Design of Experiments
The advantage of my “knocks-blocks” training method is that your engineers will learn to deal with the real-life issues involved in planning and executing experiments.
DOE course participants learn to reduce the variation in the knocks blocks machine through a series of experiments of increasing complexity and resolution.
Design of experiments will help you unlock sources of variation and find the best combination of variables. It applies to business and process problems where there are many interacting contributing factors.
- Three-day course
- Taught at your facilities
- Option to combine it with projects
- Option for follow-up consulting to implement projects
- Focus is application of techniques from knocks blocks to real life situations
- Use statistical software (such as MinitabTM) to design and analyze experiments
“Great use of practical examples to explain theory.” Nader Bayani, MDS Sciex
“Strongly recommended for designers.” Eric Wu, MDS Sciex
“Phil has good interface with each of the students and challenges them to think through case problems, unlike a consultant going through a canned presentation.” Troy Washburn, TRW Automotive
“Very informative not only for engineering design but any other type of design, whether industrial or personal.” Salman Younas, Sims Recycling Solutions.
Use of Designed Experiments
- What is a factor?
- Experimenting is a learning process
Replication and Randomization
Designing full factorial experiments
- Using Minitab for designing full factorial experiments
Designing fractional factorial experiments
- Why use fractional factorial experiments?
- Three factors in eight runs 23
- Three factors in four runs 23-1
- Four factors in eight runs 24-1
- Five factors in eight runs 25-2
- The design resolution
- Designing a fractional factorial experiment with Minitab
Blocking: dealing with nuisance variables
- Adding blocks to a fractional factorial experiment with Minitab
- Designing a blocked fractional factorial experiment with the knocks blocks machine
Covariates in designed experiments
- Regression in process improvement
- Analysis of covariance
Contact me for a quote or to discuss your needs.