High Level Inventory Network Modeling Approaches

High Level Inventory Network Modeling Approaches

Tan Miller (Rider University, USA), Renato de Matta (University of Iowa, USA) and Minghong Xu (University of Illinois at Chicago, USA)
Copyright: © 2016 |Pages: 30
DOI: 10.4018/978-1-4666-9639-6.ch002
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Abstract

Over the last several decades, practitioners have used the Square Root of N (SQRTN) and the Portfolio Effect models to develop estimates of the change in finished goods inventory investment that will result from potential consolidations of existing supply chain networks. The relative simplicity of these two models has made them commonly used tools of consultants and practitioners. However, what is often overlooked or ignored in practice is that these models may or may not provide accurate projections, and that there are limitations to the range of problems which these models can address. In this paper, we evaluate the accuracy of projections made by the SQRTN and portfolio effect models under a variety of network conditions, and we provide guidance on when and how practitioners can both use and supplement these models. Our evaluations are based on the results of simulation studies which we conducted for this paper as well as many years of inventory management practice in private industry.
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Literature Review

The SQRTN model dates back over 35 years ago to Maister (1976) who noted that his work, for the first time, provided a model based on a mathematical proof which could estimate the impact on inventory investment of consolidating multiple field warehouses into one central facility serving an entire market area. Maister pointed out that others (e.g., Brown, 1962; Heskett, Ivie, & Glaskowsy, 1974; Starr & Miller, 1962); had previously discussed the impact of consolidating warehouses, but had not offered mathematical verification of this impact. As discussed shortly, Maister articulated that the “total inventory in a system is proportional to the square root of the number of locations at which a product is stocked”.

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