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  • 标题:Possibilities of Usage of Strategic Business Intelligence Systems Based on Databases in Agile Manufacturing
  • 本地全文:下载
  • 作者:Ahmet Uçaktürk ; Ahmet Uçaktürk ; Tülay Uçaktürk
  • 期刊名称:Procedia - Social and Behavioral Sciences
  • 印刷版ISSN:1877-0428
  • 出版年度:2015
  • 卷号:207
  • 页码:234-241
  • DOI:10.1016/j.sbspro.2015.10.092
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractOrganizations must have the ability of responding quickly to predictable and unpredictable changes in the market. Organizations’ ability to adapt to the fast changing conditions depending on the differing requirements of the customers, depend to a great extend on their ability to become agile. Since change and ambiguity always exist and always will in future, the necessity of the organizations to understand these changes in environment conditions quickly and to be able to respond gains importance every passing day. This requirement provides the wide spreading of agile manufacturing philosophy in organizations. Organizations, due to agile manufacturing, will be able to have the chance of turning the ambiguities in the environment to possibilities. Today, accessing correct and more detailed information became possible however another problem has arisen. This problem is the management of large numerical data stacks and their becoming meaningful. The work of turning raw data into information or making them meaningful can be made with data warehouse and business intelligence tools (data mining, inquiry-reporting, OLAP). At the stage of quickly accessing and using data which will form the basis of agile manufacturing work, creation of data warehouses and using them by integrating into organization information system will be useful. In this work, information is tried to be provided with the literature scanning about the possibility of usage of data warehouse and data base architecture during agile manufacturing process and with business intelligence tools.
  • 关键词:Agile Manufacturing Management;Business Intelligence;Data Warehouse;Data Mining;On-Line Analytical Processing (OLAP)
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