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Erschienen in: International Journal of Machine Learning and Cybernetics 5/2015

01.10.2015 | Original Article

Enhancing the management of shared inventory in the steel industry using RFID: an alternative to bar codes

verfasst von: Hector E. Talavera, Jerry Banks, Neale R. Smith, Leopoldo Eduardo Cárdenas-Barrón

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2015

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Abstract

An inventory problem of a company that specializes in cutting steel coils into rolls and sheets of steel is addressed in this paper. Rolls and sheets of steel require a high level of customization depending on the specifications by the end user. This causes complexity in the management of finished goods, mainly due to the wide variety of dimensions of the rolls and sheets that are in the storage area. Due to the great variety of products built for stock, it is not possible to define a specific location in the storage area to place a product. For a company located in México, recent data show that the percentage of obsolete inventory (items stored more than 90 days) is 7 %. The objective of this work is to reduce this percentage to 0 %. From a time study, it was learned that searching for finished inventory averaged 15.5 min. This activity consumes a large amount of the available forklift operator time. This means that in situations during which the forklift operator is pressed for time, it may be necessary to abandon the search for the correct finished products in the storage, and instead ship products of more recent production. Over time, the repetition of this event generates increased rates of obsolete inventory. The proposal to solve the problem was to reduce the search times through the implementation of a system that identifies inventory using RFID technology. This will improve the quality of the stored information, and therefore reduce the rate of obsolescence.

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Metadaten
Titel
Enhancing the management of shared inventory in the steel industry using RFID: an alternative to bar codes
verfasst von
Hector E. Talavera
Jerry Banks
Neale R. Smith
Leopoldo Eduardo Cárdenas-Barrón
Publikationsdatum
01.10.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0406-x

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