A framework For decision automation in operations

Amazon's leadership has set a goal to achieve a highly automated warehouse by 2020. Automation involves two challenges, physical movement and decision-making. Decision making often increase variability, defects and consumes time. The goal of this paper is to offer a structured framework to design a machine-learning based solution to automate decisions. It will leverage recorded decisions made by Amazon's employees every minute. Using the stow problem as an example, the paper will showcase how captured decision data can be a source of knowledge about processes and products. The stow case is a perfect example for non-trivial continuous decision making process. The workers' decisions are recorded but to this day Amazon does not leverage them for learning purposes. Our framework will offer a few outcomes: 1) High accuracy decision model, improving forecasting abilities from 59% to 95% 2) Self healing mechanism and learning system 3) Coaching and training tool. Using only the first outcome, cost savings are estimated to be over $25M annually across the US network. This paper will also discuss the psychological implication for decision automation while keeping manual work as part of the process.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Ben Nun, Shai

Advisor dc:contributor.advisor

• Berthold Horn and Karen Zheng.

• Electrical Engineering and Computer Science.
• Sloan School of Management.
• 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

http://hdl.handle.net/1721.1/111863

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111863