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Charles Cooney and Don Rosenfield. Sita, Dannielle (Dannielle Rose) Leaders for Global Operations Program. Leaders for Global Operations Program at MIT Massachusetts Institute of Technology. Department of Mechanical Engineering Sloan School of Management 2011-09-27T18:36:46Z 2011-09-27T18:36:46Z 2011 2011 http://hdl.handle.net/1721.1/66049 752306707 Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 63-65). With increasing cost pressure on commodity vaccine products, Novartis Vaccines & Diagnostics is continually looking for ways to improve operating efficiencies and decrease costs. As the largest drug product manufacturing site for Novartis flu vaccine products, Rosia Aseptic Operations experiences dramatic swings in required man-hours throughout the year to accommodate the seasonal nature of flu demand. This challenge is further exasperated by long training lead times for new aseptic operators and substantial severance costs for a permanent employee headcount reduction in Italy. With over 50% of the aseptic operators in Rosia on temporary contracts, management spends at least 25 hours per month reviewing headcount in order to make assessments on contract renewals and expirations. Therefore, this thesis investigates the hypothesis that understanding resource needs can decrease labor costs as well as save management time. A labor resource model based on a demand forecast, operational input data, and a scheduling optimization was developed and validated. The outputs of the model support decisions on overall staffing levels by department as well as provide tools to analyze the appropriate mix of temporary and permanent employee contracts and to understand the time lag associated with staffing decisions. Additionally, sensitivity analysis can be performed to see the effect of changes in policies and shift structures. The model reduces costs and saves management time in the Rosia Aseptic Organization through the longer-term depiction of headcount needs, the cost analysis structure and tools, insights from the production scheduling optimization, and the automatic, pre-crafted graphs and tables. Further discussion of the concepts of aggregate production planning, reveals additional opportunities for Novartis to reduce overall production costs through enabling strategies to match capacity with demand. by Dannielle Sita. S.M. M.B.A. 75 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 Sloan School of Management. Mechanical Engineering. Leaders for Global Operations Program. Forecasting and planning for a multi-product seasonal production facility Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="af1dd3cf-5a47-4815-b64e-ae86bc5a14b4"> <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>Forecasting and planning for a multi-product seasonal production facility</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2011</PublicationDate> <Authors> <Author> <DisplayName>Sita, Dannielle (Dannielle Rose)</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>Sloan School of Management.</Keyword> <Keyword>Mechanical Engineering.</Keyword> <Keyword>Leaders for Global Operations Program.</Keyword> <Abstract>With increasing cost pressure on commodity vaccine products, Novartis Vaccines &amp; Diagnostics is continually looking for ways to improve operating efficiencies and decrease costs. As the largest drug product manufacturing site for Novartis flu vaccine products, Rosia Aseptic Operations experiences dramatic swings in required man-hours throughout the year to accommodate the seasonal nature of flu demand. This challenge is further exasperated by long training lead times for new aseptic operators and substantial severance costs for a permanent employee headcount reduction in Italy. With over 50% of the aseptic operators in Rosia on temporary contracts, management spends at least 25 hours per month reviewing headcount in order to make assessments on contract renewals and expirations. Therefore, this thesis investigates the hypothesis that understanding resource needs can decrease labor costs as well as save management time. A labor resource model based on a demand forecast, operational input data, and a scheduling optimization was developed and validated. The outputs of the model support decisions on overall staffing levels by department as well as provide tools to analyze the appropriate mix of temporary and permanent employee contracts and to understand the time lag associated with staffing decisions. Additionally, sensitivity analysis can be performed to see the effect of changes in policies and shift structures. The model reduces costs and saves management time in the Rosia Aseptic Organization through the longer-term depiction of headcount needs, the cost analysis structure and tools, insights from the production scheduling optimization, and the automatic, pre-crafted graphs and tables. Further discussion of the concepts of aggregate production planning, reveals additional opportunities for Novartis to reduce overall production costs through enabling strategies to match capacity with demand.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>