The business of online retailing is growing rapidly. According to the U.S. Department of Commerce, in the third quarter of 2018, the e-commerce sales in the U.S. market was $130.9 billion, a 14 percent increase from the third quarter of 2017.
U-M IOE’s Professor Xiuli Chao and Assistant Professor Ruiwei Jiang, together with Associate Professor Stefanus Jasin from the U-M Ross School of Business, propose to develop a data-driven optimization scheme, which uses historical sales data for demand estimation.
“We aim to demonstrate that the solution of our optimization scheme is easy to compute and implement, and it is asymptotically optimal — meaning the scheme will increase in accuracy as the available data increases,” said Chao.
"We aim to demonstrate that the solution of our optimization scheme is easy to compute and implement, and it is asymptotically optimal — meaning the scheme will increase in accuracy as the available data increases."Xiuli Chao, Professor, U-M Industrial & Operations Engineering
Online optimization for e-commerce has huge potential to increase revenue for online businesses while also lowering the cost of products for consumers and improving the personalization experience of online browsing.
“The results of this project will lead to algorithms for better pricing decisions, improved revenues, and increased customer satisfaction and social welfare.”
Chao joined U-M IOE in 2007. His research interests include queueing, scheduling, financial engineering, inventory and supply chain management, and online optimization.
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