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Supply chain optimization and inventory management optimization stand out as prominent concerns within the realm of modern business analytics. Surprisingly, while supply chain optimization has been in the spotlight for many years, its crucial inventory management component has often been neglected. Companies that have invested in supply chain optimization have typically allowed inventory management policies to be determined by outdated textbook models or even “managerial guesswork,” without consideration of employing advanced analytics technology. Recent discoveries have shown, however, that many organizations can save millions of dollars annually by applying state-of-the-art analytics to optimize inventories. Moreover, substantial gains in profits over and above those obtained from “good” analytics approaches result by using special models from a meta-analytics framework, which combines metaheuristics with analytics. We demonstrate this finding by an integrated meta-analytics platform that combines network optimization, netform modeling and simulation optimization for inventory management. We report computational tests that compare our meta-analytics approach to the status quo methodology customarily used for inventory management and to a recent innovation in inventory management reported to save over $90 million for a major U.S. retail firm. The results show that our meta-analytics approach provides dramatic improvements over both of these alternative approaches, yielding appreciably better levels of service and greater cost savings, and having broad implications for modern inventory management policies. Keynote II (Intl. conf. room, 2F, Venture Bldg.) Wednesday, August 17 08:30-09:10 Factor Space: A Mathematical Framework for New Paradigm Driven by Big Data Peizhuang Wang Professor, Intelligent Engineering and Math Institute, Liaoning Technical University, China