Spring 2024 Projects
Best Overall Senior Design Project in SSoE
Project Summary
The Lawrence Livermore National Laboratory is a federally funded research and development center that is collaborating with the Space Domain Awareness (SDA) Tools, Applications, and Processing (TAP) Lab as part of the second cohort of the TAP Lab’s Apollo Accelerator program. This technology accelerator program aims to solve the operational needs of the US Space Force and further space domain awareness capabilities. The RocketWatch team was assigned to create a tool to detect if a rocket launched from Earth to geosynchronous orbit (GEO) could be a threat to GEO satellites. The primary satellites of interest are the ten Wideband Global SATCOM satellites, which are the backbone of the U.S. military’s Wideband satellite communication capabilities. The team’s Python-based tool uses an ordinary differential equation to simulate a rocket’s trajectory based on given rocket inputs, such as mass, fuel consumption rate, and maximum thrust. Satellite location and velocity data is propagated through time to determine each satellite’s estimated future position at the time the rocket could potentially reach GEO orbit. Then, the rocket’s thrust profile is optimized to see if, given the rocket parameters, the minimum possible distance between the rocket and the satellite will be within two hundred kilometers. If so, a warning that the satellite is at risk of collision will be generated. The target users for this tool are US Space Force Guardians, who can then assess the threat and employ maneuvering techniques if necessary to keep the satellite safe from collision. This tool will fit into a wide catalog of applications that the SDA TAP lab develops through their collaborations with industry, the government, and academia.
Pictured
Project Summary
Our collaborative project with the Cyprus Marine & Maritime Institute (CMMI) addresses key challenges in the maritime shipping sector, particularly pertaining to port congestion and fuel usage. Leveraging real-time ship movement data sourced from the Automatic Information System (AIS), our focus lies in predictive analytics to optimize port congestion management. By enhancing the efficiency of vessel queuing and port operations, our initiative aims to concurrently diminish fuel consumption and reduce waiting durations for ship docking, thus fostering a more sustainable maritime environment.
Employing a multifaceted approach, our project integrates various data analysis techniques and classification models. Initial stages involved the development of port congestion indicators utilizing methodologies such as convex hull and geohash area calculations. Subsequently, we constructed a classification model utilizing these indicators alongside other pertinent ship data, enabling precise predictions of individual ship service times. Guided by these models, directions are offered to ships approaching port queues, presenting three distinct scenarios: direct port entry, deceleration towards port entry, or deceleration followed by into the anchorage area for waiting. These scenarios are designed to streamline port operations, thereby fostering efficiency. In essence, our project endeavors not only to decrease operational costs for ships but also to mitigate waiting times, fuel consumption, and emissions. These measures align with sustainability goals and contribute to mitigating negative environmental impacts. Furthermore, by facilitating smoother trade flows, our efforts contribute to the establishment of reliable supply chains, fostering punctual deliveries, increased customer satisfaction, and broader economic advantages across stakeholders within the maritime shipping industry.
Project Summary
DEVCOM, the U.S. Army Combat Capabilities Development Command, provides the research, engineering, and analytical expertise to deliver capability that enables the Army to deter and defeat any adversary. Our team collaborated with DEVCOM’s Energetics, Warheads & Materials division as we worked to design an additive manufacturing (AM) layout that could manufacture propulsion charges. Propulsion charges combust in a cartridge to propel defense weaponry. These were historically manufactured using solvent-based traditional methods which are very time-consuming, complex, and involve many different types of machinery. Therefore, DEVCOM wants to move away from this and is currently manufacturing the product in a small-scale AM laboratory with a capacity of 2 pounds of product per day.
DEVCOM tasked us with creating a facility layout with enough 3D printers and staff to meet propulsion charge daily throughput of 20,000 pounds while complying with Division 1.3 safety requirements. Our team considered two different 3D printers and decided to utilize the Stratasys NEO450s due to its superior production scale capabilities and lower printer purchase cost. After selecting the Stratasys, we developed two different facility layouts in AutoCAD. These layouts consider DEVCOM’s safety requirements, production schedule, and desired production rates.
A dynamic Excel file was created to perform sensitivity analysis relative to the two proposed layout alternatives. The layouts were compared using fixed printer costs and variable labor, maintenance, and electricity costs. We recommend the layout that had the lowest cost per pound produced. Our project served as a way for DEVCOM to visualize what AM could look like so they can begin to accept it as a production method for propulsion charges. The primary future state benefit of AM is the ability to produce complex and precise geometries that could not otherwise be produced via traditional manufacturing.
Project Summary
DEVCOM Armaments is at the cutting edge of military research and development. Their continued support of the United States military includes the production of gun propellant. DEVCOM is currently beginning the process of transitioning propellant production from traditional manufacturing techniques to additive manufacturing. They are currently operating with additive manufacturing on an experimental lab-scale, and hope to expand to a fully functioning additive manufacturing facility in the future. During this project, the team worked with DEVCOM, the Advanced Robotics for Manufacturing (ARM) Institute, and the University of Pittsburgh Industrial Engineering faculty to meet the needs of the client and create solutions for a future DEVCOM additive manufacturing plant. The team developed a dynamic file for throughput calculations and size requirements of the facility, a high-level facility layout that meets DEVCOM’s desired daily throughput of 20,000 lbs, and a recommended implementation plan to incorporate automated material handling methods. The facility layout includes individual bays that contain multiple printers surrounded by cement blast walls to minimize the safety risks of manufacturing explosives. Our automation findings include the use of automated guided vehicles (AGVs) to transport printer build plates and a recommendation to collaborate with ARM to conduct research and development for automation of the printer vat refill process. These advancements and recommendations ensure that DEVCOM will remain at the forefront of armament technology for the US government, and the future transition of production can be smoothly made to both additive manufacturing and automated material handling.
3rd Place in Senior Design Expo
Project Summary
UPMC Shadyside admits approximately 45 patients daily. Every patient is seen by the admissions team at the start of their care, and the discharge team throughout their stay. Currently, there is limited communication between admissions and discharge employees. This can result in patients being discharged late, causing financial burden to the patient and hospital. Additionally, UPMC currently uses Cerner as their information system, but will be switching to Epic in 2025. Because of this change, UPMC wants to seize this opportunity to evaluate their current processes and implement a centralized model of care to ensure efficient communication between admissions and discharge.
The team conducted a review of healthcare literature and UPMC’s existing protocol documentation to understand the responsibilities performed by each team. The team also conducted interviews with various stakeholders, including an admissions nurse, discharge planners, a hospitalist, a patient, an Epic representative, and the client. After mapping the current state and adding recommendations, we received feedback on our initial findings. From there, we implemented improvement recommendations and developed the centralized model of care.
Proposed process improvements include reviewing patient allergies at admissions, identifying a preliminary discharge plan, and creating a database for post-care patient facilities. Technology improvements include adding a miscellaneous note section to increase visibility of patient condition, alerts for changes, and role-based editing permissions.
Upon the completion of the model of care, we conducted a SWOT analysis and sought out client feedback on the process. We expect the benefits of these changes to enable better communication of patient information between admissions and discharge, facilitate more efficient decision making, reduce the discharge process time, and make regularly needed information more accessible.
Project Summary
The Day of Admission Surgery Unit at UPMC Shadyside is a surgical unit which admits patients and conducts surgery on the same day. Rapid Recovery patients are patients who are discharged as well on the same day as their procedure. Of orthopedic DAS patients at Shadyside, which the team focused on for this project, 35% of Rapid Recovery patients fail to be discharged on the same day. The team aimed to develop strategies to decrease this proportion and help more patients return home on the same day.
To better understand the current state, the team conducted interviews, shadowed at Shadyside, and conducted data analysis on patient data from the past year. With this data, the team developed six machine learning models classify patients as good or bad candidates for Rapid Recovery based on their demographic and intrapersonal characteristics. The trends found in constructing these models, along with findings from a literature review conducted by the team, served as the basis for the development of patient selection guidelines for the program.
Using the qualitative findings from data collection, the team created a set of adaptive guidelines to correct process shortcomings and guide best practices moving forward. These recommendations include reviews of anesthesiology and physical therapy processes, paperwork streamling on multiple fronts, and a medication review among others. The team also developed, using figures received from Dr. Adolph Yates, an economic analysis tool to justify staffing increases in the unit, which the team found would increase system capacity and throughput through various forms of analysis.
Project Summary
Introduction
PPC Pitbulls is a digital marketing company that specializes in e-commerce marketing. The company utilizes the power of Google, YouTube, and Meta for ad management, while also placing a strong emphasis on email marketing and data analytics. The company primarily utilizes LinkedIn to draw in clients with content posts that would attract a client searching for a way to market their company more effectively that can be done through PPC Pitbulls services.
Problem Statement
There is too much time and money spent on the content creation process deemed inefficient by PPC Pitbulls. There is a lack of structure which decreases the number of potential clients to interact with.
Tasks Explained
The daily content creation process of LinkedIn posts is what the team was tasked with improving. This process is defined by a certain type of post getting put on LinkedIn from every day from Tuesday-Friday. Each post describes a different subject about the company itself or the marketing industry. The team improved the efficiency of creating these posts, allowing PPC Pitbulls employees to have more time in their schedules for other activities.
Results, Recommendations, and Conclusions
The results were obtained through lean processes and root cause analysis, along with data crawling utilizing Google API. Tools such as fishbone diagrams and the 5 Whys were used. The team recommends implementing a standardized content creation process utilizing AI, including ChatGPT for initial drafts and content categorization. Also, enhancing promotional strategies by using data analytics to identify peak engagement times and trending topics for content focus and continuous monthly review cycles with room for adjustments based on analytics. Final conclusions suggest that this new content schedule can improve the efficiency of PPC Pitbulls content creation process if implemented.
Project Summary
FedEx Ground is a package delivery company covering 220 countries and territories with more than 2000 operating facilities across the world. Currently, their operating facilities use barcodes to track and move packages throughout the network which require manual scanning. Physically touching every package, as one facility handles tens of thousands every day, consumes a lot of time. The current process is not at peak efficiency. In order to improve the system used now, we recommend FedEx to implement radio frequency identification (RFID) technology, which is a wireless technology using a tag and receiver to “scan” each package. RFID does not require physical touch and can scan multiple packages at once.
In order to accurately support our recommendation for FedEx to replace their current system at the North Pitt facility with RFID, we researched RFID technology, pros and cons to the system, and conducted a market analysis with companies who already implemented RFID. Furthermore, we conducted a quantitative throughput comparison and processing time analysis. From the results, we confidently concluded it would be in the best interest of FedEx Ground to implement RFID into their operating facilities. We created models from our cost analysis and payback period estimation that can be used at each facility. Lastly, we will offer an RFID placement plan that includes which RFID product to use, where to place it, and key performance metrics to track ongoing measurements post-implementation. By leveraging these tools and insights, FedEx Ground facilities can make informed decisions, measure costs, and track improvements in operational efficiency and performance, ensuring long-term success and competitiveness in their respective markets.
Project Summary
To address maintenance inefficiencies in dieset at Kennametal Inc., a top metalworking company, this project sought to improve the lifespan estimation of dieset parts using detailed statistical analysis. Traditionally, operators estimated maintenance times based on their experience and inspect in every 5000 pieces, which led to variability and more scrapped products, affecting profits.
We conducted a comprehensive data analysis on production records from 20 pressing machines, focusing on the number of units produced between required maintenance for top and bottom punches. Employing statistical methods such as Mean and Median, Histogram and Goodness-of-Fit Tests, we successfully quantified the lifespan of top and bottom punches. Additionally, we utilized Fourier Transform Analysis to investigate the periodicity of scrap production, which directly influences maintenance timing.
The analysis revealed distinct lifespans for different dieset components, with top punches lasting an average of 20,342.8 cycles and bottom punches 22,579.3 cycles before degradation impacts production quality. Our findings led to a significant revision of the maintenance schedule, recommending inspections at every 12,048 units produced. This schedule adjustment was based on a failure probability analysis, which showed a failure probability of 27.67% for top punches and 20.10% for bottom punches at this interval.
Project Summary
Our team, consisting of IE students from the University of Pittsburgh, partnered with Alstom, a global leader in rail transport and engineering, to address inefficiencies in the electrical and mechanical subassembly process at the West Mifflin, Pennsylvania facility. The focus of our project was to improve workflow and space utilization in the subassembly department, which constructs critical components for airport rail systems.
The challenges identified included inadequate space allocation, inefficient materials, and information flow, and frequent misidentification of work-in-process (WIP) as finished goods. These issues contributed to reduced productivity. Our methodology integrated lean Six Sigma and the 5S methodology, emphasizing continuous improvement and more effective layout of subassembly.
Employing Systematic Layout Planning (SLP), our methodology centered around three aspects: space requirements analysis, activity relationship mapping, and process improvement based on the 5S framework. We assessed the physical space to ensure adequacy for the tasks and implemented an Affinity Analysis Diagram to evaluate the proximity needs between different departments. The Miami assembly department, for instance, was identified with a "U" relationship (Unnecessary) to other departments, suggesting no need for proximity, which guided the redesign of the layout to reduce unnecessary interactions and enhance efficiency.
Significant enhancements were proposed through targeted 5S improvements, derived from direct observations and meticulous notetaking during facility visits. These improvements focused on implementing and optimizing visual management tools to aid in the identification and status tracking of subassemblies, thereby reducing errors and improving communication across teams.
Preliminary results demonstrated a potential for a 49% improvement in layout efficiency and a significant reduction in the distance between key workstations. (Reduced by 57.48%, 28.19%, 45.35% respectively.)
This project not only aims to enhance operational efficiency at Alstom but also serves as a model for integrating systematic layout planning and lean management in complex manufacturing environments.
Project Summary
IDL is a subsidiary of Matthews International located in Butler, JSON PA, where the printing house serves printing commercials for the big retail businesses. They have issues with the usage of the press most of the time incurring costs together with operational effectiveness. This project aimed to evaluate and improve IDL's three large-format digital presses, focusing mainly on the 2500, since it has the lowest average utilization.
On the other hand, the historical data emphasized the pressing need to increase the adjusted utilization from 47% to a target of 50% in the short term and 57% in the long term, at the same time aiming for the extension of actual operational hours by 850. This method involved a mixture of Lean Six Sigma and actual observations that helped in the identification and mitigation of activities that add no value and inefficiencies in the pressing process.
Some of the interventions conducted included the application of the Single Minute Exchange of Dies (SMED) technique in the area to reduce setup times and the suggestion of a restructured warehouse layout to optimize the handling of materials. The project also aimed at standardizing the operational procedures by the shifts in order to realize uniformity and efficiency. Preliminary quick-win improvements, which produced immediate positive impacts, set the stage for more extensive process modifications. The included matrix covered in detail, showing impact versus difficulty; it will help guide future enhancements: the redesigned process of reloading the presses and strategic recommendations on changes in the layout of the warehouse.
The expected outcome from such interventions is raised press utilization, reduction of operational costs, and a general boost to the production efficiency levels. This means that in the process, this IDL project will add to the current thrust of the IDL that operations are facilitated with significantly higher productivity without any further capital investment in new printing equipment, which is meant to enhance customer satisfaction.
Project Summary
Our project this semester was to help UPMC find ways to reduce the average amount of time it takes to turn over an operating room. A turnover is the transition period from one surgery (cleaning up the room) to the preparation for the next surgery done by EVS (Environmental Service is responsible for the bulk of the turnover process). In 2023, UPMC Presbyterian completed 18,116 surgeries and UPMC Montefiore completed 5,081 surgeries. A turnover occurs between every surgery, and it costs $100/minute to turn over a room. UPMC Presbyterian’s average turnover time is 55 minutes and UPMC Montefiore’s average is 40 minutes. The UPMC’s goal is to reach the following timings: Presbyterian at 35 mins and Montefiore at 30 mins. We approached/analyzed the problem by conducting a Root Cause Analysis and Value Stream Map.
The first method we used is a root cause analysis which is a problem-solving method that identifies and addresses the root causes of underlying issues found. We conducted a root cause analysis for each department involved in the process to find where underlying issues occurred and offered UPMC recommended solutions. The second method we used was a value stream map that outlines current state operations identifying key performance measures in processes. In the Value Stream Map, based on their current turnover process, we identified value added and non-value-added steps. This helped guide us in understanding where potential delays occurred during the transition periods.
A few solutions we recommended at UPMC were the following: improving teamwork in the OR, communication/scheduling recommendations, implementing kaizens, and training to employees in case of EVS call outs.Implementing a plan in a complex and demanding environment will require proper planning, so it will be some time before we see some results to meet the goal timings.