Camp Carey is an annual freshman orientation program that takes place before the beginning of the semester in late July and early August. As the incoming W. P. Carey classes continue to grow each year, so to does the size of Camp. Beginning this project, we looked at potential that we could directly impact the quality of the camp experience, and ensure that Camp remains a memorable and quality experience for all involved. Camp is directed and facilitated every year by W. P. Carey staff members and a group upperclassmen, the camp directors and facilitators. Due to the direct impact that these upperclassmen have on the camp experience, we decided to focus our attention on improving the training provided to these individuals, and to emphasize a process of continuous data collection and improvement. The director training is broken into three modules that focus on risk management, facilitator selection, and facilitator training. Each of the seven exercises in the director training is based on a tool or practice used by modern companies in project management and human resources management. They were designed with three goals in mind: to immediately increase the directors' level of preparedness for Camp, to produce a written record to be used by directors in subsequent Camp seasons, and to provide directors with an introductory level of experience with concepts and tools that will benefit them in their professional careers. The facilitator training portion centers around the creation of a 1 credit, repeatable hybrid course to both reward facilitators, train them in proper conduct and materials for camp, as well as collect valuable feedback from the facilitators. The creation of a larger spring training session, designed to prepare the facilitators for activity facilitation, emergency preparedness, and representing W. P. Carey and ASU, and the implementation of a summer review training session are designed to prepare facilitators to lead the best camp possible. Further, the essays and surveys involved in the class are set up to gather valuable information and feedback from the facilitators for further improving the program year-over-year.

The authors (hereinafter, "the team") engaged in a consulting project with Honeywell Process Solutions, on behalf of the New Venture Group (nVg) The New Venture Group is a student-run management consulting firm within the W.P. Carey School of Business. Its purpose is to provide an experience that allows members to learn about management consulting by interacting with real clients doing value -adding work. Through this particular client engagement, the team was asked to research and develop a structured process that would allow Honeywell Process Solutions to usefully compare 22 factories to each other on a broad range of performance issues.

Within humanitarian logistics, there has been a growing trend of adopting information systems to enhance the responsiveness of aid delivery. By utilizing such technology, organizations are able to take advantage of information sharing and its benefits, including improved coordination and reduced uncertainty. This paper seeks to explore this phenomenon using organizational information processing theory. Drawing from complexity literature, we argue that demand complexity should have a positive relationship with information sharing. Moreover, higher levels of information sharing should generate higher responsiveness. Lastly, we examine the effects of organizational structure on the relationship between information sharing and responsiveness. We posit that the degree of centralization will have a positive moderation effect on the aforementioned relationship. The paper then describes the methodology planned to test these hypotheses. We will design a case-based simulation that will incorporate current disaster situations and parameters experienced by Community Preparedness Exercise and Fair (COMPEF), which acts as a broker for the City of Tempe and various humanitarian groups. With the case-based simulation data, we will draw theoretical and managerial implications for the field of humanitarian logistics.

Purpose: This thesis studies the behavior and actions of introverted and extraverted students in regards to preparing for and obtaining a postgraduate position. The purpose of this thesis is to develop an understanding of introverts' and extraverts' approach to the job search process and to provide suggestions to improve the job search process. Methodology: In addition to research of existing literature, a survey was given to students at the W. P. Carey School of Business at Arizona State University, to determine students' job search behaviors, and to recruiters of organizations who recruit from the W. P. Carey School of Business at Arizona State University to determine what recruiters look for in a candidate. Findings - We found that extraverts are more likely to network online than introverts. Secondly, we found that extraverts are more likely to self-promote their strengths to company recruiters than introverts. Thirdly, we found that introverts are more reserved when it comes to discussing their strengths with company recruiters than extraverts. Fourthly, we found that extraverts are more likely to feel as though they successfully represent themselves to company recruiters than introverts. Additionally, we found that the top three behaviors that recruiters look for in candidates include the candidate being energized about the prospects of working for the organization, that the candidate is knowledgeable about the organization, and that the candidate asks questions and introduces him-or-herself at organization information sessions. The three lowest rated behaviors were that the candidate uses live (in-person) networking to connect with the recruiter, that the candidate is reserved when discussing his/her accomplishments, and that the candidate uses online networking to connect with recruiters.

This paper will explore how suppliers are being evaluated. It will focus on the automotive industry and the state of supplier relations in two major automotive manufacturers in the United States. A literature review will reveal common supplier metrics across industries and what they attempt to measure. Further exploration into the structure and problems at one automotive manufacturer will reveal areas of improvement. Finally, a new balanced scorecard system will be proposed to better measure supplier performance.

Yield is a key process performance characteristic in the capital-intensive semiconductor fabrication process. In an industry where machines cost millions of dollars and cycle times are a number of months, predicting and optimizing yield are critical to process improvement, customer satisfaction, and financial success. Semiconductor yield modeling is essential to identifying processing issues, improving quality, and meeting customer demand in the industry. However, the complicated fabrication process, the massive amount of data collected, and the number of models available make yield modeling a complex and challenging task. This work presents modeling strategies to forecast yield using generalized linear models (GLMs) based on defect metrology data. The research is divided into three main parts. First, the data integration and aggregation necessary for model building are described, and GLMs are constructed for yield forecasting. This technique yields results at both the die and the wafer levels, outperforms existing models found in the literature based on prediction errors, and identifies significant factors that can drive process improvement. This method also allows the nested structure of the process to be considered in the model, improving predictive capabilities and violating fewer assumptions. To account for the random sampling typically used in fabrication, the work is extended by using generalized linear mixed models (GLMMs) and a larger dataset to show the differences between batch-specific and population-averaged models in this application and how they compare to GLMs. These results show some additional improvements in forecasting abilities under certain conditions and show the differences between the significant effects identified in the GLM and GLMM models. The effects of link functions and sample size are also examined at the die and wafer levels. The third part of this research describes a methodology for integrating classification and regression trees (CART) with GLMs. This technique uses the terminal nodes identified in the classification tree to add predictors to a GLM. This method enables the model to consider important interaction terms in a simpler way than with the GLM alone, and provides valuable insight into the fabrication process through the combination of the tree structure and the statistical analysis of the GLM.

In today's global market, companies are facing unprecedented levels of uncertainties in supply, demand and in the economic environment. A critical issue for companies to survive increasing competition is to monitor the changing business environment and manage disturbances and changes in real time. In this dissertation, an integrated framework is proposed using simulation and online calibration methods to enable the adaptive management of large-scale complex supply chain systems. The design, implementation and verification of the integrated approach are studied in this dissertation. The research contributions are two-fold. First, this work enriches symbiotic simulation methodology by proposing a framework of simulation and advanced data fusion methods to improve simulation accuracy. Data fusion techniques optimally calibrate the simulation state/parameters by considering errors in both the simulation models and in measurements of the real-world system. Data fusion methods - Kalman Filtering, Extended Kalman Filtering, and Ensemble Kalman Filtering - are examined and discussed under varied conditions of system chaotic levels, data quality and data availability. Second, the proposed framework is developed, validated and demonstrated in `proof-of-concept' case studies on representative supply chain problems. In the case study of a simplified supply chain system, Kalman Filtering is applied to fuse simulation data and emulation data to effectively improve the accuracy of the detection of abnormalities. In the case study of the `beer game' supply chain model, the system's chaotic level is identified as a key factor to influence simulation performance and the choice of data fusion method. Ensemble Kalman Filtering is found more robust than Extended Kalman Filtering in a highly chaotic system. With appropriate tuning, the improvement of simulation accuracy is up to 80% in a chaotic system, and 60% in a stable system. In the last study, the integrated framework is applied to adaptive inventory control of a multi-echelon supply chain with non-stationary demand. It is worth pointing out that the framework proposed in this dissertation is not only useful in supply chain management, but also suitable to model other complex dynamic systems, such as healthcare delivery systems and energy consumption networks.