{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/112870"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/112870","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Predicting on-time delivery in the trucking industry","abstract":"On-time delivery is a key metric in the trucking segment of the transportation industry. If on-time delivery can be predicted, more effective resource allocation can be achieved. This research focuses on building a predictive analytics model, specifically logistic regression, given a historical dataset. The model, developed using six explanatory variables with statistical significance, results in a 76.4% resource reduction while incurring an impactful error of 2.4%. Interpretability and application of the logistic regression model can deliver value in predictive power across many industries. Resulting cost reductions lead to strategic competitive positioning among firms employing predictive analytics techniques.","abstract_html":"On-time delivery is a key metric in the trucking segment of the transportation industry. If on-time delivery can be predicted, more effective resource allocation can be achieved. This research focuses on building a predictive analytics model, specifically logistic regression, given a historical dataset. The model, developed using six explanatory variables with statistical significance, results in a 76.4% resource reduction while incurring an impactful error of 2.4%. Interpretability and application of the logistic regression model can deliver value in predictive power across many industries. Resulting cost reductions lead to strategic competitive positioning among firms employing predictive analytics techniques.","abstract_has_math":false,"creators":["Duarte Alcoba, Rafael","Ohlund, Kenneth W"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Supply Chain Management Program","school":null,"contributors":[],"advisors":["Matthias Winkenbach."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:20:57Z","subjects":["Supply Chain Management Program."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/112870","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Matthias Winkenbach."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Supply Chain Management Program"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Supply Chain Management Program."]},{"key":"dc:creator","label":"Author","values":["Duarte Alcoba, Rafael","Ohlund, Kenneth W"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-12-20T18:15:31Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-12-20T18:15:31Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Supply Chain Management Program."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."]},{"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/1721.1/112870"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017.","Cataloged from PDF version of thesis.","Includes bibliographical references (page 51)."]},{"key":"dc:description.abstract","label":"Abstract","values":["On-time delivery is a key metric in the trucking segment of the transportation industry. If on-time delivery can be predicted, more effective resource allocation can be achieved. This research focuses on building a predictive analytics model, specifically logistic regression, given a historical dataset. The model, developed using six explanatory variables with statistical significance, results in a 76.4% resource reduction while incurring an impactful error of 2.4%. Interpretability and application of the logistic regression model can deliver value in predictive power across many industries. Resulting cost reductions lead to strategic competitive positioning among firms employing predictive analytics techniques."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng. in Supply Chain Management"]},{"key":"dc:title","label":"Title","values":["Predicting on-time delivery in the trucking industry"]}]}],"canonical_facts":{"dc:contributor.advisor":["Matthias Winkenbach."],"dc:contributor.department":["Massachusetts Institute of Technology. Supply Chain Management Program"],"dc:contributor.other":["Massachusetts Institute of Technology. Supply Chain Management Program."],"dc:creator":["Duarte Alcoba, Rafael","Ohlund, Kenneth W"],"dc:date.accessioned":["2017-12-20T18:15:31Z"],"dc:date.available":["2017-12-20T18:15:31Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017.","Cataloged from PDF version of thesis.","Includes bibliographical references (page 51)."],"dc:description.abstract":["On-time delivery is a key metric in the trucking segment of the transportation industry. If on-time delivery can be predicted, more effective resource allocation can be achieved. This research focuses on building a predictive analytics model, specifically logistic regression, given a historical dataset. The model, developed using six explanatory variables with statistical significance, results in a 76.4% resource reduction while incurring an impactful error of 2.4%. Interpretability and application of the logistic regression model can deliver value in predictive power across many industries. Resulting cost reductions lead to strategic competitive positioning among firms employing predictive analytics techniques."],"dc:description.degree":["M. Eng. in Supply Chain Management"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/112870"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Supply Chain Management Program."],"dc:title":["Predicting on-time delivery in the trucking industry"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:20:57Z"}