Applied Engineering

Projects

For information on becoming a project sponsor, please contact Dr. Laura Genik.

Applied Engineering students participate in Spring Term Design Day. The following were the project sponsors and projects for the Spring of 2018:

MSU Infrastructure Planning and Facilities: Business Case for Campus-wide Temperature Control Guidelines

Michigan State University’s Infrastructure Planning and Facilities (IPF) department is tasked with the enormous project of maintaining the heating, ventilation, and air conditioning (HVAC) systems campus- wide consisting of over 550 buildings. The control technology currently utilized ranges across several decades from entirely manual to entirely automated.

Our team was brought in to develop a business case for a campus-wide temperature control guideline. This guideline includes multiple approaches acknowledging the fact that IPF utilizes a variety of technologies, including direct digital control (DDC). Big data analysis techniques were used to pinpoint weak systems and prioritize the order in which they should be updated. Additionally, a statistic-based marketing scheme was developed to encourage behavioral shifts towards sustainability. This project will serve as the foundation for a campus- wide cultural and behavioral shift towards increased HVAC efficiency and sustainability.

Our team targeted buildings with the most potential to save energy and created a business case/best practices guideline to help move campus towards sustainability regarding room temperatures. To reach this, a 3-faceted approach was developed.

First is the DDC building protocol development, which includes the utilization of current and historical data. This determined the efficiency difference between DDC and non-DDC buildings, the savings from a DDC system, and conveyed possible savings in relatable terms.

The second approach was the non-DDC building protocol for optimal comfort and efficiency, as well as a compliance determination system. The focus was to engage and inspire non-DDC building occupants to follow the guideline.

Lastly, a DDC conversion priority guideline was created. This was a data driven guideline for converting non-DDC buildings to DDC buildings. Metrics to determine this are theoretical energy savings, reduction in comfort complaints, and the cost of DDC installation.

Team Members: Back Row (L-R): Jinshou Yao, Brandon Kortum, Joseph Andring, Ian Beason. Front Row (L-R): Youssef Abdelhafiz, Ian Rowan, Travis Winans, Jacob Brower

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MSU Sustainability: EV Charging Stations

Sustainability has always been a major priority of Michigan State University (MSU), both internally with their own operations, as well as externally to the faculty/staff, students and other visitors to the campus. Now the university is taking things one step further to promote environmental responsibility through the installation of several new EV (Electric Vehicle) and Plug-In Hybrid charging stations.

The university currently has a number of charging stations in various spots around campus. But, MSU’s Infrastructure Planning and Facilities (IPF) team does not believe those existing stations are sufficient to keep up with current or future demand for EV/Plug-In Hybrid charging. This project will, however, be a very costly addition to the campus and should not be done without a proper feasibility analysis. In order to gain further understanding about current and future demand, MSU IPF has collaborated with our team.

The goal of our team is to perform market research on the demand for these charging stations and derive a recommendation for how many new charging stations should be installed as well as where to install them. They should be located in places where it is both convenient for users to access as well as be compatible with the current infrastructure of campus, specifically in terms of electrical power draw capability and potential remote monitoring capabilities. This recommendation will also be dependent on projections of expected use of EV/Plug-In Hybrid vehicles among drivers at MSU.

Team Members (L-R): Michael Carlson, TJ Shelton, Reed Cortright, David Cohle, Tiegan Cunningham, Jessica Vedrody, Max Knoblock, Chris Capaldi

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MSU Sustainability: Innovation for Efficiency – Library Lighting System

As technology advances so does the use of library space. Presently, the facility is moving from housing numerous traditional books stacked on shelves to a digital library. This has opened a lot of space for new uses by the campus community.

These changes have resulted in an inefficient and inflexible lighting system. Our team analyzed the current state of the lighting system and developed a proposal for an updated system. The proposal includes a three- part plan for updating the library lighting.

Part one of the updated lighting proposal utilizes current infrastructure through the use of new technology, creatively improving the lighting system. This includes the utilization of light motion sensors in strategic areas, daylight harvesting where possible, ensuring proper lighting levels, and other energy saving strategies.

Part two focuses on updating the first floor of the east wing. This involves a full renovation of the current lighting system as well as the implementation of energy efficient lighting fixtures. This new lighting system will be more energy and cost efficient and be designed to be adaptable to future uses of the space as well as compatible with IOT technology.

Part three will adapt the process our team developed in updating the first floor of the east wing to the rest of the library. This process will provide a step-by-step map for future lighting renovations in other parts of the library lighting system.

Team Members (L-R): Back Row: Charlie Morgan, Patrick Donovan, Tim Els, Joe Backlas. Front Row: Detong Che, Zixuan (Eddie) Zhang, Paul Rankin, Hao Shen

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MSU IPF/Johnson Controls: Analysis of Current Operation of the Regional Chilled Water Plant

Michigan State University’s Infrastructure Planning and Facilities (IPF) is working with our team and Johnson Controls Inc. (JCI) to improve their Regional Chilled Water Plant on MSU’s campus. The Regional Chilled Water Plant (RCWP) provides cool air to almost 20 buildings around MSU’s campus. The plant has ten chillers that vary in age and usage. Within the next two years, two of the chillers are going to be replaced due to exceeding a 30-year life span. The objective of this project is to find the most efficient way to remove and replace these chillers, while taking into consideration the long-term effects of the replacement.

Our team will be using the Johnson Controls Planning Tool to test different options for the new chillers in the plant. The JCI Planning Tool is a simulation software system that allows companies, such as IPF, to see an overhead view of their current plant layout and test its yearly efficiencies and usage, as well as test new options. When a new change is made to the digital plant layout, future efficiencies and usage are predicted. The purpose of using the Planning Tool is to compare current efficiencies to new predictions, and determine which changes should be made for a given facility. Johnson Controls provides an Optimization Tool that monitors and controls the new plant changes once the Planning Tool simulation has been run and approved. The Optimization Tool will not be used in this project but will be a possible resource for IPF in the future.

For this project, there is no budget or limit to creativity. Our team has the opportunity to think outside the box and implement an effective change. A new idea for these chillers could benefit Michigan State University for decades to come and change the way MSU uses their resources.

Team Members: Back Row (L-R): Edmund Justin, Michael Branch, Willem Byl, Seneca Moore. Front Row (L-R): Erik Carrier, Gabrielle Logan, Macie Hornbacher, Emilie Hawks

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Bosch: Reducing Order Cycle Time

Bosch is an engineering and electronics company headquartered in Germany. Bosch supplies a multitude of parts, ranging from brakes to power tools, that are shipped to different companies in numerous industries. Our group was tasked with finding the inefficiencies in Bosch’s processes that would lead to the solution of reducing order cycle time from three days to two days. Our team was focused on one of Bosch’s tool distribution centers located in West Memphis, Arkansas. Reducing Bosch’s order cycle time would increase profitability and decrease lead time for customers. The approach our team took to accomplish this goal was the creation of a simulation in Arena. This simulation successfully captured the flow of different packages throughout the distribution center.

Our team designed this simulation to include the pick-to-pack procedure of Bosch’s tool shipping process. Our team was searching for any flaw in the current system that was slowing the process throughput time. Using the simulation, our team looked for different bottlenecks, inefficiencies, or inactive systems. Once the problems were identified, discussions were held to review the different opportunities for Bosch to assist with its main goal of reducing order cycle time and the best means of implementing these improvements.

Team Members (L-R): Marcus Featherly, Justin Bastedo, Clayton Rutkowski, Tripp Van Der Hyde, Harrison Malach, Sam Mioduszewski, Xiaoyifu (Leon) Chen, Stephen Lipshaw

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Roberts Sinto Corporation: Mapping Internal Processes

Roberts Sinto Corporation (RSC) is the US branch of Sintokogio, Ltd, headquartered in Japan. RSC builds a variety of machines ranging from automation equipment used in large assembly plants to molding machines, mold handling systems and blast equipment used in the foundry industry. All of their systems have constantly moving machine parts, whether they’re being transferred into a welding robot or connected to the other side of the car. Roberts Sinto is responsible for ensuring all parts fit accordingly.

The main goal of this project is to map the purchasing process at Roberts Sinto Corporation in Grand Ledge, Michigan. Process mapping will help the company discover any opportunities for improvement in their current process and create a future state process map of the purchasing process. The process maps will use swim lines to visually distinguish jobs that share responsibilities with purchasing and display different sub-processes that are directly or indirectly involved.

Surveys will be conducted with the shop floor managers to assess the improvements that need to be made in the purchasing department. Our team will ask questions that assess what needs to be changed to make this process more efficient. Process mapping and data from RedMap will be able to shorten cycle times on products for the buyers at Roberts Sinto Corporation. Roberts Sinto can also use process maps to reduce costs throughout their inventory, labor hours, and waste elimination through surveys and review of the warehouse shop floor.

The future state process map will display a more efficient method of the current purchasing process. Our team will create a more efficient map that displays the processes with the improvements that our team has made by working with the Roberts Sinto purchasing department and other departments related to purchasing. Areas of improvement will be documented and used in creating this future state process map.

Team Members: Back Row (L-R): Lawrence Upshur, Parag Kalothia, Caitlin Killeen, Sydney May. Front Row (L-R): Jane Harrelson, Katie White, Erin Schwiegershausen, Mengtao Wu.

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Ingersoll Rand: Supply Chain Optimization

This project, sponsored by Ingersoll Rand, is concerned with supply optimization. This is accomplished through the use of a milk-run model to consolidate shipments. Specifically, our team focused on inbound supplier routes for an Ingersoll Rand manufacturing plant located in Lynn Haven, FL. The milk-run route was determined through an analysis of the suppliers for the Lynn Haven plant and the corresponding volumes in each region. Further research was done to find a path that targeted multiple suppliers while also optimizing the overall supply chain for the milk-run region. Ingersoll Rand enlisted our team to develop a model and the supporting documentation for a fully developed milk-run that increased efficiency, reduced inbound freight and inventory costs, and upheld Ingersoll Rand’s commitment to being environmentally conscious through the reduction of carbon emissions. Ingersoll Rand requested a process that consolidated multiple separate supplier routes into a more efficient route that is not only consistent with the Lean Strategy Ingersoll Rand has in place but also reduces the quantity of empty trucks and Less Than Truckload deliveries to the manufacturing plant.

The process that was created is intended to be implemented immediately by Ingersoll Rand at the Lynn Haven Plant as a pilot program. Then, if the milk-run process model is a success, it will be applied across all locations and divisions of Ingersoll Rand and their suppliers. This will exponentially save costs given the many milk-run opportunities available, reducing freight, inventory, and carbon emissions with each new implementation.

Team Members (L-R): Nikki Onopa, Sikun Zhang, Yukan Pang, Nicholas Valentine, Christian Nault, Ely Masson, Meghan Metzker, Andrea Prebay

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Ingersoll Rand: Retail Distribution Market Network Optimization

Ingersoll Rand is a business-to-business company that manufactures and sells various products across four different product lines. Currently, their product lines include industrial tools, club cars, heating, and cooling. In addition, they also provide transportation refrigeration. Products are sold in over 100 countries through distributors and sales branches. The objective of this project is to optimize Ingersoll Rand’s Heating Ventilation and Cooling (HVAC) delivery network in the Atlanta, Georgia area.

Our team will be analyzing order shipments from three Ingersoll Rand warehouses and 6 parts stores to the final customer. Currently, there is overlap in service coverage resulting in multiple warehouses serving the same area. The overlap in service coverage creates an opportunity for reduction in operational and logistics costs through network optimization while maintaining unsurpassed delivery to customers.

There are four milestones to be completed throughout the project. The first milestone consists of identifying current and new services for shipping. For the second milestone, heat maps will be created to compare the relationship of current warehouse sites and sales. This will show the gaps and overlap areas that need to be worked on or consolidated. For the third milestone, our team will create optimized network models in which warehouses and parts stores do not overlap in services. In milestone four, all of the prospective models will be reviewed and compared to the current services. As of now, the project will focus on the Atlanta, Georgia area but, in the long run, our team hopes to establish a standard process for footprint optimization initiatives to implement across the United States.

Team Members: Back Row (L-R): Chris Rinaldi, Austin Latack, Drew Antonelli, Walt Thomas. Front Row (L-R): LaToya Smith, Milena Montalbano, Maureen Conway, Sweet Hou.

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Ingersoll Rand: E-procurement

Founded in 1905, Ingersoll Rand is a global manufacturing company headquartered in Ireland. It enhances operational efficiency, saves energy, and improves productivity through innovative equipment, products, and services. In collaboration with Rochelle Savel, Christian Solorzano, and Erin Nuccitelli, Our team will work to evaluate and help to implement the E-procurement process.

E-procurement is the business of purchasing supplies, work, and services through the internet. Ingersoll Rand has identified an opportunity to improve the procurement process through a third-party company called Coupa, a cloud-based software that works to make the purchasing process more efficient. Through the implementation of E-procurement and Coupa, Ingersoll Rand hopes to identify opportunities to improve the cycle time and total savings of indirect procurement.

Our team is assisting Ingersoll Rand in their effort to enhance the current E-procurement Catalog within the indirect-spend category. The project objective is to identify indirect-spend items to catalog based on market research and company data. In addition, our team will determine which items are sourceable or non-sourceable, identify whether current catalogued items are goods or services, and document which indirect commodities to catalog. Our team hopes to evaluate the change in cycle time to procure indirect items and evaluate the total cost savings related with E-procurement.

Team Members: Back Row (L-R): Eric Vanslembrouck, Kyle Newman, Elliot Zhao, Sanjay Kallumkal. Front Row (L-R): Eric McDonald Perry, Morgan Bujold, Zoey Yang

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Hess Corporation: Strategic Category Development

Hess Corporation is a leading global independent energy company engaged in the exploration and production of crude oil and natural gas. Founded in 1933 by Leon Hess, the company is currently headquartered in Houston, TX with operations around the globe. Hess has two industry leading positions in the U.S. in Bakken, North Dakota and Utica, Ohio.

Last year Hess Corporation had 4 rigs that drilled 80 wells in Bakken. This year, Hess has 115 planned wells. This increase in business has caused an increase for services provided to the drilling rigs. As the drilling rigs are running they produce solid waste from the well they are drilling. This waste is removed from the site and disposed of by contractors in accordance with EPA regulations.

To accommodate the increase, vendors in Bakken received a request for proposal. The request for proposal includes questions for the vendor to answer and pricing booklets, among other things. When the request for proposal is returned, four different criteria are used to score the vendors that choose to respond to the request for proposal: safety, quality, delivery, and cost.

Hess operates using lean techniques that they have adapted to the oil and gas industry. They are committed to sustainable business practices and social practices within the community. They are seeking a vendor that can operate in the same way and scale with Hess in Bakken.

Team Members: Back Row (L-R): James Hague, Matthew Fanko, Collin Gagnon, Drew Laske. Front Row (L-R): Saajan Shah, Armando Ruiz, Kristi Lauzon, Nathan Petrylak

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Intel: Inventory Management Strategy for xPV Testers

Intel is a supplier of processors for computer system manufacturers and other IT products. Intel’s business is expanding at this point and has been having issues with inventory management. This project focuses solely on one of its product testers, known as xPV, and on their manufacturing facility located in Malaysia. The xPV testers are a highly customized chip tester made according to both chip design and customer requirements. Due to its characteristics, which include uniqueness, unpredictable demand, long lead-time, sensitive components, and minimum quantity orders, Intel’s current inventory strategy is not equipped to handle the high variability in demand. The xPV testers are a high part count, high mix and low volume product, which ultimately results in a high level of excess inventory.

Overall, there are two main opportunities. First, our team has proposed a new inventory management strategy for the xPV testers. Although the xPV inventory strategy is very broad, it focuses heavily on the commonly faced problems that occur when managing xPV inventory. Next, our team has performed internal data analysis to reduce the level of excess inventory. Intel uses an SAP system, and it requested various internal data reports, including the bill of materials, customer purchase orders, point-of-sale transactions, and the shelf life of xPV testers.

Our team is focused on creating guidelines of how much excess inventory to keep in-house and find an effective inventory management strategy for the unique xPV tester product. Ultimately, by utilizing the internal data analysis, it is hoped that our team can help reduce xPV inventory by 50% as well as create a more time efficient decision-making and purchasing process.

Team Members: Back Row (L-R): Christopher Weyrich, Daniel Madura, Jack Heinle, Michelle Begovic,. Front Row (L-R): Morgan Schliem, Christina Garrisi, Jiaqi Wu, Siyu Diao

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Grant Thorton: Historical M&A Deal Analysis Study

Established in 1924 and headquartered in Chicago, IL, Grant Thornton has evolved into one of the world’s leading organizations of independent audit, tax and advisory firms. With revenues in excess of $16 billion and 59 operating offices, Grant Thornton is on a mission to help clients achieve their business and personal goals.

Grant Thornton deals largely with mergers and acquisitions, creating value across the M&A life cycle using their unique process that follows the Purple Curve. With this approach, GT avoids the pitfalls that many M&A deals face, while maximizing sustainable deal value.

It is essential to understand original deal drivers, or rather the compelling reasons why a company decided to make a deal, and what the results were, in order to understand any value gaps. After identifying value gaps, it is important to understand the root cause of them. Because each deal is different, numerous reasons arise.

All data analyzed comes from surveys completed by previous GT M&A clients. Using Excel and Tableau, our team analyzed how well deals within the last 5 years performed against Grant Thornton’s Purple Curve, in regards to both value realized and timing of value realization. Our team confirmed that the surveys typically did not reach the intended respondent (C-Suite or one level below), leading to less accurate and less descriptive results, and that the majority of value ‘missed’ or ‘lost’ was in areas relating to IT systems and culture integration.

The findings, compiled into a valuable White Sheet document, will help private equity, middle market, and Fortune 500 companies to better understand the common sources of value, timing of realization, and deal risk so they can better position their organizations to create more value in their own deals.

Team Members (L-R): Alex Loughman, Jodie Kozler, Savannah Krull, Andrea Tomczak, Abigail Wampler, Nicolette Birkett, Jasmine Kim, Sean MacFarland

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BASF: Market Research for High Insulation Material

Our team worked with BASF to analyze two new high-performance insulation materials (HPIM): Slentex® and Slentite®. Within BASF, the HPIM market belongs to the multi-billion-dollar Performance Materials business function, which in 2016 accumulated €6.888 billion in sales. The ultimate goal is to add to this bottom line by researching a variety of applications and markets for the two insulation materials that BASF has yet to enter.

Slentex® and Slentite® are both high-performance insulation materials that provide inimitable performance compared to the rest of the market. They provide significantly greater insulation, while using less material. Both products are aerogels, which are Nano porous solids in which gas has replaced the liquid component in traditional gel. Their differentiating features, however, provide a variety of unique applications for each product.

The first step towards identifying new applications and new markets for Slentex® and Slentite® was to establish a basic understanding of the HPIM market, after which our team developed a methodology for evaluating future applications. This methodology involved evaluating market size, market growth, weight of environmentally friendly pressures, and the degree to which Slentex® and Slentite® could provide a sustainable competitive advantage. In this way, BASF will be able to use this decision-making process to evaluate future uses of these two materials.

After developing a list of applications and markets for Slentex® and Slentite®, our team conducted additional research, including cold calling potential clients and meeting with current key account managers at BASF to further determine the market appeal of the future applications.

Through this process, several different markets and applications were identified and recommended to BASF as future business ventures to pursue.

Team Members: Back Row (L-R): Stephen Christy, Parker Godfrey, Ziaho Wang, Collin Seneski, Maxwell Bradley. Front Row (L-R): Xiaochang (Andy) Zheng, Kyle Frazier, Andy Stefani

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ArcelorMittal: “Beached” Iron Decision-Making Model

ArcelorMittal is the world’s leading steel and mining company producing approximately 133 million tons of crude steel annually. ArcelorMittal was ranked as 123 in the 2017 Fortune Global 500, a ranking of the world’s biggest corporations. With 199,000 employees across 60 countries, ArcelorMittal is leading the way in the steel business.

The first major process in creating steel is the creation of molten iron from various raw materials including scrap, iron ore and oxygen; this molten iron is later refined into steel. Since ArcelorMittal’s iron creation is a continuous process, there are instances when the amount of molten iron produced is too much for the steelmaking process to handle. The plant must “beach” some of the molten iron, by pouring it slowly over a bed of slag, in order to save it for future uses. The company may break it up and reuse it later in the steelmaking process or sell it to an outside company.

For this project our team has designed a model that operates on a monthly basis to determine the most cost-effective strategy for the plant’s excess beached iron. The model considers the different conditions for each month and determines the best decision based on various factors including the amount of scrap inventory on hand, the market price of beached iron, and transportation costs.

This model will have a major impact on ArcelorMittal by generating revenue from the sale of beached iron or reducing the costs of purchasing raw materials, depending upon monthly conditions. The project will also open up acres of storage space in their facilities currently being occupied by unassigned beached iron. Lastly, the project will save the company valuable time by giving them an immediate answer to a monthly decision.

Team Members: Back Row (L-R): Josh Chartier, Samuel Morris, Joe White, Patrick Kuiper. Front Row (L-R): Michael Hamilton, Saisha Johnson, Colin White, Shayna Evans.

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Asahi Kasei Plastics: Warehouse Expansion Project

Located in Fowlerville, Michigan, Asahi Kasei Plastics North America (APNA) currently faces a dilemma one could only hope for: their innovative plastics are proving to be in high demand. APNA is becoming increasingly aware that their current facility lacks the capacity for storing their high demand of finished goods. Lately, APNA has been housing finished products in third- party warehouses as the plastic resins await their end destination.

To keep up with demand, which is forecasted to increase, APNA has concluded that expansion is necessary to output future volumes and to maintain a competitive supply chain advantage. Their goal is to expand for future production, offset third party storage costs by adding additional warehouse space, and to do this all within a 5-year period.

The challenge presented to our team of is full of logistical conflicts. Material flow and productivity must be maintained to prove financially beneficial. The site to be built upon is landlocked in virtually all directions. Westward expansion involves moving extensive masses of machinery, resulting in large takedown costs paired with an unknown length of downtime. The logical response to add additional space towards the east is met with residential properties. The north is locked in by a heavily utilized railyard, while the southern portion is hindered by the presence of Interstate 96.

By analyzing capacity requirements, our team is set to deliver a warehouse expansion plan within budget, material flow, and facility location limitations. Tasked with providing a unique solution to an international supplier with local Michigan roots, our team is developing a space that all members involved will find both aesthetically and fiscally rewarding.

Team Members (L-R): Aaron Dunn, Arian Jaberi, Harrison Duong, Ian Burgler, Rachel O’Neill, Erich Grohs, Jake Seitz

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American Axle & Manufacturing, Inc.: Transportation Initiative

American Axle & Manufacturing is a global Tier-1 manufacturer of automobile driveline and drivetrain components and systems. Headquartered in Detroit, MI, AAM has over 25,000 associates operating at more than 90 facilities in 17 countries around the world, including France, The UK, Germany, India, and Japan. To support AAM’s customers on a global and regional platform, their focus is mainly drawn towards quality, operational excellence and technology leadership.

AAM currently uses a third-party logistics provider to transport their products. The goal of this project is to determine whether a leased transportation fleet would provide cost savings when compared to their current transportation network.

As a starting point, the project will only encompass dedicated lanes within Michigan. The project will have two deliverables, with the first being a cost analysis report related to leasing a fleet of trucks. The report will have detailed information pertaining to what kind of trucks will make up the fleet, as well as additional information regarding the operating costs of a leased fleet of trucks. The second major deliverable will be a map of the dedicated lanes servicing Michigan. These maps will show where improvements can be made within the lanes.

In order to successfully determine the feasibility of this project, both deliverables will be heavily considered in the final report. After creating a map of dedicated lanes with the best efficiency and considering costs of transportation and travel, AAM will be able to successfully determine the best logistical option

Team Members: Back Row (L-R): Jackson Rapaich, Austin Murray, Andrew Lunder, Eurick Crayton. Front Row (L-R): Justin Kim, Joe Szczupakiewicz, Mitchell Sokolsky, Daniel Johnson

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General Motors – Spring Hill Plant: Logistic Total Cost Dashboard

General Motors Company (GM) is a long time automotive manufacturing leader. The corporation started over 100 years ago and in 2016 sold over 10 million vehicles globally. Headquartered in Detroit, Michigan, GM has more than 400 facilities worldwide and has millions of parts shipped to its assembly plants.

General Motors operates a vehicle assembly plant in Spring Hill, TN where parts from multiple suppliers are delivered. These parts employ various modes of transportation to reach the plant. General Motors currently uses spreadsheets to monitor thousands of these shipments. At its volume, General Motors needs an interactive visual model to determine the most cost- efficient mode of transportation for the deliveries, and the effects of selecting each mode to aid in its decision-making of the overall logistics strategy.

This project provides General Motors with a visual representation of these costs to assist its logistics operations at the Spring Hill Plant. In order to solve the problem, our team used R, DAX, Python, and relational databases to integrate software and create an interactive dashboard that provides in- depth analysis of elements that are used to compute the Total Enterprise Cost (TEC).

The dashboard helps the user review and visualize the financial trade-offs of TEC across different functional areas such as logistics, handling, and manufacturing. It will aid GM’s Global Logistics team in understanding the trade-offs when selecting modes of transportation to ship both frequent and infrequent items to its Spring Hill plant in Tennessee.

Team Members: Back Row (L-R): Malik Hall, Samuel Cawood, Alex Knop, Wesley Ma. Front Row (L-R): Angelica Garrido, Michael Kemper, Natalie Hepburn, Mackenzie Vespa

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General Motors – Silao Plant: Total Enterprise Cost/Ship Frequency Dashboard

Our team was tasked with a project to lower the total enterprise cost for the General Motors Silao truck plant. When looking at total enterprise cost, multiple factors are included. This includes costs for transportation, holding, logistics, carrying, container, and numerous other costs. When working to lower the total enterprise cost, our team had to consider all these factors, but more importantly, we had to look at the various modes of transportation. All parts that arrive at the Silao truck plant come from suppliers in various locations. All of these parts can be transported via four ways: less than truckload (LTL), truckload (TL), intermodal (ITL), and milk-runs (MR). Choosing what mode to utilize is a timely and inconsistent process for General Motors.

Our team worked to create a tool that can evaluate all these options and create an output of the right form of transportation that General Motors should use at that point in time. This tool is a dashboard that is created via object-oriented programming. The dashboard will save General Motors employees a significant amount of time in their day-to-day operations. Before, GM employees had to look at multiple data sources and manually input different alternatives to find the best solution. After this tool was created, all the alternative options could be automatically evaluated via the program that our team created. This led to significant employee time savings, less room for error, and a consistent method that will not vary based on human error, leading to overall cost savings for the Silao truck plant’s transportation mode decision.

Team Members (L-R): hew Hamilton, Jake Herwaldt, Conor Estabrook, Jayson Armbruster, Yujie (Chris) Zhang, Collin Thompson, Steven Schaller, Weilin Du

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General Motors – Fairfax Plant: Total Enterprise Cost/Ship Frequency Dashboard

General Motors is one of the world’s foremost designers and manufacturers of cars and trucks sold in more than 125 countries. In order to deliver parts on time with cost in mind, General Motors must factor in many decision- making variables to obtain an optimal cost. GM operates a large, complex network to deliver parts to vehicle assembly, propulsion, and stamping plants across North America. One of the primary drivers of cost within this logistics network is the ship frequency between the supplier and assembly plant.

The global logistics department for General Motors had tasked our team with creating a Total Enterprise Cost/Ship Frequency Dashboard for its Fairfax plant. Using Microsoft BI, our team created this practical tool that can be used by members of the logistics department to evaluate different options for shipping network design. The user will be able to compare the cost incurred from different modes of shipping (Truck Load, Milk-Run, Intermodal, and Cross-docking) on different ship frequencies (once a week vs every other day, etc.). For example, if the members of the global logistics department wanted to determine if multiple two-supplier milk-runs are cheaper than dedicating a truckload to each supplier on a lower frequency, the dashboard would evaluate that information and help the user make an accurate and informed decision. The costs that the dashboard will evaluate will be fuel costs, container costs, floor space cost to the plant, and ordering cost if the reorder point were to change. This dashboard will not only reduce costs by allowing the user to optimize network design, but it will save time by generating all the calculations automatically.

Team Members (L-R): Matt May, Alec Levine, Matt Miller, Josh Murray, Brian L’Heureux, Nicholas Kilpatrick, Zachary Lance, Amber Jabboori

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