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Gamification as a means of improving performance in human operator processes

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dc.contributor.advisor Bruce Cameron and Steven J. Spear. en_US
dc.contributor.author Small, Aaron Alexander en_US
dc.contributor.other Leaders for Global Operations Program. en_US
dc.date.accessioned 2017-09-18T14:39:40Z
dc.date.available 2017-09-18T14:39:40Z
dc.date.copyright 2017 en_US
dc.date.issued 2017 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/111585
dc.description Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017. en_US
dc.description 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. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (pages 41-42). en_US
dc.description.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. en_US
dc.description.statementofresponsibility by Aaron Alexander Small. en_US
dc.format.extent 42 pages en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Sloan School of Management. en_US
dc.subject Institute for Data, Systems, and Society. en_US
dc.subject Engineering Systems Division. en_US
dc.subject Leaders for Global Operations Program. en_US
dc.title Gamification as a means of improving performance in human operator processes en_US
dc.type Thesis en_US
dc.description.degree M.B.A. en_US
dc.description.degree S.M. in Engineering Systems en_US
dc.contributor.department Sloan School of Management. en_US
dc.contributor.department Massachusetts Institute of Technology. Institute for Data, Systems, and Society. en_US
dc.contributor.department Massachusetts Institute of Technology. Engineering Systems Division. en_US
dc.contributor.department Leaders for Global Operations Program. en_US
dc.identifier.oclc 1003324651 en_US


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