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The Business Venture Approach to Alleviating Poverty: What is the Bottom of the Pyramid Solution and Can It Work?

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Billions of people around the world deal with the struggles of poverty every day. Consequently, a number of others have committed themselves to help alleviate poverty. Many various methods are used, and a current consensus on the best method to

Billions of people around the world deal with the struggles of poverty every day. Consequently, a number of others have committed themselves to help alleviate poverty. Many various methods are used, and a current consensus on the best method to alleviate poverty is lacking. Generally the methods used or researched exist somewhere on the spectrum between top-down and bottom-up approaches to fighting poverty. This paper analyzes a specific method proposed by C.K. Prahalad known as the Bottom of the Pyramid solution. The premise of the method is that large multinational corporations should utilize the large conglomerate of money that exists amongst poor people \u2014 created due to the sheer number of poor people \u2014 for business ventures. Concurrently, the poor people can benefit from the company's entrance. This method has received acclaim theoretically, but still needs empirical evidence to prove its practicality. This paper compares this approach with other approaches, considers international development data trends, and analyzes case studies of actual attempts that provide insight into the approach's potential for success. The market of poor people at the bottom of the pyramid is extremely segmented which makes it very difficult for large companies to financially prosper. It is even harder to establish mutual benefit between the large corporation and the poor. It has been found that although aspects of the bottom of the pyramid method hold merit, higher potential for alleviating poverty exists when small companies venture into this space rather than large multinational corporations. Small companies can conform to a single community and niche economy to prosper \u2014 a flexibility that large companies lack. Moving forward, analyzing the actual attempts provides the best and only empirical insights; hence, it will be important to consider more approaches into developing economies as they materialize.

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2016-05

Modelling Megacities: An Approach to Modelling Dense Urban Area

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In 2010, for the first time in human history, more than half of the world's total population lived in cities; this number is expected to increase to 60% or more by 2050. The goal of this research effort is to

In 2010, for the first time in human history, more than half of the world's total population lived in cities; this number is expected to increase to 60% or more by 2050. The goal of this research effort is to create a comprehensive model and modelling framework for megacities, middleweight cities, and urban agglomerations, collectively referred to as dense urban areas. The motivation for this project comes from the United States Army's desire for readiness in all operating environments including dense urban areas. Though there is valuable insight in research to support Army operational behaviors, megacities are of unique interest to nearly every societal sector imaginable. A novel application for determining both main effects and interactive effects between factors within a dense urban area is a Design of Experiments- providing insight on factor causations. Regression Modelling can also be employed for analysis of dense urban areas, providing wide ranging insights into correlations between factors and their interactions. Past studies involving megacities concern themselves with general trend of cities and their operation. This study is unique in its efforts to model a singular megacity to enable decision support for military operational planning, as well as potential decision support to city planners to increase the sustainability of these dense urban areas and megacities.

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2016-05

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Intervention Strategies for the DoD Acquisition Process Using Simulation

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The current Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation of the major tasks and decisions within the DoD acquisition system, identifies several what-if intervention strategies to improve program completion time. However, processes that contribute to the program

The current Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation of the major tasks and decisions within the DoD acquisition system, identifies several what-if intervention strategies to improve program completion time. However, processes that contribute to the program acquisition completion time were not explicitly identified in the simulation study. This research seeks to determine the acquisition processes that contribute significantly to total simulated program time in the acquisition system for all programs reaching Milestone C. Specifically, this research examines the effect of increased scope management, technology maturity, and decreased variation and mean process times in post-Design Readiness Review contractor activities by performing additional simulation analyses. Potential policies are formulated from the results to further improve program acquisition completion time.

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2013-05

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Early Career Performance Models: Regression-Based Forecasting Models for Predicting Future Major League Baseball Player Performance

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The widespread use of statistical analysis in sports-particularly Baseball- has made it increasingly necessary for small and mid-market teams to find ways to maintain their analytical advantages over large market clubs. In baseball, an opportunity for exists for teams with

The widespread use of statistical analysis in sports-particularly Baseball- has made it increasingly necessary for small and mid-market teams to find ways to maintain their analytical advantages over large market clubs. In baseball, an opportunity for exists for teams with limited financial resources to sign players under team control to long-term contracts before other teams can bid for their services in free agency. If small and mid-market clubs can successfully identify talented players early, clubs can save money, achieve cost certainty and remain competitive for longer periods of time. These deals are also advantageous to players since they receive job security and greater financial dividends earlier in their career. The objective of this paper is to develop a regression-based predictive model that teams can use to forecast the performance of young baseball players with limited Major League experience. There were several tasks conducted to achieve this goal: (1) Data was obtained from Major League Baseball and Lahman's Baseball Database and sorted using Excel macros for easier analysis. (2) Players were separated into three positional groups depending on similar fielding requirements and offensive profiles: Group I was comprised of first and third basemen, Group II contains second basemen, shortstops, and center fielders and Group III contains left and right fielders. (3) Based on the context of baseball and the nature of offensive performance metrics, only players who achieve greater than 200 plate appearances within the first two years of their major league debut are included in this analysis. (4) The statistical software package JMP was used to create regression models of each group and analyze the residuals for any irregularities or normality violations. Once the models were developed, slight adjustments were made to improve the accuracy of the forecasts and identify opportunities for future work. It was discovered that Group I and Group III were the easiest player groupings to forecast while Group II required several attempts to improve the model.

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2013-05