The first step in process improvement is to scope the problem, next is measure the current process, but if data is not readily available and cannot be manually collected, then a measurement system must be implemented. General Dynamics Mission Systems (GDMS) is a lean company that is always seeking to improve. One of their current bottlenecks is the incoming inspection department. This department is responsible for finding defects on parts purchased and is critical to the high reliability product produced by GDMS. To stay competitive and hold their market share, a decision was made to optimize incoming inspection. This proved difficult because no data is being collected. Early steps in many process improvement methodologies, such as Define, Measure, Analyze, Improve and Control (DMAIC), include data collection; however, no measurement system was in place, resulting in no available data for improvement. The solution to this problem was to design and implement a Management Information System (MIS) that will track a variety of data. This will provide the company with data that will be used for analysis and improvement. The first stage of the MIS was developed in Microsoft Excel with Visual Basic for Applications because of the low cost and overall effectiveness of the software. Excel allows update to be made quickly, and allows GDMS to collect data immediately. Stage two would be moving the MIS to a more practicable software, such as Access or MySQL. This thesis is only focuses on stage one of the MIS, and GDMS will proceed with stage two.

Based on findings of previous studies, there was speculation that two well-known experimental design software packages, JMP and Design Expert, produced varying power outputs given the same design and user inputs. For context and scope, another popular experimental design software package, Minitab® Statistical Software version 17, was added to the comparison. The study compared multiple test cases run on the three software packages with a focus on 2k and 3K factorial design and adjusting the standard deviation effect size, number of categorical factors, levels, number of factors, and replicates. All six cases were run on all three programs and were attempted to be run at one, two, and three replicates each. There was an issue at the one replicate stage, however—Minitab does not allow for only one replicate full factorial designs and Design Expert will not provide power outputs for only one replicate unless there are three or more factors. From the analysis of these results, it was concluded that the differences between JMP 13 and Design Expert 10 were well within the margin of error and likely caused by rounding. The differences between JMP 13, Design Expert 10, and Minitab 17 on the other hand indicated a fundamental difference in the way Minitab addressed power calculation compared to the latest versions of JMP and Design Expert. This was found to be likely a cause of Minitab’s dummy variable coding as its default instead of the orthogonal coding default of the other two. Although dummy variable and orthogonal coding for factorial designs do not show a difference in results, the methods affect the overall power calculations. All three programs can be adjusted to use either method of coding, but the exact instructions for how are difficult to find and thus a follow-up guide on changing the coding for factorial variables would improve this issue.

In an increasingly global economy, companies face challenges with implementing successful business and marketing strategies in cultures different from their own. This paper calls upon previous research to compile a per-country outline of general behaviors and expectations when doing business overseas. Using categorical definitions from Hofstede's 1984 study and those found in the Handbook of Global and Multicultural Negotiation, a table has been prepared to group similar countries based on their cultural biases.

The definition of "beauty" can be interpreted in many ways but when defining it literally, it is considered as such: "a combination of qualities that pleases the intellect or moral sense" (Oxford Dictionaries). Beauty simply "pleases" the intellect; it does not say that intellect is a factor of beauty itself. Beauty is nice to look at, a mere pleasure to experience in the "moral sense." It does not have anything to do with one's actions, principles or intelligence, but instead the way one presents himself or herself. If someone is deemed as intellectual, does that mean they are not viewed as beautiful? Is that why beauty is rarely associated with brains and vice versa? Has a history of stereotypes and media interference convinced us that these two concepts cannot coexist? And what if we found out that they did? Could we take that person seriously? I decided to challenge the idea of beauty and brains, and see if beauty is in fact measured by its literal definition and controlled by its assumed stereotype, or if other factors apply when deciphering someone's "beauty." First, I will analyze the perceptions and stereotypes of engineers. By looking at the public opinion of both engineers and women engineers, I can show the common struggles engineers face. Next I will look at perceptions of cheerleaders, in particular, professional cheerleaders. Through analyzing current stereotypes and gender roles associated with these women in the spotlight, I can establish how opinions of these women are formed. I will also look at a survey of a sample of Arizona State University students in which we can confirm or deny the results found through research of previous studies. We will also be able to gather personal opinions about why these stereotypes exist and how to break them down. Finally, we will look at personal accounts of current or retired National Football League (NFL) cheerleaders. These will give first-hand examples of what it is like to be both a cheerleader and a woman in STEM (science, technology, engineering, and mathematics).

The U.S. Navy and other amphibious military organizations utilize a derivation of the traditional side stroke called the Combat Side Stroke, or CSS, and tout it as the most efficient technique available. Citing its low aerobic requirements and slow yet powerful movements as superior to the traditionally-best front crawl (freestyle), the CSS is the go-to stroke for any operation in the water. The purpose of this thesis is to apply principles of Industrial Engineering to a real-world situation not typically approached from a perspective of optimization. I will analyze pre-existing data about various swim strokes in order to compare them in terms of efficiency for different variables. These variables include calories burned, speed, and strokes per unit distance, as well as their interactions. Calories will be measured by heart rate monitors, converting BPM to calories burned. Speed will be measured by stopwatch and observer. Strokes per unit distance will be measured by observer. The strokes to be analyzed include the breast stroke, crawl stroke, butterfly, and combat side stroke. The goal is to informally test the U.S. Navy's claim that the combat side stroke is the optimum stroke to conserve energy while covering distance. Because of limitations in the scope of the project, analysis will be done using data collected from literary sources rather than through experimentation. This thesis will include a design of experiment to test the findings here in practical study. The main method of analysis will be linear programming, followed by hypothesis testing, culminating in a design of experiment for future progress on this topic.

Zero Waste Management at Arizona State University is an initiative that aims to divert 90% of the waste that goes into the landfills. In order to do this, it is important to focus on the biggest generator of waste every year, which is "Food and Catering". One of the biggest challenges facing the food and catering industry is the lack of efficient and standard processes which results in immense waste every year. As a result, this thesis takes a Lean Six Sigma approach into ASU's zero waste event processes and identifies possible gaps that could be improved. It uses the DMAIC methodology to dive into a standard process for requesting and handling a zero waste event at ASU and concentrates on the logistics behind those zero waste events.

The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value for δ of the call and put option, the %B value of the stock, and the amount of time until expiration of the stock option. The %B value is the most important. The purpose of analyzing the data is to see the relationship between the variables and, given certain values, what is the probability the trade makes money. This result will be used in finding the probability certain trades make money over a period of time.

Since options are so dependent on probability, this research specifically analyzes stock options rather than stocks themselves. Stock options have value like stocks except options are leveraged. The most common model used to calculate the value of an option is the Black-Scholes Model [1]. There are five main variables the Black-Scholes Model uses to calculate the overall value of an option. These variables are θ, δ, γ, v, and ρ. The variable, θ is the rate of change in price of the option due to time decay, δ is the rate of change of the option’s price due to the stock’s changing value, γ is the rate of change of δ, v represents the rate of change of the value of the option in relation to the stock’s volatility, and ρ represents the rate of change in value of the option in relation to the interest rate [2]. In this research, the %B value of the stock is analyzed along with the time until expiration of the option. All options have the same δ. This is due to the fact that all the options analyzed in this experiment are less than two months from expiration and the value of δ reveals how far in or out of the money an option is.

The machine learning technique used to analyze the data and the probability



is support vector machines. Support vector machines analyze data that can be classified in one of two or more groups and attempts to find a pattern in the data to develop a model, which reliably classifies similar, future data into the correct group. This is used to analyze the outcome of stock options.

In this paper, a literature review is presented on the application of Bayesian networks applied in system reliability analysis. It is shown that Bayesian networks have become a popular modeling framework for system reliability analysis due to the benefits that Bayesian networks have the capability and flexibility to model complex systems, update the probability according to evidences and give a straightforward and compact graphical representation. Research on approaches for Bayesian network learning and inference are summarized. Two groups of models with multistate nodes were developed for scenarios from constant to continuous time to apply and contrast Bayesian networks with classical fault tree method. The expanded model discretized the continuous variables and provided failure related probability distribution over time.