Stay relevant and competitive with modern quality engineering
Avoiding Common Pitfalls in the Early Stages of Improving Quality
“[If we] move from natural instincts of rushing the problem specification phase and emphasizing the solution phase to try to achieve a more balanced view of things, if we fight our action bias, I think we can move the needle to a more balanced allocation of time and effort for problem specification and solution finding,” says Christine Anderson-Cook of Los Alamos National Laboratory. In this talk, she explains why it is essential to spend time identifying the right problem before beginning a quality engineering project.
Watch to learn more about:
- Asking the right question, not the convenient question.
- Defining the right metric(s) to capture what is most important.
- Tradeoffs and costs associated with quality projects.
- Why the quality of data is situation-dependent: Do you have the right data to answer your question?
Los Alamos National Lab, Siemens Healthineers and Wolfspeed embrace automation and the rise of the digital economy
Organizations are embracing more automation and the rise of the digital economy in the production and delivery of products. And with in-line sensors afforded by the rise of the Industrial Internet of Things (IIoT) and advanced measurement systems, they are generating and collecting a growing volume of data that is more complex than ever before.
But they may not know what to do with the data. Moreover, the data may not be useful for driving process knowledge. In this discussion, innovative organizations share how they overcome these challenges.
You’ll hear from:
- Christine Anderson-Cook, Los Alamos National Laboratory
- Greg Mattiussi, Siemens Healthineers
- Ed Hutchins, Wolfspeed, A Cree Company