{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/34741"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/34741","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Critical process parameter determination during production start-up","abstract":"Production start-up data is consistently utilized in a reactive manner during the initial stages of a product's lifecycle. However, if proactive information systems are created before full scale production starts, ramp-up cycles can be shortened considerably. This project attempts to develop a framework for analyzing process data quickly and efficiently during a new product start-up in order to provide information for the short term goals relating to attaining stable processes as well as provide guidance on long term handles for process improvement. First, a summary of previous literature regarding start-up process data as well as typical stable process data usage will be presented. This will provide adequate background for evaluating typical gaps present during production ramp-up. Then, solutions to these gaps will be discussed in order to develop tools for better data analysis in shorter periods of time. These methods will then be applied to a case study involving the. new production of Kodak's DCS Pro 14N digital camera. The Kodak Professional DCS Pro 14N was an amazing leap in technology: a camera with double the resolution for roughly half the price of any product available. Unfortunately, it soon became apparent that the original demand estimates were grossly underestimated, straining original resource allocations. Manufacturing struggled to start and was already a year behind in backorders. With over 1.500 process attributes collected on each camera, the key drivers of quality had yet to be determined. The surrounding circumstances made the quick analysis of start-up data vital to effective resource management and yield improvement of the camera.","abstract_html":"Production start-up data is consistently utilized in a reactive manner during the initial stages of a product's lifecycle. However, if proactive information systems are created before full scale production starts, ramp-up cycles can be shortened considerably. This project attempts to develop a framework for analyzing process data quickly and efficiently during a new product start-up in order to provide information for the short term goals relating to attaining stable processes as well as provide guidance on long term handles for process improvement. First, a summary of previous literature regarding start-up process data as well as typical stable process data usage will be presented. This will provide adequate background for evaluating typical gaps present during production ramp-up. Then, solutions to these gaps will be discussed in order to develop tools for better data analysis in shorter periods of time. These methods will then be applied to a case study involving the. new production of Kodak's DCS Pro 14N digital camera. The Kodak Professional DCS Pro 14N was an amazing leap in technology: a camera with double the resolution for roughly half the price of any product available. Unfortunately, it soon became apparent that the original demand estimates were grossly underestimated, straining original resource allocations. Manufacturing struggled to start and was already a year behind in backorders. With over 1.500 process attributes collected on each camera, the key drivers of quality had yet to be determined. The surrounding circumstances made the quick analysis of start-up data vital to effective resource management and yield improvement of the camera.","abstract_has_math":false,"creators":["Lindsey, Christine M. (Christine Marie), 1977-"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Leaders for Manufacturing Program at MIT","school":null,"contributors":[],"advisors":["Roy M. Welsch and Gregory J. McRae."],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004","date_published":"2004","updated_at":"2026-07-22T22:21:44Z","subjects":["Chemical Engineering.","Sloan School of Management.","Leaders for Manufacturing 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/1721.1/34741","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Roy M. Welsch and Gregory J. McRae."]},{"key":"dc:contributor.department","label":"Department","values":["Leaders for Manufacturing Program at MIT","Massachusetts Institute of Technology. Department of Chemical Engineering","Sloan School of Management"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Leaders for Manufacturing Program."]},{"key":"dc:creator","label":"Author","values":["Lindsey, Christine M. (Christine Marie), 1977-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2006-11-08T16:30:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2006-11-08T16:30:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2004"]},{"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":["Chemical Engineering.","Sloan School of Management.","Leaders for Manufacturing 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":["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."]},{"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/1721.1/34741"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.","Includes bibliographical references (p. 83-84)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Production start-up data is consistently utilized in a reactive manner during the initial stages of a product's lifecycle. However, if proactive information systems are created before full scale production starts, ramp-up cycles can be shortened considerably. This project attempts to develop a framework for analyzing process data quickly and efficiently during a new product start-up in order to provide information for the short term goals relating to attaining stable processes as well as provide guidance on long term handles for process improvement. First, a summary of previous literature regarding start-up process data as well as typical stable process data usage will be presented. This will provide adequate background for evaluating typical gaps present during production ramp-up. Then, solutions to these gaps will be discussed in order to develop tools for better data analysis in shorter periods of time. These methods will then be applied to a case study involving the. new production of Kodak's DCS Pro 14N digital camera. The Kodak Professional DCS Pro 14N was an amazing leap in technology: a camera with double the resolution for roughly half the price of any product available. Unfortunately, it soon became apparent that the original demand estimates were grossly underestimated, straining original resource allocations. Manufacturing struggled to start and was already a year behind in backorders. With over 1.500 process attributes collected on each camera, the key drivers of quality had yet to be determined. The surrounding circumstances made the quick analysis of start-up data vital to effective resource management and yield improvement of the camera.","(cont.) After using the new process modeling framework and modified control techniques on the example Kodak case, two additional topics will be discussed. First, the many classifications of return on investment in proactive start-up data analysis will be presented. Ranging from waste minimization to higher customer satisfaction, these incentives justify early preparation for start- up data analysis. Finally, future areas of study will be recommended to augment the findings within the thesis."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.B.A.","S.M."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Critical process parameter determination during production start-up"]}]}],"canonical_facts":{"dc:contributor.advisor":["Roy M. Welsch and Gregory J. McRae."],"dc:contributor.department":["Leaders for Manufacturing Program at MIT","Massachusetts Institute of Technology. Department of Chemical Engineering","Sloan School of Management"],"dc:contributor.other":["Leaders for Manufacturing Program."],"dc:creator":["Lindsey, Christine M. (Christine Marie), 1977-"],"dc:date.accessioned":["2006-11-08T16:30:02Z"],"dc:date.available":["2006-11-08T16:30:02Z"],"dc:date.issued":["2004"],"dc:description":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.","Includes bibliographical references (p. 83-84)."],"dc:description.abstract":["Production start-up data is consistently utilized in a reactive manner during the initial stages of a product's lifecycle. However, if proactive information systems are created before full scale production starts, ramp-up cycles can be shortened considerably. This project attempts to develop a framework for analyzing process data quickly and efficiently during a new product start-up in order to provide information for the short term goals relating to attaining stable processes as well as provide guidance on long term handles for process improvement. First, a summary of previous literature regarding start-up process data as well as typical stable process data usage will be presented. This will provide adequate background for evaluating typical gaps present during production ramp-up. Then, solutions to these gaps will be discussed in order to develop tools for better data analysis in shorter periods of time. These methods will then be applied to a case study involving the. new production of Kodak's DCS Pro 14N digital camera. The Kodak Professional DCS Pro 14N was an amazing leap in technology: a camera with double the resolution for roughly half the price of any product available. Unfortunately, it soon became apparent that the original demand estimates were grossly underestimated, straining original resource allocations. Manufacturing struggled to start and was already a year behind in backorders. With over 1.500 process attributes collected on each camera, the key drivers of quality had yet to be determined. The surrounding circumstances made the quick analysis of start-up data vital to effective resource management and yield improvement of the camera.","(cont.) After using the new process modeling framework and modified control techniques on the example Kodak case, two additional topics will be discussed. First, the many classifications of return on investment in proactive start-up data analysis will be presented. Ranging from waste minimization to higher customer satisfaction, these incentives justify early preparation for start- up data analysis. Finally, future areas of study will be recommended to augment the findings within the thesis."],"dc:description.degree":["M.B.A.","S.M."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/34741"],"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":["Chemical Engineering.","Sloan School of Management.","Leaders for Manufacturing Program."],"dc:title":["Critical process parameter determination during production start-up"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:44Z"}