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A framework For decision automation inoperations
by Nun Shai Ben
Institution: | MIT |
---|---|
Department: | |
Degree: | |
Year: | 2017 |
Keywords: | Electrical Engineering and ComputerScience.; Sloan School of Management.; Leaders for Global Operations Program. |
Posted: | 2/1/2018 12:00:00 AM |
Record ID: | 2190940 |
Full text PDF: | http://hdl.handle.net/1721.1/111863 |
Amazon's leadership has set a goal to achieve ahighly automated warehouse by 2020. Automation involves twochallenges, physical movement and decision-making. Decision makingoften increase variability, defects and consumes time. The goal ofthis paper is to offer a structured framework to design amachine-learning based solution to automate decisions. It willleverage recorded decisions made by Amazon's employees everyminute. Using the stow problem as an example, the paper willshowcase how captured decision data can be a source of knowledgeabout processes and products. The stow case is a perfect examplefor non-trivial continuous decision making process. The workers'decisions are recorded but to this day Amazon does not leveragethem for learning purposes. Our framework will offer a fewoutcomes: 1) High accuracy decision model, improving forecastingabilities from 59% to 95% 2) Self healing mechanism and learningsystem 3) Coaching and training tool. Using only the first outcome,cost savings are estimated to be over $25M annually across the USnetwork. This paper will also discuss the psychological implicationfor decision automation while keeping manual work as part of theprocess.Advisors/Committee Members: Berthold Horn, Karen Zheng (advisor).
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