Capstone Experience
Each academic year, industrial engineering seniors participate in the Industrial Engineering Capstone Experience as part of teams of four or five students. Every student pursuing the Bachelor of Science in Industrial Engineering and Operations Analytics at the University of Arkansas is required to complete the two-semester course sequence. Richard Cassady, university professor of industrial engineering, coordinates the Industrial Engineering Capstone Experience courses. The teams are matched with an industry partner and led by a student team leader.
In the fall semester, teams begin by getting to know their industry partner and the issues motivating the project. Students perform preliminary analysis and define objectives for their spring semester work. In the spring semester, the teams apply their industrial engineering and operations analytics skills to achieve their project objectives. They assess the potential impact of their work and create the deliverables that their industry partner need to implement their work.
The teams present their work at the Capstone Symposium in the spring, just before graduation. Industry partners and faculty mentors are recognized for their support of teams, and top teams and students receive awards in several categories.
The Department of Industrial Engineering at the University of Arkansas needs industry partners to support senior capstone projects. Each academic year, teams of four or five seniors will be assigned to one of the projects with one student serving as the team leader. The experience is coordinated by Richard Cassady (cassady@uark.edu), University Professor of Industrial Engineering, and students also receive technical advice from all industrial engineering faculty.
Formation of Teams
Dr. Cassady uses an application process to select the students who serve as team leaders. An auction-type process is then used to match each team leader with a project. Dr. Cassady assigns the remaining students to teams. Each team makes initial contact with their industry partner no later than the first week of September.
Intellectual Content of Projects
To provide a more specific description of the content of the capstone experience, nine specific capstone experience outcomes are used to guide the activities that are part of the experience.
Although every project is different, and all projects have evolving requirements:
- Each team will describe the mission of their industry partner organization. For large organizations, the team will describe the mission of the segment(s) of the organization that is of specific interest to the project.
- Each team will describe: (a) the concerns of their industry partner organization that are motivating their project; and (b) any formal and/or ad hoc processes, procedures, policies, etc., that are currently in place and are related to the concerns motivating their project.
- Each team will: (a) identify and/or define one or more metrics that can be used to determine their success in addressing the concerns motivating their project; and (b) attempt to assess and evaluate how their industry partner organization currently is performing relative to the project-success metrics.
- Each team will make recommendations for addressing the concerns motivating their project and use the project-success metrics to assess and evaluate these recommendations.
- Each team will provide deliverables to their industry partner organization that facilitate the implementation of their recommendations.
In achieving capstone experience outcomes 1-5:
- Each team will use project management software to track and document their activities and hold frequent meetings to receive support and feedback from their industry partner organization and the faculty coordinator.
- Each team will: (a) make use of the academic literature related to their project; and (b) learn and apply industrial engineering and operations analytics skills beyond their required BSIEOA coursework.
During each semester:
- Each team will communicate their findings in: (a) well-organized and well-written documents; and (b) well-organized, well-rehearsed and effective oral communication.
Throughout the experience:
- Each student will demonstrate professionalism and effective teamwork.
Expectations of Industry Partners
A successful capstone project requires extensive and timely support from the industry partner. The industry partner is expected to provide the team with a point of contact (and a backup point of contact) who can meet with the team (in-person or remotely) at least every other week to help the team identify and interview stakeholders, obtain data, assess the validity of their analysis, review course milestones and project deliverables, etc. The industry partner is also expected to cover any travel expenses incurred by the team during the project and to participate in the team’s final presentation near the end of the project.
Industry partners are expected to provide project ideas that are consistent with the department’s expectations regarding technical content. Historically, meeting teams’ data needs has been the most difficult expectation for industry partners to meet. Industry partners should consider the availability of real and relevant data when submitting project ideas. While the department is not opposed to teams collecting data as part of their project activities, the preference is that the majority of project activities related to data be focused on analysis rather than collection. Specifically, the preference is that projects require teams to deal with large, messy, data sets.
Limited scope also has been a challenge associated with some past projects. For example, developing a forecasting tool or developing a software tool to automate a manual process are worthy project activities. However, neither is enough on its own to meet the department’s expectations for a capstone project. The department wants students to take the next step to understand how the predictive model or the software tool impacts strategic or tactical decision-making.
Dissemination
Once a potential industry partner expresses interest, issues related to non-disclosure and intellectual property will be addressed via a separate email communication. When necessary, the university and the industry partner enter into an agreement that covers non-disclosure and intellectual property. The agreement covers all university faculty and staff. Student teams sign a read-and-acknowledge form related to this agreement.
The audience for almost all capstone experience documents and presentations is limited to the industrial engineering faculty and staff, the students in the capstone experience and the industry partner associated with the project being discussed. However, there are a few public activities associated with the capstone experience. First, teams make a final, fall semester presentation that is open to the entire industrial engineering student body. The industry partner has the responsibility of approving any information that their student team disseminates in this presentation.
Second, the experience concludes with the Industrial Engineering Capstone Symposium in late April or early May. At the symposium, which will be open to the public, teams participate in an interactive poster session and deliver detailed technical presentations about their projects. The industry partner has the responsibility of approving any information that their student team disseminates at the symposium.
Third, at least one team will be selected to compete for the Institute of Industrial and Systems Engineers (IISE) Senior Capstone Design Award. To enter this competition, each selected team submits a written executive summary, a poster and a set of presentation slides regarding their project.
Typical Milestones
The following list summarizes the major milestones associated with the experience. Additional details are provided to industry partners throughout the experience.
| Early September | Teams contact industry partner for the first time |
| Early November | Industry partner submits team assessment 1 |
| Late November | Industry partner submits team assessment 2 |
| Late November | Industry partner approves fall, public presentation content |
| Early December | Teams make fall, public presentations |
| Mid-February | Industry partner submits team assessment 3 |
| Mid-March | Industry partner submits team assessment 4 |
| Mid-March | Industry partner approves team’s symposium website material |
| Mid-March | Industry partner agrees to list of final project deliverables |
| Late April | Industry partner submits ABET assessment |
| Late April | Industry partner approves public symposium content |
| Late April | Industry partner approves final project deliverables |
| Late April | Annual IE Capstone Symposium |
Submission of Project Ideas
If you are interested in being one of our industry partners, please contact Richard Cassady at cassady@uark.edu. Dr. Cassady is willing to meet with you (in-person or remotely) to discuss the capstone experience in more detail. Each year, our goal is to have our industry partners identified by mid-June.
8th Annual Industrial Engineering Capstone Symposium
Arkansas Union Verizon Ballroom, University of Arkansas, Fayetteville
Wednesday, May 1, 2024
Forecasting Backlog of Cross-Dock Operations using Regression and Machine Learning Algorithms
Student Team Leader: Marshal Ray
Other Student Team Members: Maryanne Attee, Jacob Dixon, James Jett and Seth Turner
Industry Partner: ABF Freight
Industry Partner Contacts: Bradley Taylor, Lead Operations Strategy Engineer; Chad Treadaway, Senior Manager, Engineering
ABF Freight, a subsidiary of ArcBest, excels in nationwide less-than-truckload (LTL) shipping, with operations based in Fort Smith, AR. Its vast network includes 10 distribution centers and 240 service centers, handling approximately 20,500 daily shipments in 2023 (8K released in February 2024). ABF Freight is known for providing efficient and cost-effective shipping solutions. However, ABF Freight faces complexities in managing its cross-dock operations due to factors such as equipment availability, labor, freight profiles, and fluctuating demand, which pose logistical challenges at these hubs. We primarily focused on addressing fluctuations in demand. ABF Freight utilizes a metric called Days-To-Current (DTC) to assess how swiftly a station can clear its inventory. Following an analysis of extensive freight data, we explored various regression and machine learning techniques to forecast DTC five days into the future. Subsequently, we developed a Python-based decision support tool incorporating our most successful models. This tool generates a forecasted DTC value for each distribution center for the next five days, empowering ABF Freight to make preemptive adjustments to their network, potentially reducing backlogs, minimizing costs, and optimizing profitability.
Designing a User interface with Excel VBA to Generate Rate Increases for Existing Low-Risk Accounts
Student Team Leader: Ellen Timmerman
Other Student Team Members: Natalie Hernandez, Kaden Jones and Elias Theodore
Industry Partner: ArcBest
Industry Partner Contacts: Drew Caple, Manager of Pricing and Supply Chain Analytics; Erin Hills, Logistics Engineer II
ArcBest, headquartered in Fort Smith, AR, is a five-billion-dollar integrated logistics corporation with multiple subsidiaries. ABF Freight is by far the largest of their subsidiaries, accounting for over 60% of ArcBest’s annual revenue, and specializes in less-than-truckload shipments. While ABF has standard base rates available to any customer, preferred customers may negotiate personalized pricing structures. Each customer's pricing undergoes an annual review to ensure profitability, with adjustments made based on market changes and cost fluctuations. However, the process of evaluating and adjusting pricing for smaller customers can be disproportionately time-consuming for pricing analysts compared to the revenue generated from those accounts. To address this, we aim to streamline rate increase tasks for smaller accounts by developing a decision-support tool with an intuitive user interface that automatically generates rate increases, considering past account performance. Using the ROMC framework for specialized decision support systems, we created our tool in Excel's VBA. This interface enables analysts to quickly make informed decisions about each rate increase task by assessing an account's historical profitability and predicted negotiation style.
Improving Radiology Productivity by Optimizing Planned Staffing Levels and Supporting Real-Time Adjustments
Student Team Leader: Karleigh Eoff
Other Student Team Members: Charles Eason, Weston Harp, Du'Maurier Looney and Joshua Peetoom
Industry Partner: Baptist Memorial Healthcare Corporation
Industry Partner Contacts: Katie Parker, System Director for Human Resources
Headquartered in Memphis, Tennessee, Baptist Memorial Healthcare Corporation (BMHCC) is a network of hospitals and healthcare facilities that serve the Mid-South Region. We are working with BMHCC’s Women’s Hospital’s radiology department in Memphis, TN. The radiology department struggled to meet productivity goals set by BMHCC in the 2023 fiscal year. We are focusing on the staffing of radiology technicians to improve the department's productivity. Currently, there are baseline staff levels for each type of technician (MRI, CT, Ultrasound, and X-Ray) for each day of the week, but these baseline numbers can be adjusted throughout the day by managers if needed. First, we created an optimization model to adjust the baseline staff levels for each type of technician in the department. The model minimizes labor hours of the technicians while meeting demand and labor requirements set by BMHCC. We created a decision support tool to aid in decisions regarding staff levels throughout the day. This tool allows the managers to get an estimate of what productivity is currently and how adjusting the current staff level will affect productivity.
Applying Markov Decision Processes to Optimize Aggregate Workforce Planning with Uncertain Funding Conditions
Student Team Leader: Blake Sooter
Other Student Team Members: Katie Augsburger, James Fite, Nathanael Harris and Deniz Vural
Industry Partner: Infinity Labs LLC.
Industry Partner Contacts: Stephen Sieck, Division Lead, Transformational Technologies; David Hillstrom, Ph.D., Simulation Scientist
Founded in 2020 with 7 employees, Infinity Labs is an innovation-focused firm that fosters an entrepreneurial mindset to solve the world’s hardest problems. Located in Dayton, Ohio, Infinity Labs specializes in modeling and simulation work, primarily in the defense sector. Accelerating growth at the company has led to a litany of challenges relating to hiring and staffing, and Infinity desires a more robust quantitative component to their decision-making process for hiring new technical staff. Our team has improved Infinity’s full-time equivalent (FTE) need forecast by better accounting for uncertain inputs using Monte Carlo simulation. We then use this estimate to optimize sequential hiring decisions, which we’ve modeled as a Markov Decision Process. We formulated our model such that the observable state is the gap between actual and needed FTEs at a given step in time, which then prescribes an aggregate hiring action to the decision maker. Our team is developing a decision support tool in Python that will include Excel input/output of project data, the ability to modify assumptions such as the possible hiring decisions, distribution assumptions, discount factor, etc., and the ability to measure changes’ impact on model results.
Delivery Zone Definition with K-Medoids Clustering and Integer Programming
Student Team Leader: Rachel Thomas
Other Student Team Members: Zayna Abu-Safe, Bennett Foret, Trent Sawyer and Luke Smith
Industry Partner: J.B. Hunt
Industry Partner Contacts: Reid Nelson, Manager II, E&T; Brett Phillippe, Sr. Logistics Engineer
J.B. Hunt is a Fortune 500 transportation logistics company that provides shipping and carrier solutions to its customers. With over 35,000 employees, J.B. Hunt has five business units, including Final Mile, who specializes in delivering products to customers via freight. J.B. Hunt Final Mile defines delivery zones for several of its customers to reduce their weekly mileage. A delivery zone is a group of zip codes serviced on specific day(s) of the week. J.B. Hunt Final Mile Engineering’s challenge is that its current process of creating delivery zones is a manual, guess-and-check process that is time-consuming and does not guarantee the best zone definition for reducing route mileage. To address this, we developed a decision-support tool that uses Python to automate and improve the process of creating delivery zones. Using K-Medoids clustering and an integer program, we devised a comprehensive solution to streamline delivery zone creation. We determine the number of zones to create for a customer account, assign zip codes to zones, and assign delivery days to zones. The primary goal is to create zone definitions where workload and daily volume are balanced throughout the week and average weekly miles per stop for customer accounts are reduced.
Allocating Truck Yard Resources and Reducing Bobtail Miles
Student Team Leader: Kate Bowden
Other Student Team Members: Hector D. Santillan, Mackenzie Frederick, Riley Baker and Santiago Schrader
Industry Partner: J.B. Hunt Transport Services, Inc.
Industry Partner Contacts: Jake Wofford, Vice President, Intermodal Operations; Trevor Rogers, Director II, Operations and Brandon Workman, Logistics Engineer II, West Dray Operations
J.B. Hunt Transport Services, Inc., a Fortune 500 company headquartered in Lowell, Arkansas, provides trucking and logistics services across five business units. The Intermodal business unit utilizes a network of five truck yards and the BNSF Railway to deliver freight in the Los Angeles, California, region. J.B. Hunt is concerned that they are driving too many "bobtail" miles, which occurs when there is no trailing equipment attached to the cab of a truck. Bobtailing typically occurs at the beginning and end of a driver's shift, driving between a truck yard and a rail yard. J.B. Hunt recently purchased two new truck yards in Los Angeles and believes the properties' proximity to the rail yards can decrease bobtailing. We developed an optimization model that minimized bobtailing by assigning trucks to yards, giving us a baseline recommendation for the resource allocation at the new truck yards. For the remaining space at the truck yards, we created a VBA decision support tool allowing users to rank the relative importance of dedicating space for trucks, trailers, containers, and office space at each new yard. The tool performs a multiple objective decision analysis, outputting the recommended amount of equipment to store at each yard.
Improving Table-Pack Work Cell Efficiency using Intrinsic Motivation and Lean Manufacturing Principles
Student Team Leader: Gabe Hesington
Other Student Team Members: Joshua Jowers, Caleb Morris and Mitchell Cooksey
Industry Partner: Marco Group, Inc.
Industry Partner Contacts: Richard Davidson, Chief Executive Officer, Co-Founder
Marco Group, Inc. is a manufacturer of classroom furniture that is headquartered in Neosho, MO. They employ about 65 people and generate around $20 million in annual revenue. Marco uses cellular manufacturing to produce a wide array of desks and tables, while outsourcing production of chairs and storage carts. The last work cell that all tables go through is called Table-Pack, where each tabletop is inspected, cleaned, given support braces if needed, and packaged. We discovered that Table-Pack’s performance was substantially lower than Marco’s standardized goals. We attributed this to several factors including lack of standard practices, Parkinson’s law, and a lack of employee motivation. We have outlined a series of standard practices using Standard Operating Procedures (SOP) documentation. These documents outline safe work standards covering cell layout, work instructions, and lean manufacturing techniques. These documents lay a foundation for improved process observation and future improvements. To address Parkinson’s law and employee motivation, we built an Operator Dashboard that gives workload visibility and specific goals to the Table-Pack operators. The goal of the dashboard is to give operators intrinsic motivation which, based on our research, increases productivity.
Improving Chicken Breast Production Scheduling through Pareto Optimization with VB.NET
Student Team Leader: Addison Standridge
Other Student Team Members: Carter Baldwin, Kevin Connelly and Joshua Smith
Industry Partner: Simmons Prepared Foods
Industry Partner Contacts: Turner Vance, Director Continuous Improvement; Ross Vandever, VP Continuous Improvement; Kerry Bartholomew, Senior Continuous Improvement Manager
Headquartered in Siloam Springs, Arkansas, Simmons Foods, Inc. and its associated enterprises are primary providers of poultry, pet, and animal nutrition items. Simmons Prepared Foods ranks among the top 15 poultry producers in the United States and operates as a division of Simmons Foods within the foodservice industry. A variety of people, starting with the sales team all the way to the plant manager, give different inputs to generate a customer order schedule. This schedule begins with forecasts from the sales team and then each plant scheduler generates a schedule with these inputs for their respective facility based on long-term employee knowledge. Currently, there is no process during schedule building that incorporates the minimization of waste of value-added product, or trim. Our primary goal is to use multi-objective optimization to improve the production schedule for chicken breasts at Simmons’ Van Buren facility with an aim to enhance the output of value-added product while simultaneously reducing the tardiness of customer orders. We designed a Microsoft Excel Add-In tool using Microsoft Visual Basic .NET framework that allows Simmons to choose from multiple solutions on a Pareto frontier. This increases the flexibility of the scheduler’s decision, speeds up the time to generate a schedule, and incorporates trim reduction into the schedule building process.
Enhancing Automation Decisions using Power BI Analysis and Linear Programming
Student Team Leader: Lathan Gregg
Other Student Team Members: Ashwin Narayan, Austin Easley, Seth Rosenfeld and Sebastian Alborta
Industry Partner: Tyson Foods
Industry Partner Contacts: Maria Rene Arandia, Automation Analyst; Irving Polanco, Senior Automation & Robotics Engineer; Sean Lawrence, Senior Manager Automation
Tyson Foods, headquartered in Springdale, Arkansas, is a Fortune 100 multinational protein corporation serving consumers on five continents. Tyson divides their operations into three business units: Poultry, Prepared Foods, and Fresh Meats. Tyson’s automation team works across all three units to deliver automated solutions, helping reduce costs and eliminate ergonomic risks. Automation engineers analyze end of line data collected inside facilities to understand the volume and variety of products handled. At Fresh Meats facilities, non-uniform and decentralized data complicates this process, resulting in a tedious and incomprehensive ad hoc analysis. Our project goal was to streamline this process while providing a more complete analysis of facility data. To accomplish this, we created an ingestion process to load then structure data, and an interactive Power BI dashboard to support the comprehensive examination of a facility’s performance. To supplement this tool, we created a linear program to prescribe an optimal combination of palletizer options to implement at a facility.
7th Annual Industrial Engineering Capstone Symposium
Arkansas Union Verizon Ballroom, University of Arkansas, Fayetteville
Wednesday, May 3, 2023
Improving Starting Price Point for Customer Re-Negotiations using XGBoost
Student Team Leader: Abby Harris
Other Student Team Members: Zachary Oldham, Wesley Tate, Ernesto Serna and Jacob Boshears
Industry Partner: ArcBest
Industry Partner Contacts: Drew Caple, Manager - Pricing and Supply Chain Analytics; Erin Hills, Logistics Engineer II
ArcBest is a five-billion-dollar transportation and logistics company based in Fort Smith, AR. ABF Freight is ArcBest’s largest subsidiary and specializes in less-than-truckload (LTL) shipments, employing thousands of people. Roughly 50% of ABF’s clients use pricing structures that expire after one year, triggering a two-and-a-half-month re-negotiation process. This process is currently based heavily on an engineer’s experience or knowledge about a customer. The goal of our project is to find a way to streamline and standardize the beginning of this process by setting the initial price increase. Using past re-negotiation data provided by ArcBest, we grouped instances of customer re-negotiations into bins allowing XGBoost models to be built that predict the amount customers will negotiate. We then packaged these predictive models into an easy-to-use tool in R that allows an engineer to input customer account information and receive a starting point for their negotiations. This tool is expected to reduce the time it takes an engineer to complete the overall re-negotiation process.
Reducing Stock-outs in a Hospital Medication Dispensing System using Simulation Analysis
Student Team Leader: Carson Doss
Other Student Team Members: Armon Afrasiabi, Gabe Ellis, Will Plunkett and Dylan Deramus
Industry Partner: Baptist Memorial Health Care Corporation
Industry Partner Contacts: Katie Parker, Director of Performance Improvement; and Ben Eddlemon, Director of Pharmacy at DeSoto Hospital
Baptist Memorial Health Care Corporation (BMHCC) is network of hospitals spanning the greater Memphis area that started in 1912. Their network includes 22 hospitals with over 600 of the region’s leading specialists. Our project focuses on the Omnicell Medication Dispensing Cabinets within the DeSoto Hospital located in Southaven, Mississippi. BMHCC uses Omnicell cabinets to hold and dispense drugs to nurses and staff as needed. These Omnicell cabinets use different par levels for each medication stored within them. When a medication is at ‘par’, this means that the stock for the respective medication is full. However, a medication could completely stock-out, meaning its stock is at zero. BMHCC made it our focus to analyze different stock levels to work to minimize these stockouts. We created an Omnicell simulation in Arena. With our process analyzer, we were able to evaluate many combinations of par level versus demand levels. The results of the simulation populate a visual to prescribe par levels based on demand level and desired fill rate.
A Decision Support Tool to Automate and Optimize Contract Staffing using Linear Programming
Student Team Leader: Paris Joslin
Other Student Team Members: Nathan Skinner, Will Cunningham, Conner Oxford and Zach Leondike
Industry Partner: Infinity Labs LLC
Industry Partner Contacts: Stephen Sieck, Senior Program Manager; and Nick Marquart, Co-Founder and Chief Analytics Officer
Infinity Labs LLC is a cutting-edge innovation company headquartered in Dayton, Ohio, that specializes in utilizing innovative modeling and simulation, cybersecurity, and training techniques to solve the hardest problems in the national defense industry. Since the launch of the company in 2020, Infinity Labs has experienced rapid growth with employees located across the United States. Due to the increasing number of employees and the complex nature of defense contracts, the staffing process has become time-consuming and requires a significant amount of effort from program managers. Infinity Labs has expressed the need for an automated tool that alleviates time constraints on program managers and ultimately balances employee utilizations. Our team designed a Microsoft Excel decision support tool that analyzes employee qualifications and contract requirements. The goal of our optimization model is to minimize the variability in employee utilizations in order to create a balanced workforce. The tool utilizes AMPL to solve a linear programming model to produce optimal staffing assignments. Using our tool, program managers are able to post-process the optimal assignments to fit business needs for the future months.
Developing a Priority Assignment Policy for the Empty Planner Application
Student Team Leader: Bill Byers
Other Student Team Members: Jackson Barclay, Carter Christian and Joshua Walters
Industry Partner: J.B. Hunt Transport Services, Inc.
Industry Partner Contacts: Jessica Smith, Sr. Manager, Engineering & Technology; Sam Webb, Data Scientist, Engineering & Technology
J.B. Hunt Transport Services, Inc., is a Fortune 500 company located in Lowell, Arkansas, that provides a variety of freight transportation services for customers. J.B. Hunt has five different business units in operation: Intermodel (JBI), Dedicated Contacted Services (DCS), Integrated Capacity Solutions (ICS), Final Mile, and Truckload (JBT). For this project, we are focusing on supporting the decisions derived from an application developed by J.B. Hunt to aid dispatchers in the assignment of empty equipment throughout their daily operations. We did this by assigning numeric scores to each potential location to provide insight for where to assign drivers. We used Multi-Objective Decision Analysis (MODA) to understand the importance of different factors that are a part of the policy that we used to score each location and created a swing weight matrix to weigh the factors using stakeholder analysis. We reinforced the improvement of our metric of Unplanned Empty Moves with the use of simulation that created a baseline to show the current performance without the use of our policy. We then compared those results to another simulation that incorporates our policy to capture potential improvements. Finally, we created a VBA-based decision support tool that allows users to interact with the policy in real time to understand its outputs.
Linear Optimization for Driver-Route Assignment with Fatigue and Balanced Workload Considerations
Student Team Leader: Andrew Freeman
Other Student Team Members: Katy Emerson, Nicolas Alcoreza, Sarah Wilson and Emmanuel Jean Paul De La Gala Gomez
Industry Partner: LATROBE LLC
Industry Partner Contacts: Latanyua Robinson, President and James Robinson, Vice-President
Latrobe LLC, founded in 2010 and headquartered in Memphis, Tennessee specializes in manufacturing consulting, light assembly and packaging, and shipping operations as an Amazon Delivery Service Partner (DSP). Latrobe’s DSP division is responsible for hiring, training, and assigning drivers their daily routes. Currently, Amazon provides a random initial daily assignment of drivers to routes. Latrobe’s coordinator makes changes based on their knowledge to improve the assignment each morning. Routes have a wide range of difficulty and poor assignments can cause the dual problem of underworked drivers and fatigued drivers. Our goal was to build a model that automatically highlights changes to reduce fatigue by balancing the workload fairly among drivers. We defined a linear program that considers the number of packages a driver delivered in the past few days as a measure of the driver’s fatigue. The model minimizes the deviation between each driver’s workload and the system average. The linear program was defined in Python and displayed in a user interface in PowerBI. The optimization model and visuals in the dashboard also provide the company with great insight into past performance and future improvement recommendations.
Analyzing and Predicting Marketing Campaign Performance Using Regression and Time Series Analysis
Student Team Leader: Agustin German Reichhardt
Other Student Team Members: Luke Tyler, Sam Nelson, Sebastian Alborta and Skyler Mantooth
Industry Partner: Nestlé
Industry Partner Contacts: Edosa Aibangbee - Manager, Customer eCommerce Account, Walmart Frozen; Megan McCoy - Category Manager, Strategic Planning and Insights, Walmart and Kara Puntriano - Senior Analyst Category Management
We partnered with Nestle’s Rogers, Arkansas, office to perform descriptive and predictive analysis on the sales of Nestlé USA brands and products such as DiGiorno frozen pizza. The project aimed to analyze sales and marketing data from 2019 to 2023. The sales data contained the dollar sales from 2019 to 2023 of each product sold by more than ten different brands, and the marketing data contained information on when and where advertisements for each specific brand were launched. The data also contained the media type of each advertisement. Using this information, we identified four different areas that could impact the ad performance in terms of sales, which include the media type of the ad, the time of launch of the ad, if competition ads were active at the same time, and if there was a major event active during the time the ad was running. With modern statistical techniques such as regression and time series analysis, we created a final report that will provide insight on the effectiveness and impact of each ad campaign, including media types and month of launch, that Nestlé can use when planning future ad campaigns.
Increasing Production Capacity and Efficiency through Technology Upgrades and Labor Allocation
Student Team Leader: Ashley Stanek
Other Student Team Members: Ben Mitchell, Cody Bonds, Hudson McDiarmid and Rhett Caldwell
Industry Partner: National Safety Apparel - Arkansas
Industry Partner Contacts: Joey Roland, Plant Manager and Jennifer Gibson, Production Manager
National Safety Apparel is a company that creates quality clothing for those working in tough industries. Their Fort Smith, Arkansas, facility manufactures uniforms for the US Postal Service including various styles of shirts and pants. Our team is interested in increasing the production capacity and efficiency of the shirt assembly process by providing a decision support tool that will identify the bottlenecks and provide a resource allocation plan. By creating flow diagrams that represent how each style of shirt moves through the system and utilizing the SAM (standard average minute)/unit of each operation, we have identified the major bottlenecks in the system. We have created a Microsoft-Excel-based tool that will allow NSA-Arkansas to identify where to focus their improvement efforts and assist them in deciding which machines to upgrade based on the bottlenecks in the system. The tool will also take NSA-Arkansas's production goal and number of employees and output if the goal is feasible, the minimum number of employees necessary to accomplish the goal, and a suggested plan for the percent of time an employee should be assigned to each operation.
Using Simulation Analysis to Reduce Customer Sojourn Time in Tire and Battery Centers
Student Team Leader: Jessica Creech
Other Student Team Members: Joshua Manson-Endeboh, Kaylee Harrison, Ronan McDonnell and Jacqueline Saldivar
Industry Partner: Sam's Club Tire & Battery Centers
Industry Partner Contacts: Mohamed Boudhoum: Senior Director of Tire & Battery Centers and Tim Gearhart: Project Manager III-Sam’s Club
Sam’s Club is a subsidiary of Walmart, Inc., that generates $80 billion annually using a membership-based business model. Customers pay an annual fee to be eligible to access the products and services that Sam’s Club offers including their grocery and retail shopping, health services, and tire and battery center. Sam’s Club offers customers in-person shopping at 600 locations across the United States; 582 of these locations offer multiple car repair services. These services include tire services, exchanging batteries, replacing windshield wipers, and restoring headlights. Sam’s Club has expressed concerns regarding the amount of time it takes to perform these services as well as the discrepancies in service times across locations. The goal of our project is to suggest improvements to the current system by analyzing the number of technicians performing each service given the number of bays available. To achieve our goal, we developed a simulation model in Arena using data that we collected via time studies. Our model allows technicians to float between vehicles to represent simultaneous technicians performing a service. The model allows us to find a new standard number of technicians for each service to reduce sojourn time without an increase in technician idle time.
Minimizing Changeover Time in Pet Food Production using Integer Programming
Student Team Leader: Ross Harper
Other Student Team Members: Halle Schneidewind, Griffin Langford, Danny Puente and Willow Franks
Industry Partner: Simmons Pet Food
Industry Partner Contacts: Ross Vandever, VP of Continuous Improvement; Cassie Warren, Continuous Improvement Manager and Jonathon Wilson, Project Manager in Pet Food Division
Founded in 1949, Simmons Foods, Inc., is a leading supplier of poultry, pet food, and animal nutrition products. Headquartered in Siloam Springs, Arkansas, Simmons Pet Food – a division of Simmons Foods, Inc. – is North America’s largest supplier of store brand wet pet food. Our focus is the scheduling process used to sequence the production of pet food batches. The current process is highly manual and lacks full visibility into accurate changeover times occurring between produced batches. Our team uses a predictive model fed by historical production data to better estimate these times. These estimates are used as a parameter in an optimization model with the objective of minimizing total changeover time during production. Using open-source optimization software and an optimization interface, our team expects to reduce the time to schedule production sequences by up to 25%. We also expect Simmons Pet Food to see reduced downtime and increased throughput as a result of lower changeover times.
Reducing the Cost of Inventory Counting with Improved Raw Material Storage
Student Team Leader: Whitney Hines
Other Student Team Members: Christopher Haywood, Jade Easter, Preston Boscamp and Rodolfo Bissot Stargardter
Industry Partner: Steco Corporation
Industry Partner Contacts: Ken Gaines, CEO and Louis Jaramillo, Operations Director
Steco Corporation is a small cutting fluids manufacturer located in Little Rock, Arkansas, that operates with only seven employees. Steco produces a line of cutting fluids called Tap Magic that helps to reduce friction on tools for various machine processes. Steco’s main concern involves their raw material inventory management. At the end of each month, limitations force Steco to perform a 100% blind count of all raw material inventory. This process takes 2-4 days to complete and results in a 91% inventory accuracy. We were tasked with providing a better inventory counting process that will minimize the time it takes to perform the count and increase overall inventory accuracy. We created a Microsoft Excel datasheet for our industry partners to use during the inventory count to track important information four months, the new data was used to calculate accuracy and identify items that were consistently inaccurate due to items being stored in multiple locations within the warehouse. We used the analysis to recommend that Steco invest in an RFID system, as well as use dedicated storage for consistently inaccurate items.
Developing A Monitoring Tool and Management Policy for University Research, Office and Conference Room Space
Student Team Leader: Tate Hasenclever
Other Student Team Members: Valerie Jackson, Foster Thompson, Kristoffer Olsen and Bowen Zhao
Industry Partner: University of Arkansas, College of Engineering
Industry Partner Contacts: Heather Nachtmann, PhD, Former Associate Dean for Research & Professor of Industrial Engineering and Kyle Cook, Facilities Manager
The University of Arkansas College of Engineering (CoE) adds social and economic value to the region, state, nation, and the world through engineering education and cutting-edge research in emerging technologies. With the wide variety of activities and recent growth, the CoE is beginning to see space constraining operations and is concerned about a future space shortage. To understand this problem, we began by performing external research, analyzing current data sources, and conducting stakeholder interviews. We learned that the space problem stems from the lack of a formal policy to set space standards, allocation procedures, and periodic review of space assignments. There also exists limited data insights or current metrics regarding space utilization. To improve space utilization for the CoE, our team developed a space management policy recommendation and a space management data monitoring tool. The recommended space policy outlines utilization metrics and targets by space type to inform CoE decision-makers on how to analyze and distribute space within the college. The data monitoring tool we created is a decision support dashboard that centralizes CoE space-related data and creates visualizations to gain insights and evaluate performance. Our hope is that this work will allow the CoE to make objective decisions as they adapt to future growth.
6th Annual Industrial Engineering Capstone Symposium
Arkansas Union Verizon Ballroom, University of Arkansas, Fayetteville
Wednesday, May 4, 2022
Balancing Workload by Optimizing the Assignment of Field Sales Proposals to Pricing Engineers
Student Project Manager: Luke Welch
Other Student Team Members: Aidan Massanelli, Andres Luna Orosco Amelunge, Hector Aguilar, and Harrison West
Industry Partner: ArcBest
Industry Partner Contacts: Erin Hills, Logistics Engineer; Drew Caple, Manger, Pricing and Supply Chain Analytics; and Alex Hoge, Senior Manager, Carrier Partnerships
ArcBest is a multibillion-dollar freight and logistics solutions provider located in Fort Smith, Arkansas. With over 40,000 capacity providers across North America, ArcBest solves complex supply chain problems for customers utilizing truckload, less-than-truckload, freight brokerage, and several other avenues of services. When a customer identifies a service that ArcBest can provide, a Field Sales Proposal is created and assigned to a pricing engineer. Once assigned, the Field Sales Proposal is labeled a Pricing Proposal Maintenance, or PPM. ArcBest has expressed concerns that pricing engineers sometimes experience a workload imbalance due to the PPM assignment process. The goal of our project is to improve the process of assigning Field Sales Proposals to pricing engineers. By utilizing PPM data provided by ArcBest, our team performed a regression test on different types of PPMs to provide an estimated mean service time for each type. Then, we developed a Java application that assigns batches of PPMs to engineers in a way that minimizes the difference of workload between engineers. This optimization approach is expected to reduce the workload imbalance and result in a more even utilization of engineers and faster service times for PPMs.
Reducing Patient Transportation Times through Improved Staff Scheduling using Simulation
Student Project Manager: Olivia Gammill
Other Student Team Members: Taylor Knabe, Jennifer Sanchez, Andrew Hume, and Patrick Gonzalez
Industry Partner: Baptist Memorial Hospital-Memphis
Industry Partner Contacts: Katie Parker, Regional Director for Performance Improvement; and Jared Moses, Administrative Director at Baptist Memphis
Baptist Memorial Healthcare Corporation is a network of hospitals located throughout the central-south of the United States that offers patient treatment and medical care with various specialties. Our interest is in patient transportation at Baptist’s flagship hospital in Memphis, Tennessee. Transportation refers to team members, called transporters, who pick up and move patients between different areas of the hospital. Baptist is concerned with excessive patient transportation times. We identified that the Memphis hospital is falling short of Baptist’s 35-minute transportation goal. On average, transportation time takes 51.6 minutes on weekdays and 44.6 minutes on weekends. We determined that 82% of the total transportation time is spent waiting for the transporter to be ready. These long waiting times suggest a staffing issue. To explore this issue, we built a detailed discrete-event simulation model of the transportation process. We used this model to experiment with different staffing strategies. By more effectively utilizing the staff Baptist already has, we were able to decrease the average transportation time to 40 minutes where 55% of transports were meeting the goal of 35 minutes or less. By increasing the number of staff to 40, we found that the average transportation time decreased to 31 minutes and the percentage of transports completed in 35 minutes or less increased to 74%.
Improving the Scheduling of Protection Services Staff by Creating a Microsoft Excel Application and Exploring Shiftboard’s Full Functionality
Student Project Manager: Natalia Sandhu
Other Student Team Members: Camila Schrader, Itza Della-Sera, Sarah Bollinger, and Esteban Siles
Industry Partner: Crystal Bridges Museum of American Art
Industry Partner Contacts: Kash Logan, Director of Protection Services; Brent Pettingill, Protection Services Manager; and Michelle Mittiga, Administrative Assistant
Crystal Bridges Museum of American Art is a non-profit charitable organization that celebrates the American spirit in a place that unites the power of art with the beauty of nature. Our interest in Crystal Bridges is their Protection Services department, specifically the scheduling of Protection Services staff. This department provides a safe environment for employees and guests while also protecting the museum’s art. Currently, Crystal Bridges has concerns due to the inefficiency of creating schedules and the inefficient use of Shiftboard. Shiftboard is the software the Protection Services department uses to distribute the completed schedules to the employees. The administrative assistant responsible for creating the schedules takes approximately 3 hours per week to create the schedules, with 33% of time spent manually transferring the completed schedules into Shiftboard. To reduce this time, our team created two Microsoft Excel tools that eliminate Shiftboard usage. For the first tool, every employee is allowed access to one worksheet, accessed only with a username and password, that has the corresponding employee’s schedule for the upcoming two weeks. With the second tool, the administrative assistant clicks a button to automatically send the employee’s schedules to their individual email. Additionally, we found ways to improve the current use of Shiftboard. We mastered details of Shiftboard’s functionality, converted that functionality to Crystal Bridges, provided documentation of Shiftboard’s relevant tools, and taught the administrative assistant how to properly use the features. We use simulation to compare the use of the new Excel tools to the current process, and we use optimization to offer general guidance on fair scheduling practices.
Decision Making for Downstream Pace in the Event of Upstream Delays
Student Project Manager: Neil Balasekaran
Other Student Team Members: Peyton Dunman, Noah Layton, Bailee Miller, and Maddie Pearl
Industry Partner: Gerdau
Industry Partner Contacts: Nicholas Boerjan, Melt Shop Department Manager; Clinton Johnson, Organizational Development Specialist; and Heather Christensen, Process Engineer
Gerdau is a steel manufacturing company located in Fort Smith, Arkansas, that specializes in the production of steel billets. Our interest is in the pace at which the six-step steel making process of the melt shop is operating. When a delay occurs at an upstream process, the downstream caster pace is negatively impacted. Through stakeholder interviews, our team concluded improper pacing of the melt shop, due to these unpredictable delays, leads to a reduction in the caster’s Tons Per Hour (TPH), a throughput key performance indicator for the melt shop. Our goal was to create a tool that aids in decision making for setting caster pace (TPH) upon different melt shop delays and improves data collection for future delay analysis. To accomplish this goal, we designed a tool that allows for visibility of station progress. This system requires a data collection component that allows each station to record their progress, a data analysis element for the floor manager to better predict an operating TPH, a monitoring system for the floor manager to see the progress within each station, and a user interface that is easy for all operators to use.
Developing a Consolidated Toxicological Database to Perform Chemical Risk Analysis and Predictions
Student Project Manager: Quinn Salverson
Other Student Team Members: Andrew Banks, Colin Connelly, Kaitlyn Frey, and Isha Rajaram
Industry Partner: The Instituto Superiore Di Sanitá - Department of Environment and Health
Industry Partner Contacts: Dr. Olga Tcheremenskaia - Principal Investigator; Dr. Chiara Battistelli - Principal Investigator; Dr. Cecilia Bossa - Principal Investigator; and Cristina Parenti - Sinergi llc, Professional Affiliate
The Instituto Superiore di Sanità (ISS) acts as the main center for research and advice on public health in Italy. The Department of Environment and Health of ISS aims to reduce environmental and social risk factors through risk assessments that include the development of predictive toxicology models for speeding up the toxicological assessment of chemicals and, at the same time, for reduction of animal testing. The predictive toxicology approaches share the need of highly structured and standardized data as a starting point. ISS currently utilizes five separate data sources for carcinogenicity and mutagenicity called the ISSTOX to combat animal testing, with each dataset defining different chemical attributes and testing information for analysis. A 2020-2021 industrial engineering capstone team at the University of Arkansas initially sought to improve the interoperability of the ISSTOX data by designing a relational database model and proposing a user-interface that allows for chemical visualization and analysis tools. The primary goals of our project are to design and implement a simplified version of the relational database model in Microsoft Access and to design and implement a system allowing for data integration, visualization, and analysis. The finalized Microsoft Access relational database will allow public use of toxicological data to perform chemical risk and predictive analysis.
Maximizing Utilization of Intermodal Local Tractors through Driver Reassignment
Student Project Manager: Coleman Warren
Other Student Team Members: Faris Balbaid, Eric Gerstein, William Fletcher Rosenbleeth, and Hayden VanLaningham
Industry Partner: J.B. Hunt Transport Services, Inc.
Industry Partner Contacts: Sara Pierson, Logistics Engineer II; and Jordan Sonnentag, Senior Logistics Engineer
J.B. Hunt Transport Services, Inc., is a Fortune 500 company headquartered in Lowell, Arkansas, that provides a variety of freight transportation services for customers in North America. J.B. Hunt Intermodal (JBI) delivers freight to consumers using a combination of truck and rail delivery systems. JBI seeks to minimize cost per mile and maximize tractor utilization on regional and local tractors for the life of the tractor. Driver reassignments or tractor transfers between fleets occur to balance tractor utilization but are infrequent and lack a decision-making process. Tractor utilization is not closely monitored even though large costs are incurred from both opportunity costs for underutilization and fees for overutilization of JBI tractors. We created a weekly monitoring system that identifies mis-utilized tractors and displays these tractors on Power BI. We also created a driver reassignment recommendation tool that considers tractor utilization and driver productivity to provide an assignment of drivers to tractors within a fleet to balance tractor utilization. We established a database structure for JBI to utilize when inputting data into our system to reassign drivers within fleets and tractors between fleets. Finally, we documented the process of evaluating the success of a transfer and studied the effects of our tool on a simulated model of a JBI local fleet.
Data Synthesis, Storage, and Visualization for Near Real-Time Analysis of Final Mile Delivery Routes
Student Project Manager: Spencer Loper
Other Student Team Members: Henry Ward, John Santine, Luke Rafie, and Cody Parrish
Industry Partner: LATROBE LLC
Industry Partner Contacts: Latanyua Robinson, President; and James Robinson, Vice-President
Founded in 2010, LATROBE LLC is a consulting firm operating transactional businesses to provide livable wage job opportunities in manufacturing and transportation. The Mission of LATROBE LLC is to advocate for Economic Empowerment thru Education + work Experience + Entrepreneurship. Since 2019, LATROBE has entered a Delivery Service Partnership (DSP) with Amazon, acting as a subcontractor for final mile delivery. In completing their delivery routes, LATROBE receives data from multiple, independent applications that Amazon provides. Currently, no process or mechanism exists for LATROBE to synthesize and leverage their data in making business decisions. The goal of our project is to design a system to turn LATROBE’s multiple data sources into meaningful analytics and visualizations. To accomplish this goal, we designed a system composed of three subsystems: extract, transform, and load (ETL), data storage, and data visualization. In designing this system, we provided LATROBE with two solutions: our original proof-of-concept tool that could be implemented immediately, and our proposed future work to add more capabilities and automation if LATROBE wants to invest more in this area.
Improving the Usability and Accuracy of a Category Sales Forecasting Tool
Student Project Manager: Chloe Kordsmeier
Other Student Team Members: Grant Glover, Collin Snieski, Caleb Gonzales, and Carolina Virreira
Industry Partner: Nestlé
Industry Partner Contacts: Edosa Aibangbee, Customer Account Manager; Raegon Barnes, Director Category Management; Ian Rippl, Customer Account Analyst; and James Gairhan, Category Sales Analyst
Nestlé is the world’s largest food and beverage company, and Nestlé USA is a branch of the company that provides a variety of products to retailers across the United States. Nestlé USA’s office in Rogers, Arkansas, is focused on Nestle’s relationship with Walmart. Nestlé USA has three category advisorships with Walmart: Baking, Chilled Creamers, and Frozen Meals; Walmart trusts Nestlé to offer advice on product placement and quantity in those categories. Our project is focused on improving the forecasting tool created by a 2020-2021 University of Arkansas industrial engineering capstone team. Currently, Nestlé USA uses the previous year’s sales data as the prediction for the current year’s sales instead of using the tool created by the 2020-2021 team. Nestlé USA asked us to improve the user interface of the tool and to extend the forecast horizon while maintaining a low mean absolute percent error. To improve the forecasting horizon of the previous tool, we weighted the 2020 sales data to adjust for the impact of the COVID-19 pandemic. To address the tool’s user-friendliness, we altered the R code to allow the user to choose their file and automatically count the number of weeks for historical data, eliminating the need for the user to have to alter the code.
Balancing Hospitalist Workload by Optimizing Patient Assignment using Linear Programming
Student Project Manager: Brandon Jerome
Other Student Team Members: Jaiden Ellerbee, Ryan Kraichely, Terrance Martin, and Camille Sockwell
Industry Partner: Parkland Health
Industry Partner Contacts: Nina Drolc, Executive Nursing Leadership, Senior Analyst, Database; and Nainesh Shah, M.D., Deputy Medical Information Officer
As one of the largest public hospital systems in the United States, Parkland Hospital serves Dallas County, Texas. Patients with medical diagnosis are admitted and assigned to a hospitalist, a physician trained in hospital medicine tasked with day-to-day care of medicine inpatients. Parkland Hospital is concerned with the amount of time hospitalists spend each morning assigning patients to hospitalist teams, and the resulting workload imbalance among hospitalists. To automate the assignment process, we created a decision support tool that utilizes Microsoft Excel and Visual Basic for Applications to read in patient data, clean and format the data, calculate severity scores per patient, and output a file that is then read in by a Python script. The script runs an integer program built to optimize patient assignments. Our model outputs proposed patient assignments with the objective of balancing workload among hospitalists while also considering the unit location preferences of hospitalists. In addition to optimizing workload balance, our tool will reduce the time required to assign patients to hospitalists by 75-85%.
Reducing Bone, Liver, and Lung Biopsy Turnaround Times for Potential Oncology Patients by Standardizing System Processes
Student Project Manager: Shantal Sarmiento
Other Student Team Members: Tayden Barretto, Stephen Branscum, and Paola H. Franco
Industry Partner: Parkland Health
Industry Partner Contacts: Nina Drolc, Executive Nursing Leadership, Senior Analyst, Database; Jenni Burnes, Vice President, Ancillary Services; and Robyn Cobb, Program Manager
Parkland Health is one of the largest public hospitals in the U.S. Opened in 1894, Parkland is a safety net provider that serves uninsured, underinsured, and lower-income patients. Parkland offers multiple services within the hospital, but we are specifically interested in the interaction of medical imaging (radiology) and cancer care (oncology). Our goal is to reduce biopsy turnaround times of potential oncology patients to less than 14 days from biopsy order to biopsy appointment completed. We conducted analysis on different data sets provided by Parkland to determine the system's steps and how long it takes a patient to move between each major stage in the process. We identified problems related to data recording, lack of standardized processes, and appointments scheduled without patients' awareness. We wrote VBA code to reorganize the data for easier analysis, and we performed a linear regression to show the relationship between time to complete the process and day, month, and year. In addition, we developed a simulation model to support our recommendations for system standardization, to reduce turnaround times, and to improve the overall patient experience.
Improving the Centralized Lending Processes for Increased Throughput using Targeted Human-Centered Solutions
Student Project Manager: Luke Weiner
Other Student Team Members: Cleondra Cooks, Anna Lee, Jaira Porter, and Aidan Grygar
Industry Partner: Republic Finance, LLC
Industry Partner Contacts: Sherwin Poormand, Head of Loss Mitigation Strategy; and Savensky Drakeford, Head of Central Collections
Founded in 1952, Republic Finance specializes in providing a variety of consumer loans, flexible lending options, and incomparable customer service. They have assisted customers in meeting their personal finance goals for nearly 70 years. With branch offices located in Alabama, Georgia, Kentucky, Louisiana, Mississippi, Missouri, South Carolina, Tennessee, Texas, and Virginia, Republic Finance is proud to serve more than 300,000 customers in over 250 communities throughout the United States. Republic Finance provides flexible lending options to customers through both localized branches and a Central Lending Unit (CLU). While branches work directly with customers located within a specified geographical footprint, the CLU operates over-the-phone across the entire Republic Finance customer base, including those customers who are not located near a branch. We worked with the CLU to identify and provide solutions for several problem areas within their remote lending process. Several opportunities were identified within the steps of the process including initial solicitation, agent-to-customer interaction, receiving customer documents, and contract execution. Focusing on mutually exclusive and collectively exhaustive solutions to address all parts of the loan-by-phone process, our team generated five solutions with different targeted improvements. To improve initial solicitation and contract execution, a modified agent script and online appointment booking system were designed. A digital collateral list and document checklist were created to enhance the agent-to-customer interaction model. Lastly, using random forest modeling, primary factors with the highest correlation to a customer filling an application after solicitation were identified and we recommended a possible path forward to prioritize customers with a higher probability of applying for a loan.
Improving the Sustainability and Reducing Costs of Inbound Loads using Lane Consolidation
Student Project Manager: Emma Regier
Other Student Team Members: Lucas Hicks, Emily Eskens, Jacob Reich, and Austin Wood
Industry Partner: Sam's Club
Industry Partner Contacts: Abhishek Devadiga, Manager II, Supply Chain Management; and Ben Grunow, Sr. Manager Outbound Transportation
Sam’s Club is a retail warehouse club managed in conjunction with Walmart that operates out of large facilities and charges their club members annual fees to shop at their stores. Sam’s Club’s distinctive business model allows customers to buy items in bulk. Our system of interest is their inbound transportation network that is responsible for transporting products from vendors or import distribution centers to center points, Sam’s distribution centers, or fulfillment centers. Our initial goal was to investigate the use of heavy haul permits to reduce empty miles, but we found that divisible loads cannot legally be considered for heavy haul. We then pivoted our focus to a load consolidation effort. Sam’s Club asked us to design and develop a decision-support tool to evaluate the cost savings and sustainability impacts associated with load consolidation opportunities. To accomplish this, we created a tool through Power BI that identifies lanes that are underutilized and provides potential savings and sustainability improvements if consolidation efforts were to be implemented. Once connected to Sam’s Club’s live data source, this tool will be able to track and identify consolidation opportunities within lanes for the inbound transportation network.
5th Annual Industrial Engineering Capstone Symposium
Reducing Empty Costs of Relocube Repositioning through Forecasting and Optimization
Student Project Manager: Laura Acosta
Other Student Team Members: Tate Thornley, Joseph Cummings, IV, Kate Burrows, and Xander Smith
Industry Partner: ABF Freight
Industry Partner Contacts: Jacob Huffstetler, Senior Financial Analyst; and Kirby Clark, Director, Financial Planning & Analysis
ABF Freight provides less than truckload shipping using a nationwide network of 242 service centers. ABF Freight offers U-Pack, a self-pack moving service that uses containers called ReloCubes to store customer belongings while in transit to the client’s new home. Because U-Pack operates from a nationwide network of service centers, it is common for ABF Freight to reposition ReloCube containers from one service center to another to meet customer demand. The goal of our project is to reduce the empty costs associated with ReloCube repositioning. We created an inventory model to track the movement and storage of ReloCubes throughout the service center network. Based on the inventory model, we created an optimization model that prescribes when, from where, and to where ReloCubes should be repositioned. The inputs to our optimization model include forecasted ReloCube demand based on data from 2016-2020. With the use of our models, ABF Freight now has the ability to reduce the distance and costs associated with ReloCube repositioning.
Improving the Offer Creation Process by Standardizing ArcBest’s Bid Model
Student Project Manager: Ben Barron
Other Student Team Members: Jackson Marshall, Ben Baser, Blake Parrish, and Joseph Ellis
Industry Partner: ArcBest Corporation
Industry Partner Contacts: Trenton Cason, Lead Logistics Engineer - Yield Strategy and Alex Hoge, Pricing and Supply Chain Manager
ArcBest is an integrated logistics provider headquartered in Fort Smith, Arkansas. They are focused on providing the best customer experience possible with seamless access to a broad suite of logistics capabilities, including truckload, LTL, ocean and air, ground expedite, managed transportation, and warehousing. Our system of interest falls within the yield division where the goal is to create and return pricing bids to potential customers. ArcBest’s yield division currently lacks standardization in bid formats when bid offers are created and transferred to the marketing and sales teams which causes redundancy and extra time for employees across multiple areas. Our system improvement efforts are aimed at automating and standardizing the creation of offers within the bid process. We created a tool in Microsoft Excel using Visual Basic Userforms. The tool is designed to minimize the work required to create an offer. It is impossible to create a tool for every situation a Pricing Engineer will encounter, so we aimed at building an extensible tool that can be the foundation for future add-ons. To estimate the potential impact of our tool on bid processing, we created a simulation model. Based on historical data on 10,000 bids, we simulated bid processing with features such as bid type, sales region, and bid market. Our analysis shows that implementing our solution should yield a 1.9% improvement in engineer utilization which will allow for more bids to be processed without hiring more engineers. Our analysis also shows that our solution will decrease the amount of time required to process a bid by 5% or more than 38,000 hours annually.
Reducing Emergency Department Wait Time by Improving Nurse Scheduling using Simulation
Student Project Manager: Matt Nixon
Other Student Team Members: Junwoo Chang, Jacob Kinney, Matt Murry, and Hawkin Starke
Industry Partner: Baptist Memorial Healthcare
Industry Partner Contacts: Katie Parker, Regional Director of Performance Improvement
Baptist Memorial Healthcare is a large healthcare organization operating in the southeastern United States. Their flagship hospital, located in Memphis, TN, treats more than 65,000 patients in its Emergency Department (ED) every year. The ED is comprised of two main processes: the front-end process includes patient arrival, registration, and initial triage (condition assessment); the back-end process includes patient treatment. Extended patient wait times are the main concern of our industry partner. Our project focuses on the staffing needs of the front-end process with the goal of reducing patient wait time. By reducing patient wait time, the ED can also reduce patients leaving without being seen by a provider (LWBS). To better understand this process, we created a simulation model that mimics the flow of patients into and through the ED. The simulation models arrivals to the ED as a function of time of year, day of week, and time of day. We explore the levels of nursing staff required within the front-end of the ED to provide a high-quality patient experience while maintaining high utilization of nursing staff.
Reducing Length of Stay Overages by Improving Hospitalist’s Rounding Paths and Patient Assignments
Student Project Manager: Lauren Law
Other Student Team Members: Adam Corral, Courtney Johnston, Dania Quintero, and Owen Stuckey
Industry Partner: Baptist Memorial Healthcare
Industry Partner Contacts: Katie Parker, Regional Director of Performance Improvement
Our industry partner is Baptist Memorial Health Care. Our project involves Baptist’s Memphis hospital, the largest hospital in the network. As they do for all hospitals, insurance companies cover treatment for patients at the Memphis hospital a certain number of days based on diagnosis. This number of days is called Observed to Expected (O-E). Baptist is concerned that patient Length of Stay (LOS) too often exceeds the O-E and asked us to investigate potential causes and solutions involving hospitalists. Hospitalists are doctors who make daily rounds to a set of patients to track their progress and ultimately discharge them. We found that hospitalists spend a lot of their day traveling between patient rooms, because patient room location is not considered in the assignment of patients to hospitalists. They also make frequent return trips to their offices in between patient visits to input orders and make notes. This excess travel implies less time to spend with patients. Our goal is to reduce unnecessary travel time for hospitalists, giving them more time for patients, thus leading to reduced LOS. We achieve this goal by improving patient to hospitalist assignment policies and providing workstations closer to patient rooms.
Predicting Transitions of Steel in the Molten Scrap Metal Casting Process
Student Project Manager: Esteban Lopez
Other Student Team Members: Emily Feuerborn, Linden Van Hoose, and Logan Rodriguez
Industry Partner: Gerdau
Industry Partner Contacts: Clinton Johnson, Management Systems Facilitator; Eric Springs, QA & Metallurgy Manager; Alan Doss, Quality Labs Routine Facilitator; and Caleb Collins, Caster Specialist
Gerdau is a steel manufacturing company that produces long steel and special steel products. For this project, we worked with Gerdau’s mill located in Fort Smith, Arkansas. Our system of interest is the transition phase of their continuous casting process. The transition between two customer orders produces a mix of steel. This transition metal does not satisfy the chemistry composition requirements for either order and therefore must be cut out and scrapped. The transition technicians manually estimate the amount of transition metal based on rough guidelines and experience. This approach leads to unacceptable estimation errors. When the transition is overestimated, quality steel meant for the customer is cut, and good steel is wasted. Underestimating does not allow the full transition to be cut out from the quality steel before it moves on to the next phase of the manufacturing process. We improved the estimation process by creating a multiple linear regression model and a random forest model. We implemented these models in a spreadsheet tool that also automates the transition technicians’ guidelines for their current approach.
Reducing Production Costs by Optimizing the Assignment of Parts to Machines using Cost-Benefit Analysis and Monte Carlo Simulation
Student Project Manager: Matthew Walters
Other Student Team Members: Cameron Blann, Christofel Enslin, Alyssa McKnight, and Jasia Porchay
Industry Partner: Hytrol Conveyor Company, Inc.
Industry Partner Contacts: Felton Barlow, Manufacturing Engineer; and Ken Nickerson, Industrial Engineering Manager
Hytrol Conveyor Company, Inc., is an engineered to order (ETO) manufacturing company that produces conveyors for customers such as UPS, FedEx, and Amazon. Our project’s system of interest is the laser cutting and punching operations within Hytrol's brown and silver fabrication cells. Our initial goal was to reduce the amount of scrap produced from these two cells. Using Pareto analysis, we discovered a shift in the main cause of scrap from the laser cutter in 2018 to the punch in 2020. From 2018 to 2020, the amount of scrap produced from laser cutters decreased by 50% while the amount of scrap from punches increased by 30%. This shift was a result of Hytrol actively moving the production of their parts from the laser cutter to the punch in an attempt to standardize production. Because Hytrol continues to strive to standardize their operations, we switched our focus from scrap to production costs. We identified a need for a cost-benefit analysis tool to assist Hytrol in determining if switching the production of a specific part from the laser to the punch would be cost effective. A Monte Carlo simulation is used within the tool to account for the uncertainty of certain parameters used to calculate expected net present value, payback period, and return on investment.
Facilitating Chemical Risk Analysis by Organizing Toxicological Databases with a Flexible Open Platform
Student Project Manager: Conner Waybright
Other Student Team Members: Ryan Brim, Yok Lin Ong, Brandon Ward, and William Warner
Industry Partner: Istituto Superiore di Sanità
Industry Partner Contacts: Cristina Parenti - Freelance Consultant; Olga Tcheremenskaia, PhD – Principal Investigator; Cecilia Bossa, PhD – Principal Investigator; and Chiara Laura Battistelli, PhD – Principal Investigator
Istituto Superiore di Sanità (ISS), or the National Health Institute of Italy, is an organization whose mission is to guide public health policy based on scientific research. Demand for a shift away from chemical testing on animals has led ISS to take steps towards analyzing chemical toxicity with alternative computational methods using existing data. ISS has gathered and stored a collection of toxicological data in five data sources called ISSTOX. The purpose of our project is to further develop ISSTOX by increasing the interoperability of the data. Our goal is to facilitate the creation of a public toxicological data storage and analysis system using the ISSTOX data. To accomplish this goal, we must design a system that facilitates chemical risk assessment using legacy data. Integrating the data sources of ISSTOX requires data cleaning and standardization before consolidating the five data sources into a single, chemical relational database. This process requires designing a data model sufficient to store and retrieve the ISSTOX data. This system also includes a user-interface designed to interact with the database, allowing users to conduct useful toxicity analyses. Finally, this system is designed for a publicly available, online setting to increase accessibility to quality toxicity data.
Balancing Inbound and Outbound Flow of Trailers by Predicting Customer Compliance and Seasonality
Student Project Manager: Parker Tankersley
Other Student Team Members: Grayson Lee, Remy Kirk, Jacob Underhill, and Alyssa Roth
Industry Partner: J.B. Hunt 360 Box Engineering Team
Industry Partner Contacts: Matt Turner, Logistics Engineer I and Elisa Daniel, Logistics Engineer II
J.B. Hunt is a transportation and logistics company that moves customer freight throughout North America by both truck and railway. We worked with J.B. Hunt’s 360 Box service that offers drop and hook trailer shipping options. 360 Box utilizes J.B. Hunt’s online marketplace to pair third-party and J.B. Hunt trucks to trailers for transport. It is crucial for 360 Box to keep trailer flow in and out of customer markets balanced to avoid empty miles and off-route travel. With our partners within the 360 Box Engineering Team, we identified lack of compliance with expected load volumes and seasonality as the main challenges to maintaining balance. We addressed these challenges by creating a random forest regression model that predicts customer compliance as a function of information contained within award contracts and by creating a 12-month volume forecasting tool using Holt-Winters’ method to account for seasonality. These tools will allow 360 Box to make confident, data-driven trailer balancing decisions. Based on trailer balance situations explored in historical data, our team estimates a 27% reduction the mean absolute percent error in compliance prediction when using our random forest regression model, with additional balancing insights drawn from our seasonality tool.
Improving Driver Integration across Dedicated Contract Services Accounts using Resource Identification and Capacity Planning
Student Project Manager: Brianna Bert
Other Student Team Members: Jose Beltran, Gabriel Figueroa, Sam Griffin, and Emily Rodriguez
Industry Partner: J.B. Hunt Transport Services, Inc.
Industry Partner Contacts: Zach Evans, Senior Logistics Engineer; Graham Nelson, Logistics Engineer III; and Marc Alley, Senior Director of Engineering and Technology
J.B. Hunt Transport Services, Inc., is a transportation and logistics company that serves clients through their five business units: Intermodal, Integrated Contract Services, Truckload, Final Mile, and Dedicated Contract Services (DCS). We partnered with J.B. Hunt’s DCS business unit to investigate ways that we can help increase driver utilization in their western region. DCS Drivers on average work approximately 55 hours/week. Because of fluctuations in account needs due to seasonality and other factors, drivers often are not being utilized to their full potential. Currently, DCS account managers work to integrate drivers across DCS accounts by contacting other account managers to identify available loads when their account is overstaffed or available drivers when their account is understaffed. Standardizing the process for account managers to identify available drivers and loads in other accounts would result in increased driver utilization. To eliminate the need for account managers to independently seek out opportunities for their drivers, we developed a database-driven tool that has two main features: driver availability identification and utilization tracking. This tool aims to identify drivers that meet an account’s specifications while also prioritizing drivers with lower utilization and closer proximity to the account in need of drivers. The database tool will expand the network of available drivers and loads to maximize driver utilization and reduce net driver needs of JB Hunt. The tool will also continuously track utilization and forecast driver needs for all dedicated accounts.
Improving Multi-Stop Truckload Pricing using Random Forest Regression
Student Project Manager: Madeline Suellentrop
Other Student Team Members: Garret Clark, Chase Cottrell, Nicholas Jaco, Jaclyn Walls
Industry Partner: J.B. Hunt Transport Services, Inc.
Industry Partner Contacts: Doug Mettenburg, Becca Luetjen and Kyle Kraichely
J.B. Hunt Transport Services, Inc. is a Fortune 400 company that provides transportation and logistics solutions using trucks and trains to move freight across North America. Within J.B. Hunt, Integrated Capacity Solutions (ICS) conducts third-party logistics by pairing non-contract customers with third-party carriers. ICS uses their own software, ETP, to predict the cost of multi-stop truckloads. Multi-stop truckloads combine Less-Than-Truckload customer orders with a single origin and multiple destinations. The current process used to estimate the cost of a multi-stop shipment has not been evaluated in approximately ten years; therefore, our goal is to improve multi-stop truckload cost prediction. To improve the accuracy of cost estimation, we applied a machine learning technique called random forest regression. Under J.B. Hunt’s current approach, the Mean Absolute Percent Error (MAPE) of multi-stop cost estimates is 20.1%, but with the applied random forest regression model, the MAPE is now 12.5%. With improved estimation accuracy, J.B. Hunt can achieve higher customer satisfaction leading to revenue retention and growth.
Forecasting Category Sales using Data Mining and Predictive Analytics
Student Project Manager: Amie Beckwith
Other Student Team Members: Suelly Samudio Ortega, Joseph Goodman, Sebastian Alborta Pereyra
Industry Partner: Nestlé USA
Industry Partner Contacts: Raegon Barnes, Director Category Management; Emily Power, Sr. Analyst Category Management; and Edosa Aibangbee, Manager Category Management
Nestlé USA is a segment of Nestlé, the world’s largest food and beverage company. Nestlé USA provides a variety of food and beverage products to retailers across the United States. Nestlé USA’s office in Rogers, Arkansas, focuses on its Walmart and Sam’s Club operations. Nestlé USA has category advisorships with Walmart, meaning that they have been chosen by Walmart to offer professional advice on product assortment for these categories. Our project is focused on improving Nestlé USA’s sales forecasting system for its category advisorships. Currently, Nestlé USA simply uses last year’s sales as an estimate for this year’s sales. This system has proven to be inefficient as it is entirely backwards looking. To improve Nestlé USA’s forecasting system, we implemented ARIMA forecasting models using R. These models incorporate binary holiday variables and detect growth and decline over time. Our analysis shows that these models offer a clear improvement from the current system, especially when using short forecasting horizons. To add ease to the forecasting process, we also created a user interface for our forecasting models using R Shiny. This interface allows the user to make custom selections and press a button to output the desired forecast.
Automated Pairing of Clients to Job Opportunities based on a Quantitative Assessment of Skills and Abilities
Student Project Manager: Lawson Porter
Other Student Team Members: Patrick Dougherty, Andrew Powers, and Hatem Alsayed
Industry Partner: Open Avenues
Industry Partner Contacts: Jeff Hairston, Training Center and Operations Manager
We partnered with Open Avenues, a local non-profit organization that helps people with disabilities (clients) attain vocational goals. Open Avenues offers a Community Employment service which seeks to place clients into job opportunities in the local community; however, the current process has challenges with matching clients to jobs that are appropriate for their abilities. To improve this service, our team developed an automated abilities-based tool which allows Open Avenues staff to match clients to jobs from a database of over 800 occupations. The tool also allows for the input and storage of pertinent client information, including the results of the abilities assessment clients complete upon arrival to Open Avenues. This tool will equip Open Avenues with the client-specific recommendations needed to place clients into roles where they can succeed both personally and professionally.
Improving Course and Classroom Scheduling Policies through Correlation Analysis and Forecasting
Student Project Manager: Michael Rechtin
Other Student Team Members: Alyssa Bobalik, Olivia Hope, and Jacob Pannell
Industry Partner: The Office of the Provost at the University of Arkansas
Industry Partner Contacts: Gary Gunderman, Executive Director of Institutional Research and Assessment and Colleen M. Briney, Vice Provost for Planning
This project aims to assist the Office of the Provost at the University of Arkansas by analyzing and improving the course and classroom scheduling process. The responsibilities of the Office of the Provost include overseeing academic policies, budgetary affairs, and the three parties that control this scheduling process: the colleges, their departments, and the Office of the Registrar. The scheduling process starts by exporting the previous year’s semester schedule. Next, all course details are edited and approved by all three parties before courses are assigned classrooms. Later, if a student wishes to enroll in or drop a course after enrollment, an override is used. Within the scheduling process, we focus on increasing classroom utilization, forecasting core course enrollment, and improving the override sub-process. We evaluate the relationship between classroom utilization and the characteristics of a room using correlation analysis. Additionally, we analyze classroom utilization by time of day and the day of the week. Next, we forecast core course enrollment using ARIMA. Lastly, we recommend improvements to the Department of Industrial Engineering’s override process.
Increasing Violation Capture Rates by Improving Parking Control Routing and Scheduling
Student Project Manager: Warren Lewis
Other Student Team Members: Sam McKinney, Kyle Sprang, and P. Marshal Smith
Industry Partner: University of Arkansas Transit and Parking Department
Industry Partner Contacts: Gary K. Smith, Director, Transit and Parking and Andy Gilbride, Project/Program Manager
The University of Arkansas Transit and Parking Department serves the university community by providing transit and parking options to staff, students, faculty, and visitors. It is important to the department’s mission that fair parking opportunities are provided, so vehicles in violation of regulations should be identified and issued a citation. The department is concerned that a number of parking violations are missed by their Parking Enforcement staff. The department’s enforcement is made up of Parking Control Officers (PCOs) who are in charge of patrolling the university parking lots and garages in order to issue citations for any identified parking violations. We worked with the department to increase the effectiveness of the parking patrol process. We used data analysis, system engineering, and optimization techniques to provide the department with updated PCO routing and scheduling processes that improve the system’s parking violation capture rate by reducing travel time and improving patrol coverage.