Role of industrial automation in terms of providing predictive maintenance in the transportation and logistics sector

The best practitioners of Spatial cluster analysis the whole of ideas, innovation, convey the practices and challenges, so be spread is very important, the logistics industry of Spatial cluster analysis, a unique interdisciplinary effort, epidemiologists apply the statistics. In this study, reviewed the scope of the retrieval spatial cluster analysis method employed by systematically individual level, a peer-reviewed journal database to study the data obtained from the address position or coordinates. Advanced Resource management (ARM) for logistics are widely used in such consumer transport monitoring. Because of its reduced instruction set, require effective few transistors smaller for the Integrated Circuit (IC) which is used to logistics monitoring in transportation. The existing system does not have proper result for less accuracy of logistics industry spatial cluster analysis and then less poor performance of spatial cluster analysis for prediction and classification. The proposed method gives correct result of more accuracy of logistics industry spatial cluster analysis and then high performance of spatial cluster analysis in this section using Random Support Vector System (RSVS) algorithm. The proposed system processes the data collection and analysis, then use the classification process, Advanced RISC Machines (ARM) Heterogeneous and Spatial cluster analysis and predict the result.