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062: Using Multidisciplinary Design Optimization to Solve Problems

Posted By Digital Enterprise Society, Wednesday, July 22, 2020
Updated: Monday, July 20, 2020

062: Using Multidisciplinary Design Optimization to Solve Problems

 

In today’s episode, Thom and Craig welcome Kevin Brittain, the Multidisciplinary Optimization Group Leader at Cummins, Inc. Kevin coaches a team that is focused on bringing cross-functional models together to solve problems across the product life cycle.  He defines MDO, explains why it is so powerful, and highlights the importance of putting people before processes or tools.  He offers suggestions to help overcome adoption resistance and highlights the overarching benefits of rejecting silo work and embracing cross-functional decision making. As an industry leader who is clearly passionate about his work, Kevin’s career advice is advice that will help anyone excel in their work and in their relationships. 

On today’s podcast, you will learn:

Understanding multidisciplinary design optimization

  • Kevin defines MDO as the enabling of cross-functional decision making through the integration and optimization of diverse simulation models across the product lifecycle.

  • How can you bring functions together rather than having parallel functional model silos working independently on a broader problem? 

  • What is the key to linking information across models for greater information sharing?

  • Bringing this information together allows for greater cross-functional decision making. 

  • Enable decision making and highlight the trade-offs that come as a result.

Why is MDO so powerful?

  • MDO is more affordable than ever, making it widely available for greater use.
  • What are the benefits of honing in on the optimal trade space that exists between the decision makers and the constraints?   
  • MDO allows for utilizing the models that would be used to help make decisions in the product development cycle to answer the problem of constraints across several groups.   
  • MDO effectively addresses the manufacturing cost factor of optimization. 

People, then processes and tools

  • Making sustainable change requires a broader vision that focuses on people.
  • How are roles defined in order to enable a bigger vision? How is change enabled through process?
  • Changing the workflow is the critical step to effecting lasting change for any problem.
  • Democratization refers to bringing the capabilities of powerful simulation tools into the hands of non-experts.
  • Models have tremendous knowledge of systems and can be used throughout the whole product lifecycle to help make better decisions.
  • Resistance to change needs to be addressed and the people who hold the knowledge need to be valued.
  • People who help make changes feel ownership of the new culture and will embrace it even faster. 
  • There doesn’t need to be any competition — effective change happens by taking what was already great and improving upon it.

Career advice from an engineer

  • If you don’t understand something, start by Googling it.
  • Building relationships is the key to a successful career — engineering is a social exercise.
  • Find the work that you are passionate about and then enjoy working hard at it. 
  • Maintain high standards for the company that you work for and for the people you work with.

 

Do you have an example of extraordinary efforts or innovation during these unprecedented times? We would love to hear your story and possibly interview you for an upcoming episode. Please reach out to us at www.DigitalEnterpriseSociety.org

 
Guest Bio

Kevin Brittain is the Multidisciplinary Optimization Group Leader at Cummins, Inc. 

Kevin worked for Cummins in structural analysis for 6 years. He received his Master’s degree in Mechanical Engineering at the University of Illinois Urbana-Champaign in 2011 with an emphasis on structural optimization. Currently he is leading a global MDO team within the Power Systems Business Unit and also leading cross functional initiatives across the company to proliferate the use of optimization within analysis-led design.


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