An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm
A high efficient Supply Chain （SC） would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency and high quality of overall level of services. So it is important to research various methods, performance indicator systems and technol...
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| Veröffentlicht in: | Journal of Bionic Engineering 2013-07, Vol.10 (3), p.383-395 |
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| creator | Fan, Xuemei Zhang, Shujun Wang, Longzhao Yang, Yinsheng Hapeshi, Kevin |
| description | A high efficient Supply Chain （SC） would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency and high quality of overall level of services. So it is important to research various methods, performance indicator systems and technology for evaluating, monitoring, predicting and optimizing the performance of a SC. In this paper, the existing performance indicator systems and methods are discussed and evaluated. Various nature-inspired algorithms are reviewed and their applications for SC Performance Evaluation （PE） are discussed. Then, a model is proposed and developed using 5 Dimensional Balanced Scorecard （5DBSC） and LMBP （Levenberg-Marquardt Back Propagation） neural network for SC PE. A program is written using Matlab tool box to implement the model based on the practical values of the 14 indicators of 5DBSC of a given previous period. This model can be used to evaluate, predict and optimize the performance of a SC. The analysis results of a case study of a company show that the proposed model is valid, reliable and effective. The convergence speed is faster than that in the previous work. |
| doi_str_mv | 10.1016/S1672-6529(13)60234-6 |
| format | Article |
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So it is important to research various methods, performance indicator systems and technology for evaluating, monitoring, predicting and optimizing the performance of a SC. In this paper, the existing performance indicator systems and methods are discussed and evaluated. Various nature-inspired algorithms are reviewed and their applications for SC Performance Evaluation （PE） are discussed. Then, a model is proposed and developed using 5 Dimensional Balanced Scorecard （5DBSC） and LMBP （Levenberg-Marquardt Back Propagation） neural network for SC PE. A program is written using Matlab tool box to implement the model based on the practical values of the 14 indicators of 5DBSC of a given previous period. This model can be used to evaluate, predict and optimize the performance of a SC. The analysis results of a case study of a company show that the proposed model is valid, reliable and effective. The convergence speed is faster than that in the previous work.</description><identifier>ISSN: 1672-6529</identifier><identifier>EISSN: 2543-2141</identifier><identifier>DOI: 10.1016/S1672-6529(13)60234-6</identifier><language>eng</language><publisher>Singapore: Elsevier Ltd</publisher><subject>5DBSC ; Algorithms ; Artificial Intelligence ; Biochemical Engineering ; Bioinformatics ; Biomaterials ; Biomedical Engineering and Bioengineering ; Biomedical Engineering/Biotechnology ; bionics ; BP神经网络算法 ; Business performance management ; Case studies ; Convergence ; Engineering ; Indicators ; LMBP neural network ; Logistics ; Mathematical models ; Matlab ; Matlab工具箱 ; Neural networks ; performance evaluation ; supply chain ; Supply chains ; 优化性能 ; 供应链 ; 性能指标体系 ; 技术评估 ; 绩效评价 ; 评价模型</subject><ispartof>Journal of Bionic Engineering, 2013-07, Vol.10 (3), p.383-395</ispartof><rights>2013 Jilin University</rights><rights>Jilin University 2013</rights><rights>COPYRIGHT 2013 Springer</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c520t-74ca40fc29b4198ceb2c384fff08cefbebc5235d659045a50ec20ce867a00e83</citedby><cites>FETCH-LOGICAL-c520t-74ca40fc29b4198ceb2c384fff08cefbebc5235d659045a50ec20ce867a00e83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/87903X/87903X.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1016/S1672-6529(13)60234-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1672652913602346$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,41464,42533,51294,65534</link.rule.ids></links><search><creatorcontrib>Fan, Xuemei</creatorcontrib><creatorcontrib>Zhang, Shujun</creatorcontrib><creatorcontrib>Wang, Longzhao</creatorcontrib><creatorcontrib>Yang, Yinsheng</creatorcontrib><creatorcontrib>Hapeshi, Kevin</creatorcontrib><title>An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm</title><title>Journal of Bionic Engineering</title><addtitle>J Bionic Eng</addtitle><addtitle>Journal of Bionics Engineering</addtitle><description>A high efficient Supply Chain （SC） would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency and high quality of overall level of services. 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Zhang, Shujun ; Wang, Longzhao ; Yang, Yinsheng ; Hapeshi, Kevin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c520t-74ca40fc29b4198ceb2c384fff08cefbebc5235d659045a50ec20ce867a00e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>5DBSC</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biochemical Engineering</topic><topic>Bioinformatics</topic><topic>Biomaterials</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedical Engineering/Biotechnology</topic><topic>bionics</topic><topic>BP神经网络算法</topic><topic>Business performance management</topic><topic>Case studies</topic><topic>Convergence</topic><topic>Engineering</topic><topic>Indicators</topic><topic>LMBP neural network</topic><topic>Logistics</topic><topic>Mathematical models</topic><topic>Matlab</topic><topic>Matlab工具箱</topic><topic>Neural networks</topic><topic>performance evaluation</topic><topic>supply chain</topic><topic>Supply chains</topic><topic>优化性能</topic><topic>供应链</topic><topic>性能指标体系</topic><topic>技术评估</topic><topic>绩效评价</topic><topic>评价模型</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Xuemei</creatorcontrib><creatorcontrib>Zhang, Shujun</creatorcontrib><creatorcontrib>Wang, Longzhao</creatorcontrib><creatorcontrib>Yang, Yinsheng</creatorcontrib><creatorcontrib>Hapeshi, Kevin</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-自然科学</collection><collection>中文科技期刊数据库-自然科学-生物科学</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of Bionic Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fan, Xuemei</au><au>Zhang, Shujun</au><au>Wang, Longzhao</au><au>Yang, Yinsheng</au><au>Hapeshi, Kevin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm</atitle><jtitle>Journal of Bionic Engineering</jtitle><stitle>J Bionic Eng</stitle><addtitle>Journal of Bionics Engineering</addtitle><date>2013-07-01</date><risdate>2013</risdate><volume>10</volume><issue>3</issue><spage>383</spage><epage>395</epage><pages>383-395</pages><issn>1672-6529</issn><eissn>2543-2141</eissn><abstract>A high efficient Supply Chain （SC） would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency and high quality of overall level of services. So it is important to research various methods, performance indicator systems and technology for evaluating, monitoring, predicting and optimizing the performance of a SC. In this paper, the existing performance indicator systems and methods are discussed and evaluated. Various nature-inspired algorithms are reviewed and their applications for SC Performance Evaluation （PE） are discussed. Then, a model is proposed and developed using 5 Dimensional Balanced Scorecard （5DBSC） and LMBP （Levenberg-Marquardt Back Propagation） neural network for SC PE. A program is written using Matlab tool box to implement the model based on the practical values of the 14 indicators of 5DBSC of a given previous period. This model can be used to evaluate, predict and optimize the performance of a SC. The analysis results of a case study of a company show that the proposed model is valid, reliable and effective. 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| subjects | 5DBSC Algorithms Artificial Intelligence Biochemical Engineering Bioinformatics Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology bionics BP神经网络算法 Business performance management Case studies Convergence Engineering Indicators LMBP neural network Logistics Mathematical models Matlab Matlab工具箱 Neural networks performance evaluation supply chain Supply chains 优化性能 供应链 性能指标体系 技术评估 绩效评价 评价模型 |
| title | An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm |
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