The following dates are recommended because they have a low minimum student requirement,
or students are enrolled but not yet enough to hold a class. Please choose a date for your DOE Training class. Dates are formatted as year-month-day:
If none of the recommended dates work with your schedule, that's ok. Please choose a date from the list below:
Who is DOE Training for?
Design of Experiments can be used by anyone wanting to improve a service, process, or product. If the situation can be represented or modeled as a system with an output driven by inputs all of which can be measured, and inputs can be set at different levels, then DOE training is for you. In our course, Nogales students learn:
DOE Training Course Overview:
DOE training explains Design of Experiments - the strongest root cause analysis technique ranking a problem's causes & fastest way to optimize. We regard Design of Experiments (DOE) as the most powerful root cause analysis tool in the world. Our Masters have used DOE to produce 6-digit per year savings many times across a broad range of industries and applications. It's definitely worth adding to your arsenal. DOE can optimize product design when seeking to improve some performance criteria influenced by component dimensions or general features. By setting these features to varying, measurable levels, their relative influence on the response and preferable settings can be determined. Similarly, DOE can optimize process design when seeing to improve a process output influenced by process inputs. Even soft inputs like shift A,B,C can be used. Defect rate is commonly the process output, but many others such as operating cost can be used. From a service perspective, employee satisfaction would be an HR example. To domenstrate this, our class uses a fun case study providing a real, physical system allowing students to actually experience how DOE works. Advanced topics such as fractional factorial arrays, multi-level factors, Taguchi arrays, etc. are covered. Statistical software such as Minitab or SigmaXL is also used.