Query Performance Tuning in Supply Chain Analytics

Data explosion with knowledge shortage is becoming increasingly prominent. By utilizing business intelligence technology, supply chain analytics turns data into business insights and optimizes supply chain management decisions. Firstly, this paper describes the levels of Business Intelligence analytics, and formulates the architecture of supply chain analytics topics, then explains the analytics details of each topic. Furthermore, as OLAP is the most important decision support analysis tools of which query performance directly impacts the quality of analytics system end user experience, this paper proposes a variety of tuning technologies to accelerate query performance, including optimizing design of dimension, table aggregations, partitions, column store and tuning server resources technologies etc. A use scenario shows performance can be dramatically improved by dropping the processing time from previous 6-8 seconds to less than 0.1 seconds when aggregating 20+ million business transaction records.