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An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning
Joint Authors
Rodríguez-Padial, Néstor
Marín, Marta
Domingo, Rosario
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-12
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
The uncertainty of demand has led production systems to become increasingly complex; this can affect the availability of the machines and thus their maintenance.
Therefore, it is necessary to adequately manage the information that facilitates decision-making.
This paper presents a system for making decisions related to the design of customized maintenance plans in a production plant.
This paper addresses this tactical goal and aims to provide greater knowledge and better predictions by projecting reliable behavior in the medium-term, integrating this new functionality into classic Balance Scorecards, and making it possible to extend their current measuring function to a new aptitude: predicting evolution based on historical data.
In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms.
This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders.
In addition, this extension enables the prediction of indicator outcomes such as overall equipment efficiency and mean time to failure.
American Psychological Association (APA)
Rodríguez-Padial, Néstor& Marín, Marta& Domingo, Rosario. 2017. An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning. Complexity،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1142763
Modern Language Association (MLA)
Rodríguez-Padial, Néstor…[et al.]. An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning. Complexity No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1142763
American Medical Association (AMA)
Rodríguez-Padial, Néstor& Marín, Marta& Domingo, Rosario. An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning. Complexity. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1142763
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142763