Forecasting intermittent demand using the cox process

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dc.authorscopusid56576053300
dc.authorscopusid6508013954
dc.authorscopusid36018188000
dc.contributor.authorKaya, Gamze Ogcu
dc.contributor.authorDemirel, Ömer Faruk
dc.contributor.authorBeyca, Ömer Faruk
dc.contributor.otherYönetim Bilimleri Fakültesi, İşletme Bölümü
dc.contributor.otherYönetim Bilimleri Fakültesi, İşletme Bölümü
dc.date.accessioned2023-04-18T22:13:04Z
dc.date.available2023-04-18T22:13:04Z
dc.date.issued2018
dc.departmentİHÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümü
dc.description.abstractIf a demand has infrequent demand occurrences and irregular demand sizes, then it is intermittent demand. Generally, intermittent demand appears at random, with many time periods having no demand. Owing to peculiar characteristics of intermittent demand, demand forecasting for intermittent demand is especially difficult. There are ad hoc methods developed for intermittent demand forecasting. Since Cox process has shown superior performance for intermittent demand forecasting, we studied forecasting intermittent demand using Cox process in this study. We develop a new method for estimating Cox process intensity which is called Reversed Leven and Segerstedt (RLS) method. Moreover, we propose a novel method which is a Wavelet Transform and Reversed Leven and Segerstedt conjunction model for intermittent demand forecasting using Cox process. Using real data set of 500 kinds of spare parts from an aviation sector company in Turkey, we show that our method produces more accurate forecasts than other intermittent demand forecasting methods using Cox process. The comparison approach has a lead time perspective which is based on lead time ahead demand forecast and lead time demand forecast errors.
dc.identifier.citationKaya, G. Ö., Demirel, Ö. F. ve Beyca, Ö. F. (2018). Forecasting intermittent demand using the cox process. Journal of Multiple-Valued Logic and Soft Computing, 31(5-6), 425 - 441.
dc.identifier.endpage441
dc.identifier.issn1542-3980
dc.identifier.issue5.Haz
dc.identifier.scopus2-s2.0-85060674040
dc.identifier.scopusqualityQ2
dc.identifier.startpage425
dc.identifier.urihttps://hdl.handle.net/20.500.12154/2212
dc.identifier.volume31
dc.indekslendigikaynakScopus
dc.institutionauthorDemirel, Ömer Faruk
dc.language.isoen
dc.publisherOld City Publishing
dc.relation.ispartofJournal of Multiple-Valued Logic and Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCox process
dc.subjectForecasting
dc.subjectIntermittent Demand
dc.subjectLead Time Demand
dc.subjectLeven and Segerstedt
dc.subjectWavelet Transform
dc.subjectWavelet Transforms
dc.subjectWavelet Transform
dc.subjectCox Process
dc.subjectDemand Forecast
dc.subjectDemand Forecasting
dc.subjectIntermittent Demand
dc.subjectIrregular Demand
dc.subjectLead Time Demands
dc.subjectLeven and Segerstedt
dc.subjectForecasting
dc.titleForecasting intermittent demand using the cox process
dc.typeArticle
dspace.entity.typePublication
relation.isOrgUnitOfPublicationc9253b76-6094-4836-ac99-2fcd5392d68f
relation.isOrgUnitOfPublication.latestForDiscoveryc9253b76-6094-4836-ac99-2fcd5392d68f

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