{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/59185"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/59185","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A decision system for routing returned product to the optimal recovery channel","abstract":"Dell, a leading computer manufacturer, must deal with systems returned from its customers. Historically, it has refurbished most of its returned systems for resale on its Dell Outlet website. While this has provided high net recoveries (revenue less incurred costs) compared to its peers, Dell believes there is ample opportunity in cannibalizing some returned systems for the piece parts (i.e. \"teardown\"). These harvested piece parts can be used to service field systems, repair refurbished systems, or directly sold to customers as spare parts. Dell is concerned about ensuring an optimal disposition of system to teardown vs. direct resale. Written as part of research internship at Dell, this paper proposes, simulates, and evaluates a decision support system to address the question of disposition. The decision engines use historical data and statistics to estimate net recoveries in resale and forecasted demand to estimate net recoveries through teardown. Linear regressions were found to have poor power in predicting overall net recoveries; however, simple heuristics were found to identify likely low recovery systems. Overall, the implementation of the decision support system will drive improved net recoveries, with savings estimated to be greater than $1 million annually.","abstract_html":"Dell, a leading computer manufacturer, must deal with systems returned from its customers. Historically, it has refurbished most of its returned systems for resale on its Dell Outlet website. While this has provided high net recoveries (revenue less incurred costs) compared to its peers, Dell believes there is ample opportunity in cannibalizing some returned systems for the piece parts (i.e. "teardown"). These harvested piece parts can be used to service field systems, repair refurbished systems, or directly sold to customers as spare parts. Dell is concerned about ensuring an optimal disposition of system to teardown vs. direct resale. Written as part of research internship at Dell, this paper proposes, simulates, and evaluates a decision support system to address the question of disposition. The decision engines use historical data and statistics to estimate net recoveries in resale and forecasted demand to estimate net recoveries through teardown. Linear regressions were found to have poor power in predicting overall net recoveries; however, simple heuristics were found to identify likely low recovery systems. Overall, the implementation of the decision support system will drive improved net recoveries, with savings estimated to be greater than $1 million annually.","abstract_has_math":false,"creators":["Lin, Christopher C. (Christopher Cheyih)"],"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":["David Simchi-Levi and Roy Welsch."],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-22T22:21:05Z","subjects":["Sloan School of Management.","Electrical Engineering and Computer Science.","Leaders for Global Operations Program."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/59185","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["David Simchi-Levi and Roy Welsch."]},{"key":"dc:contributor.department","label":"Department","values":["Leaders for Global Operations Program at MIT","Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","Sloan School of Management"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Leaders for Global Operations Program."]},{"key":"dc:creator","label":"Author","values":["Lin, Christopher C. 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Historically, it has refurbished most of its returned systems for resale on its Dell Outlet website. While this has provided high net recoveries (revenue less incurred costs) compared to its peers, Dell believes there is ample opportunity in cannibalizing some returned systems for the piece parts (i.e. \"teardown\"). These harvested piece parts can be used to service field systems, repair refurbished systems, or directly sold to customers as spare parts. Dell is concerned about ensuring an optimal disposition of system to teardown vs. direct resale. Written as part of research internship at Dell, this paper proposes, simulates, and evaluates a decision support system to address the question of disposition. The decision engines use historical data and statistics to estimate net recoveries in resale and forecasted demand to estimate net recoveries through teardown. Linear regressions were found to have poor power in predicting overall net recoveries; however, simple heuristics were found to identify likely low recovery systems. Overall, the implementation of the decision support system will drive improved net recoveries, with savings estimated to be greater than $1 million annually."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M.","M.B.A."]},{"key":"dc:title","label":"Title","values":["A decision system for routing returned product to the optimal recovery channel"]}]}],"canonical_facts":{"dc:contributor.advisor":["David Simchi-Levi and Roy Welsch."],"dc:contributor.department":["Leaders for Global Operations Program at MIT","Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","Sloan School of Management"],"dc:contributor.other":["Leaders for Global Operations Program."],"dc:creator":["Lin, Christopher C. (Christopher Cheyih)"],"dc:date.accessioned":["2010-10-12T18:07:44Z"],"dc:date.available":["2010-10-12T18:07:44Z"],"dc:date.issued":["2010"],"dc:description":["Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2010.","Cataloged from PDF version of thesis.","Includes bibliographical references (p. 62-63)."],"dc:description.abstract":["Dell, a leading computer manufacturer, must deal with systems returned from its customers. Historically, it has refurbished most of its returned systems for resale on its Dell Outlet website. While this has provided high net recoveries (revenue less incurred costs) compared to its peers, Dell believes there is ample opportunity in cannibalizing some returned systems for the piece parts (i.e. \"teardown\"). These harvested piece parts can be used to service field systems, repair refurbished systems, or directly sold to customers as spare parts. Dell is concerned about ensuring an optimal disposition of system to teardown vs. direct resale. Written as part of research internship at Dell, this paper proposes, simulates, and evaluates a decision support system to address the question of disposition. The decision engines use historical data and statistics to estimate net recoveries in resale and forecasted demand to estimate net recoveries through teardown. Linear regressions were found to have poor power in predicting overall net recoveries; however, simple heuristics were found to identify likely low recovery systems. Overall, the implementation of the decision support system will drive improved net recoveries, with savings estimated to be greater than $1 million annually."],"dc:description.degree":["S.M.","M.B.A."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/59185"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Sloan School of Management.","Electrical Engineering and Computer Science.","Leaders for Global Operations Program."],"dc:title":["A decision system for routing returned product to the optimal recovery channel"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:05Z"}