taking into account the impact of inventories at any given point in the system.

Rosetti will be working with the cloud-based inventory software company Invistics Corp. to create software services that can analyze multi-echelon inventory segmentation, optimize inventory levels, and perform fast "what-if" analysis. The research will focus on: 1) discovering the best group size and criteria to use when applying segmentation analytics to large-scale industrial datasets; and 2) developing a software solution that best balances computational speed and solution quality.

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