Enhanced manufacturing storage management using data mining prediction techniques Article Swipe
Antonio Luque
,
F. Aguayo
,
Juan Ramón Lama-Ruiz
,
Eduardo González-Regalado
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1016/j.promfg.2017.09.166
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1016/j.promfg.2017.09.166
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. \nFor 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. \nTime 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. \nAs an alternative, in this paper it is proposed to use prediction techniques based on the data mining realm. \nThe use of consumption prediction algorithms clearly increases the storage management efficiency. The predictors based on data mining can offer enhanced solutions in many cases.
Related Topics
Concepts
Autoregressive integrated moving average
Computer science
Data mining
Key (lock)
Process (computing)
Task (project management)
Time series
Component (thermodynamics)
Consumption (sociology)
Engineering
Machine learning
Operating system
Systems engineering
Social science
Physics
Computer security
Sociology
Thermodynamics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.promfg.2017.09.166
- OA Status
- diamond
- Cited By
- 2
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2761369057
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2761369057Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.promfg.2017.09.166Digital Object Identifier
- Title
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Enhanced manufacturing storage management using data mining prediction techniquesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-01-01Full publication date if available
- Authors
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Antonio Luque, F. Aguayo, Juan Ramón Lama-Ruiz, Eduardo González-RegaladoList of authors in order
- Landing page
-
https://doi.org/10.1016/j.promfg.2017.09.166Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.promfg.2017.09.166Direct OA link when available
- Concepts
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Autoregressive integrated moving average, Computer science, Data mining, Key (lock), Process (computing), Task (project management), Time series, Component (thermodynamics), Consumption (sociology), Engineering, Machine learning, Operating system, Systems engineering, Social science, Physics, Computer security, Sociology, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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8Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Procedia Manufacturing |
| primary_location.landing_page_url | https://doi.org/10.1016/j.promfg.2017.09.166 |
| publication_date | 2017-01-01 |
| publication_year | 2017 |
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