{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/45225"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/45225","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Customer focused collaborative demand planning","abstract":"Many firms worldwide have adopted the process of Sales & Operations Planning (S&OP) process where internal departments within a firm collaborate with each other to generate a demand forecast. In a collaborative demand planning process buyers and sellers collaborate with each other to generate a mutually agreed upon forecast which takes into account the needs and limitations of both buyers and sellers. In this research we concentrate on finding out the value from both statistical and qualitative forecasts. We apply standard forecasting algorithms to generate a statistical forecast. We also generate a hybrid model that is a weighted technique using both a statistical and qualitative forecast. Then we evaluate the statistical, hybrid, and qualitative collaborative forecasts using an error analysis methodology. Finally we recommend an approach for forecasting a family of items based on our analysis and results. We also recommend changes to the existing process so that our recommendations on the forecasting approach can get seamlessly integrated into the overall process.","abstract_html":"Many firms worldwide have adopted the process of Sales & Operations Planning (S&OP) process where internal departments within a firm collaborate with each other to generate a demand forecast. In a collaborative demand planning process buyers and sellers collaborate with each other to generate a mutually agreed upon forecast which takes into account the needs and limitations of both buyers and sellers. In this research we concentrate on finding out the value from both statistical and qualitative forecasts. We apply standard forecasting algorithms to generate a statistical forecast. We also generate a hybrid model that is a weighted technique using both a statistical and qualitative forecast. Then we evaluate the statistical, hybrid, and qualitative collaborative forecasts using an error analysis methodology. Finally we recommend an approach for forecasting a family of items based on our analysis and results. We also recommend changes to the existing process so that our recommendations on the forecasting approach can get seamlessly integrated into the overall process.","abstract_has_math":false,"creators":["Jha, Ratan (Ratan Mohan)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Engineering Systems Division.","school":null,"contributors":[],"advisors":["Lawrence Lapide."],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008","date_published":"2008","updated_at":"2026-07-22T22:21:11Z","subjects":["Engineering Systems Division."],"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/1721.1/45225","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lawrence Lapide."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Engineering Systems Division."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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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."]},{"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/1721.1/45225"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.","Includes bibliographical references (leaf 74)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Many firms worldwide have adopted the process of Sales & Operations Planning (S&OP) process where internal departments within a firm collaborate with each other to generate a demand forecast. In a collaborative demand planning process buyers and sellers collaborate with each other to generate a mutually agreed upon forecast which takes into account the needs and limitations of both buyers and sellers. In this research we concentrate on finding out the value from both statistical and qualitative forecasts. We apply standard forecasting algorithms to generate a statistical forecast. We also generate a hybrid model that is a weighted technique using both a statistical and qualitative forecast. Then we evaluate the statistical, hybrid, and qualitative collaborative forecasts using an error analysis methodology. Finally we recommend an approach for forecasting a family of items based on our analysis and results. We also recommend changes to the existing process so that our recommendations on the forecasting approach can get seamlessly integrated into the overall process."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng.in Logistics"]},{"key":"dc:title","label":"Title","values":["Customer focused collaborative demand planning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lawrence Lapide."],"dc:contributor.department":["Massachusetts Institute of Technology. Engineering Systems Division."],"dc:contributor.other":["Massachusetts Institute of Technology. Engineering Systems Division."],"dc:creator":["Jha, Ratan (Ratan Mohan)"],"dc:date.accessioned":["2009-04-29T17:11:15Z"],"dc:date.available":["2009-04-29T17:11:15Z"],"dc:date.issued":["2008"],"dc:description":["Thesis (M. 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