2026-09-18T19:36:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1115852022-01-28T15:06:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Bruce Cameron and Steven J. Spear.
Small, Aaron Alexander
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Sloan School of Management
2017-09-18T14:39:40Z
2017-09-18T14:39:40Z
2017
2017
http://hdl.handle.net/1721.1/111585
1003324651
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.
Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, in conjunction with the Leaders for Global Operations Program at MIT, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-42).
The Amazon fulfillment center network is the backbone of Amazon's e-commerce business. To achieve higher efficiency and lower cost, Amazon invests heavily in robotic technology. In some buildings, robots automatically store and retrieve shelving units, delivering them to operators who can interact with product at fixed stations. This has greatly increased throughput in buildings with the technology, while adding new constraints. During periods of peak demand, throughput is limited by the number of stations available and the average operator rate at those stations. This thesis examines how this constraint can be relieved by increasing average operator rate. Time-in-motion studies, video analysis, historical data analytics, and A/B testing suggest that modifications to the station design and operator process do not yield consistent sustainable improvements in performance. Learning curve analysis suggests that operator motivation and engagement are key factors in driving increased performance. Operators perform at a rate of roughly 239 units per hour stowed, with a standard deviation of 48 units per hour. However, operators demonstrate an average maximum sustainable rate of 283 units per hour with a standard deviation of 64 units per hour. Review of available research on motivation and engagement suggests that gamification methods could be cheaply and easily employed to increase operator motivation and engagement, and have realized 30% improvements in similar manufacturing settings. A cost analysis shows that a similar implementation at Amazon is likely to yield a high return on investment, with a base-case net present project value of over $100 million. The thesis concludes by describing a custom gamification system for Amazon that could efficiently alleviate the throughput bottleneck for one type of operator station. This approach is not only widely applicable across different process at Amazon, but also similar human operator processes in the manufacturing and warehouse settings.
by Aaron Alexander Small.
M.B.A.
S.M. in Engineering Systems
42 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Institute for Data, Systems, and Society.
Engineering Systems Division.
Leaders for Global Operations Program.
Gamification as a means of improving performance in human operator processes
Thesis
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Publication
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<Abstract>The Amazon fulfillment center network is the backbone of Amazon's e-commerce business. To achieve higher efficiency and lower cost, Amazon invests heavily in robotic technology. In some buildings, robots automatically store and retrieve shelving units, delivering them to operators who can interact with product at fixed stations. This has greatly increased throughput in buildings with the technology, while adding new constraints. During periods of peak demand, throughput is limited by the number of stations available and the average operator rate at those stations. This thesis examines how this constraint can be relieved by increasing average operator rate. Time-in-motion studies, video analysis, historical data analytics, and A/B testing suggest that modifications to the station design and operator process do not yield consistent sustainable improvements in performance. Learning curve analysis suggests that operator motivation and engagement are key factors in driving increased performance. Operators perform at a rate of roughly 239 units per hour stowed, with a standard deviation of 48 units per hour. However, operators demonstrate an average maximum sustainable rate of 283 units per hour with a standard deviation of 64 units per hour. Review of available research on motivation and engagement suggests that gamification methods could be cheaply and easily employed to increase operator motivation and engagement, and have realized 30% improvements in similar manufacturing settings. A cost analysis shows that a similar implementation at Amazon is likely to yield a high return on investment, with a base-case net present project value of over $100 million. The thesis concludes by describing a custom gamification system for Amazon that could efficiently alleviate the throughput bottleneck for one type of operator station. This approach is not only widely applicable across different process at Amazon, but also similar human operator processes in the manufacturing and warehouse settings.</Abstract>
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