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Industrial Engineering


Lean Manufacturing Implementation

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Sponsor: Palomar Modular Buildings

Student Team: Miranda Marlowe, Fritz Pawelka, Dayanna Pena Lopez, Matthew Saldana

Faculty Advisor: Mr. Jerel Walters

Palomar Modular Buildings (PMB) has a large building manufacturing facility in DeSoto, TX. They design and manufacture advanced modular buildings for a range of industries. Their modular construction method creates significant cost savings and quickly produces buildings in a fraction of the time compared to conventional construction.  Materials movement is constant, the factory floor is congested, and some materials inventory is located far from its point-of-use. As part of a move toward Lean Manufacturing methodologies including Just-In-Time, PMB needs a material flow analysis including a spaghetti chart on its most used items in order to optimize their movement and storage with an emphasis on the elimination of wasted movement.  That analysis should include a Kanban developed to visually signal the need to reorder those inventory items as they are moved from storage to their point-of-use and to signal the need to refill point-of-use from storage.


Scheduling the Dock Operations at the HEB Service Center

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Sponsor: H-E-B

Student Team: Joe Craig, Kyle Hermiller, Kelsey McLoad, Isaac Salazar

Faculty Advisor: Mr. Jerel Walters

H-E-B operators pick the stock keeping units (SKUs) contained in work assignments from multiple locations in the distribution center. When a work assignment is completed, the resulting pallets are transported to the loading docks. Pallets are delivered to their work assignment’s designated docking area. Pallets are hold in a temporary buffer located within the assigned docking area, where they wait to be loaded into a truck. Due to the disparity in order picking times, pallets are currently experiencing long times at the loading docks waiting for the formation and completion of a full truck load.


Hot Test Optimization and Future State Projection

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Sponsor: Caterpillar, Inc.

Student Team: Garret Hohman, Carolina Gambarte, Nicholas Broussard, Tanner Thomas

Caterpillar is known worldwide for its heavy-duty machinery with sales that span across the globe. In 2010, Caterpillar opened a new facility in Seguin, TX, where the company produces, using both manual and automated processes, a wide range of engines such as the C7.2 and the C18.

This presentation will explain the use of computer simulation methodologies integrated into lean manufacturing studies. The application is in the engine hot testing process, which is identified as one of the system bottlenecks. The goal of this project is to remove the non-value added activities from testing as to reduce both the process throughput time and lower the utilization of the testing equipment. The presentation will show how the data was collected and analyzed to build usable probability models used for characterizing the testing operation, as well as how the simulation model was built in Arena in order to evaluate the capacity of the line after conducting the lean manufacturing study.


Material Flow Analysis at Continental Automotive Systems

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Sponsor: Continental Automotive Systems

Student Team: Eugene Alvarez, Ashley Dawson, Thomas Hajduk, Mason Reichenau-Kuhlmann

Continental Automotive Systems is a German-based company that manufactures multiple automobile components with its main focus being in microchip panels and sensors. Continental is one of the leaders of Industry 4.0 in Texas.

This project will demonstrate the application of material flow analysis for the implementation and optimization of a new Automated Guided Vehicles System (AGVS). Autonomous transportation can be vital to increase efficiency in delivering the material and ultimately lead to higher profits when accentuated properly. Currently, material movement is frequent in the Continental’s production lines, which is causing congestion in the plant’s hallways. This project will show how to use practical simulation-based optimization approach to reduce average delivery times while finding the best number of transporters for current and future demand scenarios.


Pack Process Optimization

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Sponsor: Continental Automotive Systems

Student Team: Christopher Arisco, Makenzie Corley, Joshua Goundrey, Mark Thelen

Continental Automotive Systems is a German-based company that manufactures multiple automobile components with its main focus being in microchip panels and sensors. Continental is one of the leaders of Industry 4.0 in Texas.

The Advanced Driving Assistance System (ADAS), manufactured and assembled at Continental Seguin TX, accounts for twenty-five percent of production and this product is predicted to double in size in one year. The company would like to study via computer simulation the relocation of the bracket cell machine – a potential bottleneck - to the packing area and evaluate gains in process efficiency. In its current state, the facilities added new pack lines due to the growing demands, but it is anticipated that these processes do not perform near optimal conditions. By optimizing the material flow and capacity in the pack department, it is expected that the utilization of the bottleneck will decrease, thus the company will be able to meet customer demand requirements.


Using Dynamic Demand Information for the Storage of SKUs at the HEB Service Center

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Sponsor: H-E-B

Student Team: Mason Askins, Samuel Mendicino, Antonio Rodriguez

The HEB distribution service center distributes its goods to all the HEB grocery stores in Texas and Mexico. Distribution centers are storage facilities designed to retain inventory as a reserve for their stores. They protect from the inability to face uncertain demand in a timely fashion and help reduce the risk of being out-of-stock. This center distributes predominantly non-perishable goods via picking, creating a palette, and loading the palettes onto the trucks. Optimizing picking performance through responsiveness addresses roughly 40-60% of the total costs.

This project will demonstrate how a powerful analytics tool such as data mining combined with computer simulation can allow the HEB DC improve number of cases moved per hour, a critical performance metric. The project will find an optimal product storage location using association rules algorithms for a more efficient way to pick up orders from their storage racks. The primary focus will be on the pick-to-belt system. Results will include recommendations on how to load/deliver the totes to the palletizers, and making changes to the conveyor belts layout.


Fab Kanban

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Sponsor: Philips Lighting

Student Team: Matthew Garcia, Erick Mondragon, Andrew Nelson, Haydon Reese

Philips Lighting in San Marcos, TX, manufactures LED lights for outdoor structures and areas, with their top seller being the Pureform streetlight. Their $52.7 Million facility is 240,150 sq. ft. and currently employs 271 workers.

This project will show how the optimization and management of workstation setups can increase production flexibility and reduce throughput time. Phillips’ assembly workstations undergo frequent setups since there is a high variety of product types that need to be assembled. Due to the long setup times, lots of time is wasted by repeatedly setting up for small-batch, high-usage parts. Consequently, the number of parts between setups for each product family will be determined using Factory Physics (FP) models for highly stochastic demand conditions. In addition to FP models, Data mining tools will be used in this project to conduct data-intensive analysis on the databases containing the usage and setup time data for the wide variety of product offerings available in the company’s portfolio.


Warehouse Layout Optimization

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Sponsor: Philips Lighting

Student Team: Justin Eberly, Matt Garza, Karina Yanez

Philips Lighting in San Marcos, TX, manufactures LED lights for outdoor structures and areas, with their top seller being the Pureform streetlight. Their $52.7 Million facility is 240,150 sq. ft. and currently employs 271 workers.

The order pick time of components from the warehouse takes a long time. In this project, a novel data-mining algorithm, called association rules, will be programed in R statistical software to create a grouping of components based on how items are ordered together from the warehouse. Then, a mathematical program will be applied in order to assign the components to storage locations. It is expected that by applying this methodology, the order picking time will be reduced by 100 seconds.


Improving Jig Cleaning and Conveyor Processes

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Sponsor: Toyota Motor Manufacturing Texas

Student Team: Luz Garcia, Chad Mitchell, Maria Salazar, Andrew Wolan

Located in San Antonio, TX, Toyota Motor Manufacturing Texas is the only facility in the world that manufactures the Tundra. The facility also manufactures the Tacoma.

Painting is one of the most critical departments in this facility. The use of secondary painting resources and tools during the painting process simplifies the tasks performed by the painters. Painting jigs, considered in this project as secondary or auxiliary resources, are temporarily mounted in several locations of the vehicles to facilitate the painting task especially in hard-to-access spots of the vehicle (hood, trunk, doors, etc.). Thus, these auxiliary resources are contributors to lower painting time only if they are delivered to the assembly point-of-use workstation before they are needed. Otherwise, if jigs arrive late, the process could experience an increase in throughput time. The painting department is currently experiencing a three-minute delay and the engineers believe that the main cause for this delay is due to untimely delivery of jigs. In this project, Lean Manufacturing methodologies will be applied in order to identify and remove the non-value added activities associated with the handling and delivery of these resources.


Y.Cube: Manufacturing, Assembly, Testing Improvements

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Sponsor: Younikos

Student Team: Dane Bumgardner, Nicholas Fuentes, Jasmin Haroldson, Chase Whitlock

Younicos has recently moved into a new facility in Austin, Texas; the company has released a new product: the Y.Cube. This product is a self-contained, plug-and-play battery storage system that can be deployed to nearly anywhere in the world. The Younicos team is in the process of developing assembly and testing procedures for the Y.Cube product with the goal of increasing production numbers and quality of the product. Younicos wants to understand the maximum throughput of the new facility in regards to the Y.Cube product. Through determining the maximum amount of Y.Cubes that can be produced, Younicos is also interested in the ideal layout of the facility. Younicos seeks a documented throughput time of the product, a material handling and line side delivery analysis, incoming quality inspection plan, and any possible process improvements to the system assembly and testing. By understanding these different areas of the manufacturing and assembly process, the Younicos team hopes to optimize the production of the Y.Cube within the Austin Facility.