A Modified Lotka-Volterra Model for Diffusion and Substitution of Multigeneration DRAM Processing Technologies

dc.citation.epage0en_US
dc.citation.spage0en_US
dc.citation.woscount0en_US
dc.contributor.authorHung, Hui-Chihen_US
dc.contributor.authorChiu, Yu-Chihen_US
dc.contributor.authorWu, Muh-Cherngen_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.date.accessioned2019-04-03T06:44:13Z
dc.date.available2019-04-03T06:44:13Z
dc.date.issued2017-01-01en_US
dc.description.abstractWe attempt to develop an effective forecasting model for the diffusion and substitution of multigeneration Dynamic Random Access Memory (DRAM) processing technologies. We consider market share data and propose a modified Lotka-Volterramodel, in which an additional constraint on the summation of market share is introduced. The mean absolute error is used to measure the accuracy of our market share predictions. Market share data in DRAM industries from quarter one (Q1) of 2005 to 2013 Q4 is collected to validate the prediction accuracy. Our model significantly outperforms other benchmark forecasting models of both revenue and market share data, including the Bass and Lotka-Volterra models. Compared to prior studies on forecasting the diffusion and substitution of multigeneration technologies, our model has two new perspectives: (1) allowing undetermined number of multigeneration technologies and inconsecutive adoption of new technologies and (2) requiring less data for forecasting newborn technologies.en_US
dc.identifier.doi10.1155/2017/3038203en_US
dc.identifier.issn1024-123Xen_US
dc.identifier.journalMATHEMATICAL PROBLEMS IN ENGINEERINGen_US
dc.identifier.urihttp://dx.doi.org/10.1155/2017/3038203en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/145540
dc.identifier.wosnumberWOS:000401854300001en_US
dc.language.isoen_USen_US
dc.titleA Modified Lotka-Volterra Model for Diffusion and Substitution of Multigeneration DRAM Processing Technologiesen_US
dc.typeArticleen_US

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