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Utilizing decision forest regression machine learning algorithm to predict filling line utilization for optimal maintenance scheduling

dc.contributor.authorSmith, Laurenen_ZA
dc.contributor.authorZastron, Mauritzen_ZA
dc.contributor.authorVlok, P. J.en_ZA
dc.date.accessioned2020-03-03T07:03:54Z
dc.date.available2020-03-03T07:03:54Z
dc.date.issued2018
dc.identifier.citationSmith, L., Zastron, M. & Vlok, P. J. 2018. Utilizing decision forest regression machine learning algorithm to predict filling line utilization for optimal maintenance scheduling. In SAIIE29 Proceedings, 24-26 October 2018, Spier, Stellenbosch, South Africa
dc.identifier.urihttp://hdl.handle.net/10019.1/107567
dc.descriptionCITATION: Smith, L., Zastron, M. & Vlok, P. J. 2018. Utilizing decision forest regression machine learning algorithm to predict filling line utilization for optimal maintenance scheduling. In SAIIE29 Proceedings, 24-26 October 2018, Spier, Stellenbosch, South Africa.
dc.descriptionThe original publication is available at https://conferences.sun.ac.za/index.php/saiie29/saiie29/schedConf/presentations
dc.description.abstractSmall margins within the packaging industry mean financial success in this field relies on high equipment availability. To achieve this high equipment availability, maintenance schedules should be carefully planned to minimize downtime. A key component of maintenance schedule planning is predicting equipment utilization. This can prove very difficult as there are many variables such as market demand, seasonality of products, capability and diversity of equipment, and inherent reliability, to name a few. Even some of the leading players in the packaging industry treat the complexities and chaos involved with predicting equipment utilization as a topic best avoided. Current approaches to this problem range from no prediction at all to only a simple linear extrapolation. This paper investigates the merits of using machine learning algorithms to predict equipment utilization in the packaging industry with the aim of optimizing maintenance schedules. Machine learning entails pattern recognition of past data and inclusion of pertinent variables in the present to forecast behaviour. This paper begins with a brief literature review of the field before using data, obtained from a multinational packaging company, to test some of the most promising methods of machine learning in a case study.en_ZA
dc.description.urihttps://conferences.sun.ac.za/index.php/saiie29/saiie29/paper/view/3552
dc.format.extent10 pages
dc.language.isoen_ZAen_ZA
dc.publisherSouth African Institute for Industrial Engineering
dc.subjectPackaging industry -- Managementen_ZA
dc.subjectMultivariate analysisen_ZA
dc.subjectMachine learningen_ZA
dc.subjectMaintenance schedulingen_ZA
dc.subjectPackaging machinery -- Maintenance and repairen_ZA
dc.titleUtilizing decision forest regression machine learning algorithm to predict filling line utilization for optimal maintenance schedulingen_ZA
dc.typeConference Paperen_ZA
dc.description.versionPublisher's version
dc.rights.holderAuthors retain copyright


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