Simulation as a Method for Determining Inventory Classifications for allocation

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 54-55).Companies that utilize multiple facilities to satisfy customer demand are faced with the same basic question - where should inventory be held? This thesis presents a method for answering this question, specifically for a company that allocates multiple units across multiple facilities, where any facility can fulfill an order to any customer, though with differing shipping costs. The model presented is a simulation of the shipping costs of various allocation strategies across a range of allocated inventory quantities, where the strategies simulated include consolidating all inventory in a central facility, constraining inventory to regional hubs, and spreading inventory throughout the network. The simulated results are then compared to find the low cost allocation strategy at a given level of allocated inventory. With this comparison, product groupings with the same low cost allocation strategy are identified, and are defined as "Slow", "Medium-A", "Medium-B", and "Fast" products. These groups can then be used to manage the allocation process, where "Slow" inventory is held centrally, "Medium-A" inventory held regionally, and "Fast" inventory spread throughout the network. "Medium-B" items serve as a costmitigating flexible option, where they are spread throughout the network when possible but consolidated when necessary to avoid changing the allocation for "Fast" items. At a broad level, the model presented is applicable to any company that can fulfill demand to a single customer from multiple facilities.by Braden Ball.S.M.M.B.A

Navigating to new frontiers in behavioral neuroscience: traditional neuropsychological tests predict human performance on a rodent-inspired radial-arm maze

We constructed an 11-arm, walk-through, human radial-arm maze (HRAM) as a translational instrument to compare existing methodology in the areas of rodent and human learning and memory research. The HRAM, utilized here, serves as an intermediary test between the classic rat radial-arm maze (RAM) and standard human neuropsychological and cognitive tests. We show that the HRAM is a useful instrument to examine working memory ability, explore the relationships between rodent and human memory and cognition models, and evaluate factors that contribute to human navigational ability. One-hundred-and-fifty-seven participants were tested on the HRAM, and scores were compared to performance on a standard cognitive battery focused on episodic memory, working memory capacity, and visuospatial ability. We found that errors on the HRAM increased as working memory demand became elevated, similar to the pattern typically seen in rodents, and that for this task, performance appears similar to Miller's classic description of a processing-inclusive human working memory capacity of 7 ± 2 items. Regression analysis revealed that measures of working memory capacity and visuospatial ability accounted for a large proportion of variance in HRAM scores, while measures of episodic memory and general intelligence did not serve as significant predictors of HRAM performance. We present the HRAM as a novel instrument for measuring navigational behavior in humans, as is traditionally done in basic science studies evaluating rodent learning and memory, thus providing a useful tool to help connect and translate between human and rodent models of cognitive functioning