Cat Digital, the digital arm of Caterpillar Inc., uses connected fleet information and emission reduction strategies to determine the most effective fleet management policies. Students on the Cat Digital team will develop an analytics model that helps customers identify the ideal path to lessen the environmental impact of their fleet over time.
Abstract:
Caterpillar receives data from about 1.4 million connected assets and applies advanced analytics to this data to model how customers utilize their assets. For any given equipment, dimensions available are how many hours per day the engine is on, the average fuel consumption, and how much time it is idling versus running, among many other metrics.
Caterpillar also has data on a variety of options for replacement, rebuild, and retrofit for each fleet asset, and their respective improvement in terms of efficiency, fuel consumption, and other variables. Combining the two data sets helps Caterpillar develop a tool for customers wishing to reduce their environmental impact on what alternative is the best path forward.
The student team will develop an analytics model that helps customers identify the ideal path to lessen the environmental impact of their fleet over time, taking into consideration overall cost, multiple alternatives, and their impact on equipment downtime. The team will predict the ideal timeline for customers to replace, rebuild, or retrofit their fleet given a set of constraints such as location, cost, amount of downtime, among other variables.
The result is a model that can provide the decision-making to customers based on different factors to improve their overall emissions and sustainability metrics.
Impact:
This project will help Caterpillar and customers to prepare for a future with several options to improve overall sustainability, by identifying the most efficient path forward.
User Interface Design (1 Student)
Specific Skills: UI/UX Design, usability studies, graphic design experience/interest desirable
Should have general coding/web skills
Likely Majors: SI (grad), ARTDES, CS
Tool Development, Database Design (2-3 Students)
Specific Skills: Good software engineering practice, database design, full stack design.
Must have completed EECS 281(or equivalent) prior to W23.
Likely Majors: CS, Any with completion of EECS 281
Forecasting and Optimization (2-3 Students)
Specific Skills: Excellent quantitative forecasting skills. System optimization. Practical experience with sensitivity analysis problems.
Likely Majors: IOE, STATS, ECON
Business Product Development (1 Student)
Specific Skills: Customer needs analysis, business metrics, business process development, focus group testing
(These students must also have basic coding skills and/or excellent quantitative modeling skills)
Likely Majors: BBA with CS Minor, CS with BBA minor, IOE
Sponsor Mentor
Lucas I Sardenberg
Analytics Manager
Lucas leads a team of high-skilled and good-looking data scientists designing and developing advanced analytics for all data that Caterpillar’s 1.2 million connected assets generate. Lucas works to ensure all data is used to provide valuable insights to Caterpillar, its dealers, and ultimately the customers. Prior to working in Analytics, Lucas held a variety of different positions at Caterpillar, including machine marketing, merchandise operations, pricing, new product introduction, commercial support, dealer and customer management, and product management. Born and raised in Brazil, Lucas holds a bachelor’s degree in Mechanic Engineering, and Master’s degree in Business Administration.
Executive Mentor
Dan Reaume
Executive Sponsor Mentor
Dan Reaume is the Chief Analytics Director at Caterpillar. In this role, Dan leads a world-class organization of data scientists, mathematicians, developers, and statisticians designing and developing advanced predictive analytics and optimization algorithms to drive greater competitive advantage value for Caterpillar, its dealers, and its customers. Dan provides strategic and tactical leadership to set technical priorities, improve modeling practices, maximize value generation, and analytics organization as Vice-President of Operations Research; led pricing and customer experience analytics efforts for Dow Chemical; was the founding director of Advanced Analytics for Dow Corning; and served as technical fellow and leader of the Senior Leadership Technical Council for General Motors. Dan holds a bachelor’s degree in Mathematics and Computer Science from the University of Windsor, Master’s degree in Management of Technology from the University of Waterloo and a PHD/ MS from the University of Michigan in Industrial and Operations Engineering, Intelligent Transportation Systems.
Faculty Mentor
Kerby Shedden
Kerby Shedden is Professor of Statistics in the College of Literature, Science, and the Arts (LSA) and holds a courtesy appointment as Professor of Biostatistics in the School of Public Health. He received a Ph.D. in Statistics from UCLA in 1999. His research focuses on developing and evaluating methods for analyzing high dimensional and complex data including mediation analysis and dimension reduction regression, as well as developing statistical software for applied statistics. He has served as the director of the Consulting for Statistics, Computing, and Analytics Research unit since 2011, and has extensive collaborations with researchers in human biology, nephrology, sleep and aging, and cancer research.
Citizenship Requirements:
- This project is open to all students on campus. Students applying will be subject to Caterpillar compliance screening in order to participate on the team.
- International students on an F-1 visa will be required to declare part-time CPT during Winter 2023 and Fall 2023 terms.
IP/NDA: Students will sign standard University of Michigan IP/NDA documents.
Internship/Summer Opportunity: Students will be guaranteed an interview for a 2023 internship. The interviews will take place in January/February 2023.