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Enhanced manufacturing storage management using data mining prediction techniques

 

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Author: Luque Sendra, Amalia
Aguayo González, Francisco
Lama-Ruiz, Juan Ramón
González-Regalado Montero, Eduardo
Department: Universidad de Sevilla. Departamento de Ingeniería del Diseño
Date: 2017
Published in: Procedia Manufacturing, 13, 956-963.
Document type: Article
Abstract: Performing an efficient storage management is a key issue for reducing costs in the manufacturing process. And the first step to accomplish this task is to have good estimations of the consumption of every storage component. For making accurate consumption estimations two main approaches are possible: using past utilization values (time series); and/or considering other external factors affecting the spending rates. Time series forecasting is the most common approach due to the fact that not always is clear the causes affecting consumption. Several classical methods have extensively been used, mainly ARIMA models. As an alternative, in this paper it is proposed to use prediction techniques based on the data mining realm. The use of consumption prediction algorithms clearly increases the storage management efficiency. The predictors based on data mining can offer enhanced solutions in many cases.
Cite: Luque Sendra, A., Aguayo González, F., Lama-Ruiz, J.R. y González-Regalado Montero, E. (2017). Enhanced manufacturing storage management using data mining prediction techniques. Procedia Manufacturing, 13, 956-963.
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URI: https://hdl.handle.net/11441/80473

DOI: 10.1016/j.promfg.2017.09.166

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