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Business Operations

and Analytics

  • Research
    • Applications
      • Business Operations and Analytics
      • Energy and Sustainability
      • Health and Human Safety
      • Mobility and Transportation Networks
    • Methodologies
      • Data Analytics
      • Human Systems Integration
      • Optimization
      • Stochastic Systems
    • Labs & Facilities
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home_outline/Research/Applications/Business Operations and Analytics

All sectors of the modern economy rely on fundamental quantitative tools for analysis, prediction, and optimization, to leverage data for the purposes of improving decision making. These tools allow companies and not-for-profit organizations to do more with limited resources. This gives businesses competitive advantages in the marketplace by lowering cost, increasing profits margins, and improving customer satisfaction.

This area includes:

Manufacturing and Service Systems: Designing better manufacturing systems and service operations by creating more efficient and effective facilities and processes that increase customer satisfaction and improve profit margins for businesses and not-for-profit organizations.

Supply Chain Management: Optimizing the efficiency of global supply chains to reduce overall cost and make them more robust to disruptions by managing production, distribution logistics, and inventory control to guarantee a reliable supply of goods at affordable prices to customers.

Scheduling: Developing resource allocation plans to improve the flow of materials and people in manufacturing and service systems to increase efficiencies and improve the quality of the customer experience.

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Helping people get back to work using deep learning in the occupational health system

Helping people get back to work using deep learning in the occupational health system

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University of Michigan researchers have produced a new prediction model using longitudinal information and deep learning to better predict the return to work time for people with occupational injuries.
Xiuli Chao receives funding from J&J to develop algorithms for continuous production process

Xiuli Chao receives funding from J&J to develop algorithms for continuous production process

December 10, 2019
U-M IOE’s Xiuli Chao receives funding from Johnson & Johnson for a project centered on developing optimization algorithms to improve continuous production process.
Xiuli Chao receives funding to improve the efficiencies of sharing economy

Xiuli Chao receives funding to improve the efficiencies of sharing economy

September 24, 2019
U-M IOE professor, Xiuli Chao, receives funding from Didi for a project focused on improving the efficiencies of sharing economy through enhanced matching and contract design.
Mitigating uncertainties in remote computer numerical control using data-driven transfer learning

Mitigating uncertainties in remote computer numerical control using data-driven transfer learning

September 17, 2019
U-M IOE’s Raed Al Kontar receives research funding from Cyber-physical Systems, a National Science Foundation program, for a project centered on the refinement of computer numerical control as a cloud service.
Jessie Yang receives funding from Dell for analysis of online user-generated data

Jessie Yang receives funding from Dell for analysis of online user-generated data

April 9, 2019
U-M IOE assistant professor, Jessie Yang, has received research funding from Dell Inc. for sentiment analysis of online user-generated data for business intelligence enrichment.
Xiuli Chao and Ruiwei Jiang receive MCubed funding for data-driven optimization of online retailing

Xiuli Chao and Ruiwei Jiang receive MCubed funding for data-driven optimization of online retailing

March 26, 2019
Two U-M Industrial and Operations Engineering (IOE) researchers have received MCubed funding from the University of Michigan for work on data-driven optimization for online retailing.

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