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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.
Lean philosophy is a set of practices aimed at reducing waste in an industry/enterprise. By eliminating the aspects of a system that do not add value, the system process will be able to work continuously in a flow, and as a result have a shorter cycle time. With a shorter cycle time, less resources are diminished, and efforts can be properly distributed in order to achieve maximum efficiency. In relation, Six Sigma is a process that aims to reduce the variability of a system, and in turn reduce the number of defects and improve overall quality of a product/process. For this reason, Lean and Six Sigma go hand-in-hand. Cutting out non-value adding steps in a process will increase efficiency and perfecting the steps still in place will improve quality. Both aspects are important when it comes to the success of a business practice. DNASU Plasmid Repository would be a major benefactor of the Lean Six Sigma process. The process of cloning DNA requires great attention to detail and time in order to avoid defects. For instance, any mistake made in the bacteria growth process, such as contamination, will result in a significant amount of time being wasted. In addition, the purification of DNA steps also necessitates vigilant observation since the procedure is highly susceptible to little mistakes that could have big impacts. The goal of this project will be to integrate Lean Six Sigma methodology into the DNASU laboratory. By applying numerous aspects of Lean Six Sigma, the DNA repository will be able to improve its efficiency and quality of processes and obtain its highest rate of success.
This paper analyzes the impact of the December 2022 winter storm on Southwest Airlines (SWA). The storm caused delays and cancellations for all airlines, but SWA was the only major airline that was unable to recover fully. The disruption was unique due to the higher volume of people traveling during the holiday season and the lack of good alternative transportation for stranded passengers. The paper explains SWA's point-to-point (PTP) model, which allows them to offer competitive ticket prices, and organizational factors that have helped them hold a significant market share. The paper also discusses previous failures of SWA's IT and aircraft maintenance management systems and the outdated crewing system, which were not addressed until after the storm. The paper uses AnyLogic agent based modeling to investigate why SWA was so affected and why it took them so long to recover.
Motivated by the need for efficient active learning strategies, this dissertation proposes new algorithms for batch-mode, pool-based active learning. The research considers the following questions: how can unsupervised knowledge of the input features (exploration) improve learning when incorporated with supervised learning (exploitation)? How to characterize exploration in active learning when data is high-dimensional? Finally, how to adaptively make a balance between exploration and exploitation?
The first contribution proposes a new active learning algorithm, Cluster-based Stochastic Query-by-Forest (CSQBF), which provides a batch-mode strategy that accelerates learning with added value from exploration and improved exploitation scores. CSQBF balances exploration and exploitation using a probabilistic scoring criterion based on classification probabilities from a tree-based ensemble model within each data cluster.
The second contribution introduces two more query strategies, Double Margin Active Learning (DMAL) and Cluster Agnostic Active Learning (CAAL), that combine consistent exploration and exploitation modules into a coherent and unified measure for label query. Instead of assuming a fixed clustering structure, CAAL and DMAL adopt a soft-clustering strategy which provides a new approach to formalize exploration in active learning.
The third contribution addresses the challenge of dynamically making a balance between exploration and exploitation criteria throughout the active learning process. Two adaptive algorithms are proposed based on feedback-driven bandit optimization frameworks that elegantly handle this issue by learning the relationship between exploration-exploitation trade-off and an active learner's performance.