{"title":"基于系统动力学和评价模型的港口企业研发系统优化","authors":"Yiming Shao, Jiayi Guo, Zejun Li, Wen Yan, Zhiyi Zhu","doi":"10.1117/12.2652356","DOIUrl":null,"url":null,"abstract":"Integrated data and analytics (D&A) systems are recognized for their ability to help companies ensure they have the right people, technology, and processes in place to manage, operate, use, and protect data resources. It is critical for port companies to develop the right strategy to refine the architecture of their D&A systems and integrate their data resources.In this study, system dynamics simulation, and principal component analysis are used to help seaport companies improve D&A systems and maintain their competitiveness in the future. First, starting from the three key parts of personnel, technology, and process, a 3zy comprehensive evaluation model is developed to evaluate the maturity of the D&A system. Second, we use Vensim to conduct simulations and build a seaport system dynamics model. Then, analyze the causal relationship between various influencing factors, and explore the influence of various factors on the port. Third, through the principal component analysis method, clustering multiple indicators to determine the overall skill level of the company and the degree of cooperation between people, technology, and processes has an important impact on the D&A system. Finally, a sensitivity analysis is carried out to calculate the degree of influence on the analysis indicators when the uncertain factors change. The conclusions obtained show the scalability of the D&A system maturity model in other ports and industries.","PeriodicalId":116712,"journal":{"name":"Frontiers of Traffic and Transportation Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of seaport companies' D&A systems based on system dynamics and evaluation models\",\"authors\":\"Yiming Shao, Jiayi Guo, Zejun Li, Wen Yan, Zhiyi Zhu\",\"doi\":\"10.1117/12.2652356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Integrated data and analytics (D&A) systems are recognized for their ability to help companies ensure they have the right people, technology, and processes in place to manage, operate, use, and protect data resources. It is critical for port companies to develop the right strategy to refine the architecture of their D&A systems and integrate their data resources.In this study, system dynamics simulation, and principal component analysis are used to help seaport companies improve D&A systems and maintain their competitiveness in the future. First, starting from the three key parts of personnel, technology, and process, a 3zy comprehensive evaluation model is developed to evaluate the maturity of the D&A system. Second, we use Vensim to conduct simulations and build a seaport system dynamics model. Then, analyze the causal relationship between various influencing factors, and explore the influence of various factors on the port. Third, through the principal component analysis method, clustering multiple indicators to determine the overall skill level of the company and the degree of cooperation between people, technology, and processes has an important impact on the D&A system. Finally, a sensitivity analysis is carried out to calculate the degree of influence on the analysis indicators when the uncertain factors change. The conclusions obtained show the scalability of the D&A system maturity model in other ports and industries.\",\"PeriodicalId\":116712,\"journal\":{\"name\":\"Frontiers of Traffic and Transportation Engineering\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers of Traffic and Transportation Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2652356\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Traffic and Transportation Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2652356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization of seaport companies' D&A systems based on system dynamics and evaluation models
Integrated data and analytics (D&A) systems are recognized for their ability to help companies ensure they have the right people, technology, and processes in place to manage, operate, use, and protect data resources. It is critical for port companies to develop the right strategy to refine the architecture of their D&A systems and integrate their data resources.In this study, system dynamics simulation, and principal component analysis are used to help seaport companies improve D&A systems and maintain their competitiveness in the future. First, starting from the three key parts of personnel, technology, and process, a 3zy comprehensive evaluation model is developed to evaluate the maturity of the D&A system. Second, we use Vensim to conduct simulations and build a seaport system dynamics model. Then, analyze the causal relationship between various influencing factors, and explore the influence of various factors on the port. Third, through the principal component analysis method, clustering multiple indicators to determine the overall skill level of the company and the degree of cooperation between people, technology, and processes has an important impact on the D&A system. Finally, a sensitivity analysis is carried out to calculate the degree of influence on the analysis indicators when the uncertain factors change. The conclusions obtained show the scalability of the D&A system maturity model in other ports and industries.