2026-09-19T03:42:25Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/694832022-01-13T07:54:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

David E. Hardt. Chow, Jason (Jason Tsz Lok) Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2012-02-29T17:58:38Z 2012-02-29T17:58:38Z 2011 2011 http://hdl.handle.net/1721.1/69483 775598891 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 97-98). The MIT Master of Engineering in Manufacturing team worked on productivity and operational improvement projects with Konarka Technologies, Inc., a world-leading organic photovoltaic panel manufacturing facility that is in the process of ramping up their production volumes to meet growing demand. This thesis focuses on the development of an automatic inventory tracking system and data collection tools to be used for accurate production scheduling in the organic photovoltaic (OPV) finishing process after the OPVs have been coated with the active material). Because of the significant labor hours required and inaccuracies involved in manual inventory counting, an automated inventory tracking system was developed for the finishing processes using barcode readers and a Microsoft SQL Server database. The panel yield and time stamp information collected from the database was used as the basis for statistical analysis of solar performance testing data. This statistical data, as well as other time study data, was then applied in the development of a comprehensive production scheduling tool for accurate lead-time prediction for the different product types. Through increased accuracy in production scheduling, the productivity of the manufacturing facility can be improved. by Jason Chow. M.Eng. 106 p. 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 Mechanical Engineering. Productivity and system improvements in an organic photovoltaic panel manufacturing facility Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="87fc9e0d-8423-4173-bd76-17e4fdc362ee"> <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>Productivity and system improvements in an organic photovoltaic panel manufacturing facility</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2011</PublicationDate> <Authors> <Author> <DisplayName>Chow, Jason (Jason Tsz Lok)</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>Mechanical Engineering.</Keyword> <Abstract>The MIT Master of Engineering in Manufacturing team worked on productivity and operational improvement projects with Konarka Technologies, Inc., a world-leading organic photovoltaic panel manufacturing facility that is in the process of ramping up their production volumes to meet growing demand. This thesis focuses on the development of an automatic inventory tracking system and data collection tools to be used for accurate production scheduling in the organic photovoltaic (OPV) finishing process after the OPVs have been coated with the active material). Because of the significant labor hours required and inaccuracies involved in manual inventory counting, an automated inventory tracking system was developed for the finishing processes using barcode readers and a Microsoft SQL Server database. The panel yield and time stamp information collected from the database was used as the basis for statistical analysis of solar performance testing data. This statistical data, as well as other time study data, was then applied in the development of a comprehensive production scheduling tool for accurate lead-time prediction for the different product types. Through increased accuracy in production scheduling, the productivity of the manufacturing facility can be improved.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>