Human-machine teaming for intelligent demand planning

The second machine age is reshaping the way we work, do business, and collaborate. Today collaboration is switching from just among humans to between humans and machines. Mundane and repetitive tasks will be done by machines automatically, while humans can develop insights and make wise decisions supported by data streaming from intelligent machines. If and how different human-machine teaming decision-making structures would influence the organization's performance is important to understand, so that human-machine teaming capabilities could contribute the most to business outcomes. By using the augmented inverse propensity weight estimator method, this research empirically analyzes the average treatment effects of three different human-machine decision-making structures: Full human to AI delegation, Hybrid AI-Human with adequate human intervention, and Hybrid AI-Human with all steps of demand planning overrides.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Supply Chain Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Ma, Ye,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Maria Jesus Saenz Gil De Gomez.

• Supply Chain Management Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127102

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127102