{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/111585"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/111585","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Gamification as a means of improving performance in human operator processes","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_html":"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_has_math":false,"creators":["Small, Aaron Alexander"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Leaders for Global Operations Program at MIT","school":null,"contributors":[],"advisors":["Bruce Cameron and Steven J. Spear."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:21:50Z","subjects":["Sloan School of Management.","Institute for Data, Systems, and Society.","Engineering Systems Division.","Leaders for Global Operations Program."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/111585","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Bruce Cameron and Steven J. Spear."]},{"key":"dc:contributor.department","label":"Department","values":["Leaders for Global Operations Program at MIT","Massachusetts Institute of Technology. Engineering Systems Division","Massachusetts Institute of Technology. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/111585"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.B.A.","S.M. in Engineering Systems"]},{"key":"dc:title","label":"Title","values":["Gamification as a means of improving performance in human operator processes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bruce Cameron and Steven J. Spear."],"dc:contributor.department":["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"],"dc:contributor.other":["Leaders for Global Operations Program."],"dc:creator":["Small, Aaron Alexander"],"dc:date.accessioned":["2017-09-18T14:39:40Z"],"dc:date.available":["2017-09-18T14:39:40Z"],"dc:date.issued":["2017"],"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.","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)."],"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."],"dc:description.degree":["M.B.A.","S.M. in Engineering Systems"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/111585"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Sloan School of Management.","Institute for Data, Systems, and Society.","Engineering Systems Division.","Leaders for Global Operations Program."],"dc:title":["Gamification as a means of improving performance in human operator processes"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:50Z"}