Research On Business Intelligence Of Manufacturing Based On Multidimensional Data Model

Manufacturing has accumulated large amounts of data in the application of ERP, CRM, SCM and other systems, which provides a solid data foundation and a valuable asset for manufacturing implementing business intelligence systems.How to find out the rule and model from the mass of data, access to business information, helping the manufacturing established a unified analysis platform, that has been a very fashionable topic for discussion.Business Intelligence describes a series of concepts and methods, which changes the data that stored in information system into a useful information technology, and is also refining and re-integrating process on the basis of a mass of information. And it solved analysis inadequate and low level of decision-making dilemma in the traditional information systems of manufacturing. Manufacturing Business Intelligence improved and enhanced the integrated management decision-making level of our manufacturing and the ability to withstand business risk. At the same time, it enhanced the overall capability of manufacturing information management, and plays a dominant position in the fierce global market competition. With the rapid development of business intelligence, manufacturing information system is experiencing the evolution of "MISâ†'ERPâ†'>BI".This paper studies the theory and technology of business intelligence, aimed at the characteristics of manufacturing value chain and the technology of manufacturing process mining, a process-oriented Business Intelligence model of manufacturing was established and OLAM was introduced in data analyzing. According to this model, this paper discusses the basic technology of business intelligence, researched and developed an OLAP client tool based on Microsoft Analysis Services, and then analyzed multi-dimensional database mining association rules and find out the lack of existing methods, proposed a association rule mining solutions based on user-oriented the OLAM. Through the BCTree data structure which is used to stored multi-dimensional data sets count, solve the association rules analysis under different support, reducing I/O overhead relative to the direct mining of multidimensional data sets, improved the efficiency of mining. Meanwhile, under the guidance of OLAP tools, users can analyze the dimension of interest reduced the number of mining dimensions, increase the target of system. Finally, the solution was used in the production data in this paper. Show that OLAM is used in Business Intelligence of manufacturing, which can improve the knowledge mining capacity.