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dc.contributor.advisorSchultz, David E.
dc.contributor.advisorSprouls, Eric P.
dc.contributor.advisorSchimschal, Jason
dc.contributor.authorWinternheimer, Karen A.
dc.date.accessioned2019-12-09T18:13:49Z
dc.date.available2019-12-09T18:13:49Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/20.500.12419/437
dc.descriptionThesis available in Rice Library University Archives and Special Collection.
dc.description.abstractThis paper investigates how kitting frequency, workload distribution, and innovative technology affect cycle time, throughput, and cost within Quality Control. Using historical arrival data for individual lots, the best kitting option among the, following is selected: weekly, bi-weekly, or daily. Subsequently the distribution of work among single versus multiple analysts is considered. The implication of robotics and uPLC are discussed. The best combination of kitting interval and workload distribution is composed of bi-weekly kitting and double analyst workload distribution. This combination reduces cycle time, increases throughput, and decreases cost. However, Crystal Ball sensitivity analysis reveals bi-weekly kitting is very sensitive to changes in the arrival pattern of lots. This can be counteracted by monitoring the schedule to determine what number of lots will provide the most cost effective kitting interval. Implementation of this combination provided substantial decrease in cycle time within Quality Control and provided an opportunity to utilize automation in the future.
dc.titleProcess improvement for pharmaceutical quality control
html.description.abstractThis paper investigates how kitting frequency, workload distribution, and innovative technology affect cycle time, throughput, and cost within Quality Control. Using historical arrival data for individual lots, the best kitting option among the, following is selected: weekly, bi-weekly, or daily. Subsequently the distribution of work among single versus multiple analysts is considered. The implication of robotics and uPLC are discussed. The best combination of kitting interval and workload distribution is composed of bi-weekly kitting and double analyst workload distribution. This combination reduces cycle time, increases throughput, and decreases cost. However, Crystal Ball sensitivity analysis reveals bi-weekly kitting is very sensitive to changes in the arrival pattern of lots. This can be counteracted by monitoring the schedule to determine what number of lots will provide the most cost effective kitting interval. Implementation of this combination provided substantial decrease in cycle time within Quality Control and provided an opportunity to utilize automation in the future.
dc.contributor.degreeMaster of Science in Industrial Management
dc.typeThesis (M.S.I.M.)--University of Southern Indiana, 2011


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