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dc.contributor.authorHsu, HMen_US
dc.contributor.authorWang, WPen_US
dc.date.accessioned2014-12-08T15:44:53Z-
dc.date.available2014-12-08T15:44:53Z-
dc.date.issued2000-09-01en_US
dc.identifier.issn1072-4761en_US
dc.identifier.urihttp://hdl.handle.net/11536/30302-
dc.description.abstractHigh demand uncertainty has been a major challenge for production planning in assemble-to-order (ATO) environments. Regulating dealers' demand forecasts, determining appropriate safety stock levels and deciding required capacity levels are some of the typical problems solved by ATO firms. The main objective in this study is therefore to construct a mathematical model to deal with the aforementioned managerial, problems under the consideration of product life cycle. According to the various cost and demand characteristics in different phases of a product life cycle, production managers may apply the proposed model to find a near optimal solution set of regulation factors for dealers' demand forecasts, appropriate levels of safety stocks and the number of key machines. Finally, an example is given to illustrate our model. Significance: This paper presents a linear programming model that integrates the activities of forecasting adjustment, material preparation and key machine quantity determination in an ATO environment. The proposed model reflects the phenomena of a product life cycle passing through the different appearances of the demand and cost in distinct phases.en_US
dc.language.isoen_USen_US
dc.subjectassemble-to-orderen_US
dc.subjectdemand forecasten_US
dc.subjectsafety stocken_US
dc.subjectcapacityen_US
dc.subjectproduct life cycleen_US
dc.titleModeling demand driven with forecast regulation, safety stock setting and capacity analysisen_US
dc.typeArticleen_US
dc.identifier.journalINTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICEen_US
dc.citation.volume7en_US
dc.citation.issue3en_US
dc.citation.spage195en_US
dc.citation.epage201en_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.identifier.wosnumberWOS:000087878900002-
dc.citation.woscount2-
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