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2021 | OriginalPaper | Buchkapitel

7. Prioritizing Autonomous Supply—Comparing Selection by Marginal Analysis and Neural Nets

verfasst von : Gregory D. Sherman, Mitchell Mickan

Erschienen in: Data and Decision Sciences in Action 2

Verlag: Springer International Publishing

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Abstract

When managing inventory or supply systems, it is important to make good choices about which stock to prioritize over others. We can improve the overall availability of the supplied systems by making optimal choices on which inventory items should be allocated to meet demands. In this paper, we will show how machine learning algorithms can be used to prioritize inventory. The developed algorithms were tested on a real data set and the improvement in inventory allocation measured. Machine learning is a powerful technique for transforming inputs to outputs in order to best achieve a set goal. It has many applications in areas where there is an abundance of data, and where the resulting decisions can be measured. As such inventory management is a suitable area of application, in particular the prioritization of supply. Such an approach is even more relevant to those inventory models that represent autonomous processes. The models we are interested in are those relating to system availability and that use item backorder calculations. These models rely on a traditional prioritization approach known as marginal analysis, otherwise known as a process of marginal allocation using a greedy algorithm. Because marginal analysis does not take into account performance over time, nor complex relationships in data sets, there may be potential for a machine learning algorithm to provide better results if it can learn to exploit both temporal and relationship data. The benefit of such an improvement is the value of availability generated and cost savings made in the supply network.

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Literatur
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Zurück zum Zitat Sherbrooke CC (2004) Optimal inventory modeling of systems: multi-echelon techniques (Intl Series In Operations Research & Management Science) Kluwer Academic Publishers, Norwell, MA, USA Sherbrooke CC (2004) Optimal inventory modeling of systems: multi-echelon techniques (Intl Series In Operations Research & Management Science) Kluwer Academic Publishers, Norwell, MA, USA
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Zurück zum Zitat Svanberg K (2009a) On spare parts optimization. KTH, Stockholm Sweden Svanberg K (2009a) On spare parts optimization. KTH, Stockholm Sweden
4.
Zurück zum Zitat Svanberg K (2009b) “On marginal allocation” (MALLOC). KTH, Stockholm Sweden Svanberg K (2009b) “On marginal allocation” (MALLOC). KTH, Stockholm Sweden
Metadaten
Titel
Prioritizing Autonomous Supply—Comparing Selection by Marginal Analysis and Neural Nets
verfasst von
Gregory D. Sherman
Mitchell Mickan
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-60135-5_7