Business Analytics Capstone Experience
The Business Analytics Capstone Experience allows students to work on real-world business problems using data and statistics.
Graduate students work with a UM-Dearborn faculty advisor and a mentor from a sponsoring company to model, analyze and recommend a solution for a significant business problem.
Sponsoring a Capstone Project
The curriculum for the Master of Science-Business Analytics in the College of Business prepares students to deal with the challenging task of deriving deep insights from the vast array of structured and unstructured data in a variety of business areas. The program prepares students to pursue careers such as a data scientist or analyst.
The capstone project is the core of the MS-Business Analytics curriculum. This 15-week course emphasizes a team-based learning experience in which students conduct real-world analytics projects using data provided by the sponsoring organization. Since 2016, students have completed 15 projects with six sponsoring organizations. Some of the industry sectors represented by our sponsors include manufacturing, healthcare, energy, retail and service. Student projects have included optimal supply-chain logistics configuration, health-care services profitability analysis, predictive modeling for parts classification, analysis of customer satisfaction in a call center, forecasting weather-related power outages, and data-modeling for smart mobility.
Sponsoring a project provides MS-Business Analytics students an opportunity to gain experience applying their coursework to real-world business problems.
It also offers the sponsoring organizations the opportunity to work with our students. Many organizations make formal offers of employment to project participants after their work in the capstone project.
Only select students participate in the Capstone course. These students are selected on the basis of:
- Superior academic performance in the graduate program.
- Successful completion core courses in statistical modeling and analysis, optimization, forecasting, and data mining.
- Demonstrated teamwork and problem-solving skills.
- A formal written project report.
- A software demonstration (if developed for a particular application)
- A public presentation summarizing the project results.
There is no cost to sponsor a project.
- A project lead who will interface with the student team and Faculty Coordinator on all project matters.
- 2 to 3 hours each week for meetings with the student team, either in person or online.
- A brief, well-defined problem statement. The solution to the problem must require quantitative analysis, such as statistical analysis, optimization, predictive modeling, data mining, visual analytics, and exploratory analysis.
- A prepared data set that requires data modeling, such as data cleansing, data preparation, data normalization, data merging, or data parsing. Students must be able to analyze the data using natural languages, such as R or Python.
Project teams sign a non-disclosure agreement to protect the privacy of the data and other information shared by the sponsor. Sponsors may anonymize business-sensitive information and other technical details.
At the start of the semester, the sponsor and students meet to discuss the following:
- The organization’s business problem.
- The specific goal of the data analytics project.
- A description of the available data.
- Project-meeting schedules between the sponsoring organization and the student team.
The goal of the project is for students to:
- Understand and clearly define the business decision problem.
- Process and analyze data.
- Design, model and develop a solution.
During the project, student teams maintain regular communication with the project sponsors through scheduled site visits, web meetings and weekly progress updates. The Faculty Coordinator tracks project progress with technical reviews and updates with the student teams throughout the semester. In addition, the Faculty Coordinator and project sponsors jointly conduct mid-term progress reviews and provide feedback to the student teams.
The University provides student teams with access to an extensive portal of prominent software in a cloud-based environment, which students will use on their projects.
At the conclusion of the project, the student teams will make a public presentation of their findings to the sponsor, College of Business faculty, staff and students.
The Capstone project course is offered two times a year, during Fall and Winter semesters. We welcome your inquiries about sponsoring a Capstone project. We are currently in the planning stages for projects for Fall 2020 (9/1/20 to 12/06/20) and Winter 2021 (1/6/21 to 4/28/21) semesters. The tentative schedule is as follows:
- Problem identification and project planning:
- Now through August 15, 2020 for Fall 2020 semester.
- Now through November 15, 2020 for Winter 2021 semester.
- Project kick-off meeting:
- Wednesday, September 1, 2020 for Fall 2020 semester.
- Wednesday, January 6, 2021 for Winter 2021 semester.
- Final presentation to sponsor:
- Wednesday, December 6, 2020 for Fall 2020 semester.
- Wednesday, April 28, 2021 for Winter 2021 semester
Capstone Projects for Winter 2020
With the rise of messaging apps and social media technology being used as a main form of communication, U-M's Office of Budget and Planning wants to be able to quickly and conveniently analyze student survey data over the last 5 years. Gathering useful information about student needs and expectations is vital to help the University decide which technologies they should adopt to communicate with students to maintain a better student-university relationship. Chatbot and Voice Assistant are primarily being used to support staff by gathering the information they need from the student survey data. This project will task students to utilize Google assistant to answer questions and Dialog-flow to create a seamless interaction between users and system.
U-M’s Information Technology Services has collected a lot of data over the years through various activities across the Ann Arbor campus. The Office of Budget and Planning wants its staff to have the ability to run reports and gain insights from the UMay survey data. Students will be tasked to design a model by implementing and creating Augmented Analytics that will connect the end-user to this large set of raw data. This will include the ability to process verbal queries and respond with visual analytics, which will make it easier for the end-user to understand the results.
“Students had a short period of time to work on large scale issues, but they were able to find opportunities within the data set, and indeed, Ford will be implementing solutions in this space.” - Alan Jacobson, Ford Motor Company
Process for Generating an Origin-Destination Demand Matrix for Use in a Commuter Ride-sharing Simulation at Ford Smart Mobility
Ford Motor Company has expanded into mobility services with the purchase of a startup company, Chariot, which offers a carpooling service to commuters in select urban areas and uniquely allows potential customers to vote on new commuter routes. To support Ford and Chariot in identifying new commuter routes in cities where the company is not operating, this research effort created a process that simulates commuter origin and destinations, the number of commuters, and time of departure. The process selected San Francisco, CA as a starting point in part for verification purposes because Chariot already has operations in the city. Results indicate the process properly simulated commuter flow. Specifically, commuter’s origin locations were highly dispersed across San Francisco, but the destination locations indicate commuters are traveling towards areas of high employment. The process also accurately simulated departure times associated with the rush hour commute. In summary, the process created can be replicated to model commuter traffic patterns in other cities and identify where large of commuters are traveling from and to and during what time of day. Thus, the process provides Ford and Chariot commuter data to help guide early business decisions. An end-to-end analytics framework was utilized to process publicly available data from a very large number of sources by utilizing advanced data mining, visual analytics and simulation techniques to generate traffic flow inputs for advanced transportation network and simulation models.
Modeling for Predicting Power Outages due to Weather Events for DTE Energy
DTE Energy, the electrical service provider for Southeast Michigan, allocates a great deal of resources toward managing outages, particularly those caused by big weather events. This research project aimed to develop a series of models which are geographically sensitive (at the service center level) to predict number of power outages per day based on weather data. Using an ensemble of neural networks, a model for each service center was created that is sensitive to three different scenarios. These models, specifically those belonging to the four service centers which experience the greatest number of electrical service interruptions, predict number of outages in that service center to an industry benchmark level of accuracy. Further, one scenario—high temperature and high wind speed days—predicts outages substantially beyond industry benchmarks. Finally, we discuss our attempt at a more time sensitive model, predicting outages hourly. An end-to-end data analytics framework was utilized to process large disparate datasets by utilizing advanced data mining techniques to cleanse, merge and prepare data for modeling utilizing predictive modeling, and classification & forecasting algorithms. This work has enabled DTE Energy to better plan their labor and other resources in restoring power in a more efficient manner.
An Analytics Approach to Managing Inbound Call Support at Stefanini IT Solutions
Harnessing the power of data and analytics is the first step in driving operational efficiencies for the business of the future. Stefanini is looking to gain data driven insights into two of their existing call-center processes – inbound call handling process and the existing service ticket creation process. Stefanini currently maintains and captures data related to these two processes separately. The purpose of this project is to combine the call related data with the corresponding tickets and assist Stefanini to gain a better understanding of on what impact the issue types have on the duration of the calls. The analysis will also help Stefanini identify key areas to drive automation, deploy additional employee trainings and optimize the existing staffing models that can eventually generate operational efficiencies for the company. An end-to-end analytics framework was designed by effectively combining the two disparate data sets by utilizing exploratory data analytics, visualization and advanced analytics techniques to generate key insights to help drive efficiencies and automation for Stefanini.