Performance modeling of human-machine interfaces using machine learning

As the popularity of online retail expands, world-class electronic commerce (e-commerce) businesses are increasingly adopting collaborative robotics and Internet of Things (IoT) technologies to enhance fulfillment efficiency and operational advantage. E-commerce giants like Alibaba and Amazon are known to have smart warehouses staffed by both machines and human operators. The robotics systems specialize in transporting and maneuvering heavy shelves of goods to and from operators. Operators are left to higher-level cognitive tasks needed to process goods such as identification and complex manipulation of individual objects. Achieving high system throughput in these systems require harmonized interaction between humans and machines. The robotics systems must minimize time that operators are waiting for new work (idle time) and operators need to minimize time processing items (takt time). Over time, these systems will naturally generate extensive amounts of data. Our research provides insights into both using this data to design a machine-learning (ML) model of takt time, as well as exploring methods of interpreting insights from such a model. We start by presenting our iterative approach to developing a ML model that predicts the average takt of a group of operators at hourly intervals. Our final XGBoost model reached an out-of-sample performance of 4.01% mean absolute percent error (MAPE) using over 250,000 hours of historic data across multiple warehouses around the world. Our research will share methods to cross-examine and interpret the relationships learned by the model for business value. This can allow organizations to effectively quantify system trade-offs as well as identify root-causes of takt performance deviations. Finally, we will discuss the implications of our empirical findings.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Wu, Anjian,M.B.A.Sloan School of Management.

Advisor dc:contributor.advisor

• Randall Davis and Yanchong Zheng.

• Sloan School of Management.
• Electrical Engineering and Computer Science.
• Leaders for Global Operations Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

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/1721.1/122599

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122599