Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Chih-Hsuanen_US
dc.contributor.authorChuang, Jia-Jienen_US
dc.date.accessioned2016-03-28T00:04:26Z-
dc.date.available2016-03-28T00:04:26Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn0167-9236en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.dss.2015.10.004en_US
dc.identifier.urihttp://hdl.handle.net/11536/129680-
dc.description.abstractSolar energy is a natural, clean, and inexhaustible resource to help the earth solve its energy crisis and reduce the air pollution caused by coal, nuclear, and gas electricity plants. Solar heating, solar photovoltaics, and solar thermal electricity can contribute to solving some of the most urgent problems the world faces. Despite the anti-dumping tariff and the anti-subsidy policy negatively impacting solar companies in Taiwan and China, solar cells are facing strong recovery of market growth in 2015. However, fierce competition in terms of cost and quality has resulted in a series of corporate acquisitions or bankruptcies since 2012. Inspired by the concept of business intelligence, a balanced scorecard (BSC) based framework is proposed to address the following issues: (1) How to identify key performance indicators (KPIs) that influence outcomes? (2) How to guide the lagging group to benchmark with the leading group (business diagnosis)? (3) How to adjust the significant KPIs to improve outcomes (what-if simulation)? In particular, classification and regression tree (fused with logistic regression) and back propagation neural network (fused with multiple regression) are adopted to provide managerial insights for worldwide solar companies. Furthermore, support vector machine (regression) is used to justify the validity of the presented framework. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectBalanced scorecarden_US
dc.subjectBusiness intelligenceen_US
dc.subjectBenchmarkingen_US
dc.subjectSimulationen_US
dc.subjectSolar industryen_US
dc.titleIntegrating decision tree with back propagation network to conduct business diagnosis and performance simulation for solar companiesen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.dss.2015.10.004en_US
dc.identifier.journalDECISION SUPPORT SYSTEMSen_US
dc.citation.volume81en_US
dc.citation.spage12en_US
dc.citation.epage19en_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:000369203200002en_US
dc.citation.woscount0en_US
Appears in Collections:Articles