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.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

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.