{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90673"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90673","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Cloud based sensor analysis for customer-specific residual value estimation for end-of-life product recovery","abstract":"Manufacturing companies that take back products at the End-Of-Life (EOL) face a decision whether to recycle, remanufacture, repair, refurbish or scrap that product. One of the first steps is to estimate the product's residual value, primarily on the basis of age and a cursory visual inspection. However, there is a high degree of variability in actual value, even for products of the same age. This is due to the variability of environmental and use conditions to which the product has been exposed by the consumer. This paper proposes a predictive model that uses data obtained from sensors which is stored on the cloud, throughout a product's lifecycle. This data is used to more accurately estimate the value at the EOL. The model recommends the EOL solution for an individual product based on the quality level and demand for refurbished products. An illustrative cell phone example is presented, which tracks the condition of each phone during its lifecycle. Simulation is performed to obtain the residual value distribution based on predictive indicators from sensors including accelerometer data to monitor number of free falls and impact, number of battery lifecycles, humidity and temperature sensors. Results indicate improved residual value estimation due to minimizing the loss due to variance in quality of products.","abstract_html":"Manufacturing companies that take back products at the End-Of-Life (EOL) face a decision whether to recycle, remanufacture, repair, refurbish or scrap that product. One of the first steps is to estimate the product's residual value, primarily on the basis of age and a cursory visual inspection. However, there is a high degree of variability in actual value, even for products of the same age. This is due to the variability of environmental and use conditions to which the product has been exposed by the consumer. This paper proposes a predictive model that uses data obtained from sensors which is stored on the cloud, throughout a product's lifecycle. This data is used to more accurately estimate the value at the EOL. The model recommends the EOL solution for an individual product based on the quality level and demand for refurbished products. An illustrative cell phone example is presented, which tracks the condition of each phone during its lifecycle. Simulation is performed to obtain the residual value distribution based on predictive indicators from sensors including accelerometer data to monitor number of free falls and impact, number of battery lifecycles, humidity and temperature sensors. Results indicate improved residual value estimation due to minimizing the loss due to variance in quality of products.","abstract_has_math":false,"creators":["Devnani, Pranay Sunil"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Thurston, Deborah"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T19:58:14Z","date_published":"2016-07-07T19:58:14Z","updated_at":"2026-07-22T22:26:34Z","subjects":["remanufacturing","sensor","cloud-based","cell phones","end-of-life","quality","refurbishing"],"languages":["en"],"rights":["Copyright 2016 Pranay Devnani"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90673","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Thurston, Deborah"]},{"key":"dc:creator","label":"Author","values":["Devnani, Pranay Sunil"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T19:58:14Z","2016-04-27","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["remanufacturing","sensor","cloud-based","cell phones","end-of-life","quality","refurbishing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Pranay Devnani"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90673"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Manufacturing companies that take back products at the End-Of-Life (EOL) face a decision whether to recycle, remanufacture, repair, refurbish or scrap that product. One of the first steps is to estimate the product's residual value, primarily on the basis of age and a cursory visual inspection. However, there is a high degree of variability in actual value, even for products of the same age. This is due to the variability of environmental and use conditions to which the product has been exposed by the consumer. This paper proposes a predictive model that uses data obtained from sensors which is stored on the cloud, throughout a product's lifecycle. This data is used to more accurately estimate the value at the EOL. The model recommends the EOL solution for an individual product based on the quality level and demand for refurbished products. An illustrative cell phone example is presented, which tracks the condition of each phone during its lifecycle. Simulation is performed to obtain the residual value distribution based on predictive indicators from sensors including accelerometer data to monitor number of free falls and impact, number of battery lifecycles, humidity and temperature sensors. Results indicate improved residual value estimation due to minimizing the loss due to variance in quality of products.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Pranay Devnani, accepted the attached license on 2016-04-26 at 19:38.","The student, Pranay Devnani, submitted this Thesis for approval on 2016-04-26 at 19:43.","This Thesis was approved for publication on 2016-04-27 at 10:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9534 on 2016-07-07 at 13:33:37","Made available in DSpace on 2016-07-07T19:58:14Z (GMT). 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However, there is a high degree of variability in actual value, even for products of the same age. This is due to the variability of environmental and use conditions to which the product has been exposed by the consumer. This paper proposes a predictive model that uses data obtained from sensors which is stored on the cloud, throughout a product's lifecycle. This data is used to more accurately estimate the value at the EOL. The model recommends the EOL solution for an individual product based on the quality level and demand for refurbished products. An illustrative cell phone example is presented, which tracks the condition of each phone during its lifecycle. Simulation is performed to obtain the residual value distribution based on predictive indicators from sensors including accelerometer data to monitor number of free falls and impact, number of battery lifecycles, humidity and temperature sensors. Results indicate improved residual value estimation due to minimizing the loss due to variance in quality of products.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Pranay Devnani, accepted the attached license on 2016-04-26 at 19:38.","The student, Pranay Devnani, submitted this Thesis for approval on 2016-04-26 at 19:43.","This Thesis was approved for publication on 2016-04-27 at 10:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9534 on 2016-07-07 at 13:33:37","Made available in DSpace on 2016-07-07T19:58:14Z (GMT). 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