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Roy M. Welsch and Gregory J. McRae. Lindsey, Christine M. (Christine Marie), 1977- Leaders for Manufacturing Program. Leaders for Manufacturing Program at MIT Massachusetts Institute of Technology. Department of Chemical Engineering Sloan School of Management 2006-11-08T16:30:02Z 2006-11-08T16:30:02Z 2004 2004 http://hdl.handle.net/1721.1/34741 56052459 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). 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. by Christine M. Lindsey. M.B.A. S.M. 109 p. 3766109 bytes 3765916 bytes application/pdf application/pdf eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Chemical Engineering. Sloan School of Management. Leaders for Manufacturing Program. Critical process parameter determination during production start-up Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8da0d4aa-dbc5-4162-a791-416c2b629c11"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Critical process parameter determination during production start-up</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2004</PublicationDate> <Authors> <Author> <DisplayName>Lindsey, Christine M. (Christine Marie), 1977-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Chemical Engineering.</Keyword> <Keyword>Sloan School of Management.</Keyword> <Keyword>Leaders for Manufacturing Program.</Keyword> <Abstract>Production start-up data is consistently utilized in a reactive manner during the initial stages of a product&apos;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&apos;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> <Abstract>(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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>