Daimler Trucks Driveline Innovation

Large gear and small gear with team name inside the larger gear

The team will work closely with Daimler representatives to:  

  • Remove the hurdle of updating database manually 

  • Set up the Tool logic to be independent for every shaft 

  • Set up thorough Error Checks 

  • Automate manual steps of the Tool whilst still allowing manual operations as a contingency 

  • Allow pre-set values to be overwritten for prototyping purposes 

  • Analyze Tool data to understand the common CWO trends  

How will the project tasks be sub-divided?  

Product Development Teams: The priority is to develop an update to the current tool to cut down reliance on manual operations. A higher-level goal, after the development of the basic tool, is to incorporate aspects of Machine Learning and AI to automate as many aspects of the design process as possible. 

  1. Product Owner 

  1. SCRUM Master 

  1. Developers 

Data Analysis/Mechanical Engineering: The secondary goal of the project is to analyze the custom work order trends to identify common causes of manual operations on ordered trucks.


    • Students will use the SCRUM methodology to develop and update the Driveline Custom Work Order (CWO).
    • Students will learn to work in an industry setting with Daimler representatives to develop user stories, prioritize them and conduct regular sprints to introduce features in the tool.
    • Students will also learn the use of GitHub and documentation standards.
    • In addition, students will learn to use ZOS APIs to interact with the mainframe and extract data.
    • Students will learn to test and debug the application. Alongside, evolving their programming skills, students will also learn how to set up databases and use the IBM DB2 Tables.
    • Students will be hone skills in UI/UX by developing a user-friendly interaction tool.
    • Students will also learn the analysis of commercial vehicle drivetrain performance, packaging and limitations.
    • Students will also learn data analysis on large quantities of build data to identify potential improvements and innovations. This will innovate/ improve the efficiency and prototyping ability of DTNA’s driveline design/integration team.
    • Students will also learn leadership and communication skills pertinent to professional collaboration.

    Methods and Technologies

    • Production level coding
    • Databases
    • Product Development Lifecycle
    • IBM ZOS Connect
    • Data Analysis
    • Machine Learning
    • Business Management
    • SCRUM Methodology
    • Mechanical Engineering


    • Computer Science
    • Computer Engineering
    • Mechanical Engineering
    • Data Science
    • Mathematics
    • Physics
    • Business and Technology Management
    • Business Administration


    • Daimler Trucks North America LLC

    Daimler Points of Contact

    James Vue

    Robert P Martin

    Matt Walling

    Shayan Khan

    NYU Faculty Advisor

    Sven Haverkamp