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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2007., Includes bibliographical references (p. 55-56)., In early-stage drug discovery, thousands of compounds must be tested using in vitro assays to determine their exposure and safety characteristics. This data is used to guide the selection of potential drug candidates and to help chemists in optimize the properties of those compounds. At Novartis, an internal service organization called Preclinical Compound Profiling (PCP) provides these services to the company as a whole. The purpose of this internship was to help PCP make significant improvements in cycle time and cost effectiveness without reducing the quality of information provided to their customers. The project utilized a series of deterministic and stochastic models to predict the impact of multiple operational changes on cost and cycle time. The data from each model was synthesized to create a unified view allowing combinations of changes to be analyzed together. This data was evaluated in the context of the customer needs and organizational strategy to present recommendations. Changes were implemented that will reduce materials spending by $500,000 per year while simultaneously increasing capacity, reducing cycle time, and improving customer value. Additional recommendations were developed that will enable further improvements., by John P. Heiney., S.M., M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2007., Includes bibliographical references (p. 55-56)., In early-stage drug discovery, thousands of compounds must be tested using in vitro assays to determine their exposure and safety characteristics. This data is used to guide the selection of potential drug candidates and to help chemists in optimize the properties of those compounds. At Novartis, an internal service organization called Preclinical Compound Profiling (PCP) provides these services to the company as a whole. The purpose of this internship was to help PCP make significant improvements in cycle time and cost effectiveness without reducing the quality of information provided to their customers. The project utilized a series of deterministic and stochastic models to predict the impact of multiple operational changes on cost and cycle time. The data from each model was synthesized to create a unified view allowing combinations of changes to be analyzed together. This data was evaluated in the context of the customer needs and organizational strategy to present recommendations. Changes were implemented that will reduce materials spending by $500,000 per year while simultaneously increasing capacity, reducing cycle time, and improving customer value. Additional recommendations were developed that will enable further improvements., by John P. Heiney., S.M., M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2007., Includes bibliographical references (p. 55-56)., In early-stage drug discovery, thousands of compounds must be tested using in vitro assays to determine their exposure and safety characteristics. This data is used to guide the selection of potential drug candidates and to help chemists in optimize the properties of those compounds. At Novartis, an internal service organization called Preclinical Compound Profiling (PCP) provides these services to the company as a whole. The purpose of this internship was to help PCP make significant improvements in cycle time and cost effectiveness without reducing the quality of information provided to their customers. The project utilized a series of deterministic and stochastic models to predict the impact of multiple operational changes on cost and cycle time. The data from each model was synthesized to create a unified view allowing combinations of changes to be analyzed together. This data was evaluated in the context of the customer needs and organizational strategy to present recommendations. Changes were implemented that will reduce materials spending by $500,000 per year while simultaneously increasing capacity, reducing cycle time, and improving customer value. Additional recommendations were developed that will enable further improvements., by John P. Heiney., S.M., M.B.A.