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dc.contributor.authorSamattapapong, Nara
dc.contributor.authorAfzulpurkar, Nitin
dc.date.accessioned2016-07-19T08:58:33Z
dc.date.available2016-07-19T08:58:33Z
dc.date.issued2016-06
dc.identifier.citationSamattapapong, Nara; Afzulpurkar, Nitin. A production throughput forecasting system in an automated hard disk drive test operation using GRNN. "Journal of Industrial Engineering and Management", Juny 2016, vol. 9, núm. 2, p. 330-358.
dc.identifier.issn2013-0953
dc.identifier.urihttp://hdl.handle.net/2117/88882
dc.description.abstractPurpose: The goal of this paper is to develop a pragmatic system of a production throughput forecasting system for an automated test operation in a hard drive manufacturing plant. The accurate forecasting result is necessary for the management team to response to any changes in the production processes and the resources allocations. Design/methodology/approach: In this study, we design a production throughput forecasting system in an automated test operation in hard drive manufacturing plant. The proposed system consists of three main stages. In the first stage, a mutual information method was adopted for selecting the relevant inputs into the forecasting model. In the second stage, a generalized regression neural network (GRNN) was implemented in the forecasting model development phase. Finally, forecasting accuracy was improved by searching the optimal smoothing parameter which selected from comparisons result among three optimization algorithms: particle swarm optimization (PSO), unrestricted search optimization (USO) and interval halving optimization (IHO). Findings: The experimental result shows that (1) the developed production throughput forecasting system using GRNN is able to provide forecasted results close to actual values, and to projected the future trends of production throughput in an automated hard disk drive test operation; (2) IHO algorithm performed as appropriate optimization method better than the other two algorithms. (3) Compared with current forecasting system in manufacturing, the results show that the proposed system’s performance is superior to the current system in prediction accuracy and suitable for real-world application. Originality/value: The production throughput volume is a key performance index of hard disk drive manufacturing systems that need to be forecast. The production throughput forecasting result is useful information for management team to respond to any changes in production processes and resources allocation. However, a practical forecasting system for production throughput has not been described in detail yet. The experiments were conducted on a real data set from the final testing operation of hard disk drive manufacturing factory by using Visual Basic Application on Microsoft Excel© to develop preliminary forecasting system for testing and verification process. The experimental result shows that the proposed model is superior to the performance of the current forecasting system.
dc.format.extent29 p.
dc.language.isoeng
dc.publisherOmnia Science
dc.rightsAttribution-NonCommercial 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions
dc.subject.lcshProduction control
dc.subject.lcshProduction planning
dc.subject.otherForecasting system
dc.subject.otherGeneral regression neural network
dc.subject.otherProduction throughput
dc.subject.otherInterval halving
dc.subject.otherHard disk drive manufacturing
dc.titleA production throughput forecasting system in an automated hard disk drive test operation using GRNN
dc.typeArticle
dc.subject.lemacProducció -- Control
dc.subject.lemacProducció -- Planificació
dc.identifier.doi10.3926/jiem.1464
dc.identifier.dlB-28744-2008
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
upcommons.citation.publishedtrue
upcommons.citation.publicationNameJournal of Industrial Engineering and Management
upcommons.citation.volume9
upcommons.citation.number2
upcommons.citation.startingPage330
upcommons.citation.endingPage358


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial 3.0 Spain